JP2021007665A - Optical coherence tomography (oct) data processing method, oct device, control method thereof, oct data processing device, control method thereof, program, and recording medium - Google Patents

Abstract

To improve efficiency of OCT data processing.SOLUTION: An exemplary aspect is a method for processing data collected by applying OCT scan to a sample, which prepares a three-dimensional data set collected from the sample, creates a two-dimensional map on the basis of respective representative intensity values of a plurality of pieces of scan data A included in the three-dimensional data set, executes positioning of the three-dimensional data set based on the two-dimensional map, and executes processing based on at least the partial data set of the three-dimensional data set for which positioning is executed.SELECTED DRAWING: Figure 7

Description

The present invention relates to an optical coherence tomography (OCT) data processing method, an OCT device, a control method thereof, an OCT data processing device, a control method thereof, a program, and a recording medium.

OCT is a technique capable of imaging a light scattering medium with a resolution of a micrometer level or less, and is used for medical imaging, non-destructive inspection, and the like. OCT is a technique based on low coherence interferometry, which typically utilizes near-infrared light to ensure the depth of light scattering media into a sample.

Patent Document 1 states that in order to efficiently collect OCT data and to collect OCT data from a specific region of a sample accurately and in a short time, the optical depth is measured by backscattering or backreflection. A method of processing the OCT data set obtained as a function of the above, which analyzes the OCT data set to identify at least the first subset of landmark area data and positions the OCT data set based on this landmark area data. A method of processing at least a second subset of the OCT data set based on the correspondence between the OCT data set and the landmark area data is disclosed.

Further, in Patent Document 2, in order to monitor the progression of a disease, an OCT survey data set is acquired as a function of optical depth by measuring backward scattering or backward reflection, and this survey scan data set is analyzed. Of a survey scan dataset that represents at least a portion of the affected tissue region associated with the landmark region by identifying the landmark region and assigning a location in the sample or a location associated with a fixed location to an element of the survey scan dataset. Methods are disclosed for registering parts and monitoring changes in the area of affected tissue at different time points.

U.S. Pat. No. 7,884,945 U.S. Pat. No. 8,405,834

An object of the present invention is to improve the efficiency of OCT data processing.

Some exemplary embodiments are methods of applying an optical coherence stromography (OCT) scan to a sample to process the collected data (OCT data processing method), a three-dimensional dataset collected from the sample. Is prepared, a two-dimensional map is created based on the representative intensity values of each of the plurality of A scan data included in the three-dimensional data set, and the three-dimensional data set is positioned based on the two-dimensional map. , Perform processing based on at least a partial dataset of the positioned three-dimensional dataset.

The OCT data processing method of some exemplary embodiments can be combined with any of the following embodiments: the process comprises a predetermined analysis process; the process is obtained by the analysis process. Including a predetermined evaluation process based on the data; the process includes setting a partial data set to which the analysis process is applied; the process includes setting an application area of a predetermined test on the sample; the process. Includes the setting of a partial data set to which a predetermined imaging process is applied; prepares test data obtained from the sample by a predetermined test different from the OCT, which process is the test data and the three-dimensional data. Includes a predetermined comparison process with at least a portion of the set.

Some exemplary embodiments are methods of applying an optical coherence stromography (OCT) scan to a sample to process the collected data (OCT data processing method), the first three of which are collected from a sample. A dimensional data set and a second three-dimensional data set are prepared, and a first two-dimensional map is created based on the representative intensity values of each of the plurality of A scan data included in the first three-dimensional data set. A second two-dimensional map is created based on the representative intensity value of each of the plurality of A scan data included in the second three-dimensional data set, and the first two-dimensional map and the second two-dimensional map are created. Based on the above, processing based on at least one of at least one of the first partial data set of the first three-dimensional data set and at least the second partial data set of the second three-dimensional data set is executed.

The OCT data processing method of some exemplary embodiments can be combined with any of the following embodiments: The processing comprises the first two-dimensional map and the second two-dimensional map. The process includes registration between the at least the first partial data set and the at least the second partial data set via registration between; the process comprises the first two-dimensional map and the second. Includes adjustment of the application area of the OCT scan to the sample via registration with the 2D map; the adjustment is made sequentially by sequentially processing the 3D dataset that is sequentially collected from the sample. The registration between the first two-dimensional map and the second two-dimensional map includes an image correlation operation; the image correlation operation includes the first two-dimensional map and the second two-dimensional map. The amount of displacement between the two-dimensional map is determined, and based on the amount of displacement, the registration between the first two-dimensional map and the second two-dimensional map is performed; the amount of displacement is parallel. Includes at least one of the amount of movement and the amount of rotational movement; the first three-dimensional data set and the second three-dimensional data set are collected from different three-dimensional regions of the sample, and the processing is performed by the first. Generated from the first image data generated from at least the first partial data set and at least the second partial data set via registration between the two-dimensional map and the second two-dimensional map. Includes synthesis with the resulting second image data; the process includes a predetermined analysis process; the process includes a predetermined evaluation process based on the data obtained by the analysis process; the process includes the process. Includes the setting of partial datasets to which the analysis process is applied; prepare multiple 3D datasets, each corresponding to a plurality of different time points, including said first 3D dataset and said 2D 3D dataset. However, the analysis process includes a process of obtaining a time-series change of a predetermined parameter value.

Some exemplary embodiments include an OCT scanner that applies an optical coherence stromography (OCT) scan to a sample to collect a 3D dataset, and a plurality of A scan data contained in the 3D dataset, respectively. A map creation unit that creates a two-dimensional map based on a representative intensity value, a positioning unit that positions the three-dimensional data set based on the two-dimensional map, and at least one of the three-dimensional data sets that have been positioned. It is an OCT apparatus including a processing execution unit that executes processing based on a partial data set.

Some exemplary embodiments include an OCT scanner that applies an optical coherence stromography (OCT) scan to a sample to collect a first 3D data set and a second 3D data set, and the first 3 A first two-dimensional map is created based on the representative intensity values of the plurality of A scan data included in the dimensional data set, and each of the plurality of A scan data included in the second three dimensional data set is created. A map creation unit that creates a second two-dimensional map based on the representative intensity value of the above, and the first three-dimensional data set based on the first two-dimensional map and the second two-dimensional map. It is an OCT apparatus including a processing execution unit that executes processing based on at least one of at least one of the first partial data set and at least one of the second partial data sets of the second three-dimensional data set.

Some exemplary embodiments are methods of controlling an OCT apparatus that includes an OCT scanner and processor that applies an optical coherence stromography (OCT) scan to a sample, such as collecting a three-dimensional data set from the sample. The OCT scanner is controlled to control the processor to create a two-dimensional map based on the representative intensity value of each of the plurality of A scan data contained in the three-dimensional data set, and based on the two-dimensional map. , The processor is controlled to position the three-dimensional data set, and the processor is controlled to perform processing based on at least a partial data set of the positioned three-dimensional data set.

Some exemplary embodiments are methods of controlling an OCT apparatus that includes an OCT scanner and processor that applies an optical coherence stromography (OCT) scan to a sample, from the sample to a first three-dimensional dataset and a first. The OCT scanner is controlled to collect 2 3D data sets, and a 1st 2D map is created based on the representative intensity values of each of the plurality of A scan data included in the 1st 3D data set. The processor is controlled so as to create a second two-dimensional map based on the representative intensity values of the plurality of A scan data included in the second three-dimensional data set, and the first one is created. At least the first partial data set of the first three-dimensional data set and at least the second partial data set of the second three-dimensional data set based on the two-dimensional map and the second two-dimensional map. The processor is controlled to perform processing based on at least one of the above.

Some exemplary embodiments include a reception unit that receives a 3D data set collected by applying an optical coherence stromography (OCT) scan to a sample, and a plurality of A scan data contained in the 3D data set. A map creation unit that creates a two-dimensional map based on each representative intensity value, a positioning unit that positions the three-dimensional data set based on the two-dimensional map, and the three-dimensional data set that has been positioned. It is an OCT data processing apparatus including a processing execution unit that executes processing based on at least a partial data set of the above.

Some exemplary embodiments include a reception unit that accepts a first 3D and a second 3D data set collected by applying an optical coherence stromography (OCT) scan to a sample, and said first. A first two-dimensional map is created based on the representative intensity values of the plurality of A scan data included in the three-dimensional data set of the above, and the plurality of A scan data included in the second three-dimensional data set. The map creation unit that creates a second two-dimensional map based on each representative intensity value of the above, and the first three-dimensional data based on the first two-dimensional map and the second two-dimensional map. An OCT data processing apparatus including a processing execution unit that executes processing based on at least one of at least one of the first partial data set of the set and at least the second partial data set of the second three-dimensional data set.

Some exemplary embodiments are methods of controlling an optical coherence stromography (OCT) data processor, including a processor, said to apply an OCT scan to a sample to accept a collected three-dimensional dataset. The processor is controlled to create a two-dimensional map based on the representative intensity value of each of the plurality of A scan data included in the three-dimensional data set, and the processor is controlled based on the two-dimensional map. The processor is controlled to position the 3D data set, and the processor is controlled to perform processing based on at least a partial data set of the positioned 3D data set.

Some exemplary embodiments are methods of controlling an optical coherence stromography (OCT) data processing apparatus that includes a processor, a first method collected by applying an optical coherence stromography (OCT) scan to a sample. The processor is controlled to accept the 3D data set and the 2D 3D data set, and the first is based on the representative intensity value of each of the plurality of A scan data contained in the first 3D data set. The processor is controlled to create a two-dimensional map and to create a second two-dimensional map based on the representative intensity value of each of the plurality of A scan data included in the second three-dimensional data set. , At least the first partial data set of the first 3D data set and at least the second of the second 3D data set, based on the first 2D map and the 2nd 2D map. The processor is controlled to perform processing based on at least one of the sub-datasets of.

Some exemplary embodiments are programs that cause a computer to perform the methods of any of the embodiments.

Some exemplary embodiments are computer-readable non-temporary recording media on which the program of any embodiment is recorded.

According to an exemplary embodiment, it is possible to improve the efficiency of OCT data processing.

It is the schematic which shows the structure of the ophthalmic apparatus (OCT apparatus) which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is a flowchart which shows the operation of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus (OCT apparatus) which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is the schematic which shows the structure of the ophthalmic apparatus which concerns on an exemplary aspect. It is a flowchart which shows the operation of the ophthalmic apparatus which concerns on an exemplary aspect.

Some exemplary embodiments of the embodiments will be described below. It is possible to refer to the matters disclosed in the documents cited in this specification and the matters relating to any known technology in an exemplary manner.

Some exemplary embodiments relate to techniques (OCT data processing methods) for processing a 3D data set collected by applying an OCT scan to a 3D region of a sample and are included in the 3D data set. It includes a process of creating a two-dimensional map from a plurality of A scan data.

Further, the 3D data set is positioned based on this 2D map. This positioning is typically a process of determining the correspondence (positional relationship) between the 3D data set and the sample area, and / or the correspondence (positional relationship) between the 3D data set and another 3D data set. ) Is included.

In addition, processing is performed based on part or all of the positioned 3D dataset. This process may be performed, for example, on one or more three-dimensional datasets and may include any of the following: analysis; analysis area setting; analysis data evaluation; evaluation area setting; Inspection area settings; Imaging area settings; Other inspection data (eg, data collected by applying an OCT scan to the same sample, data obtained by applying a different inspection than the OCT scan to the same sample, etc. One or more of the data collected by applying the OCT scan to one or more samples of the above and the data obtained by applying a test different from the OCT scan to the other one or more samples) Comparison.

Some other exemplary embodiments are the first 3D data set and the second 3D collected by applying OCT scans to the first and second 3D regions of the sample, respectively. It relates to a technique for processing a data set (OCT data processing method), and is a process of creating a first 2D map from a plurality of A scan data included in the 1st 3D data set and a process of creating a first 2D map and a second 3D. It includes a process of creating a second two-dimensional map from a plurality of A scan data included in the data set.

Further, based on the first 2D map and the 2nd 2D map, processing based on at least a part of the first 3D data set and / or at least a part of the second 3D data set is executed. To. This process may include, for example, any of the following: registration; tracking; panoramic OCT imaging (mosaic OCT imaging, montage OCT imaging); analysis (eg, static analysis or dynamic analysis (time series analysis,). Analysis of changes over time); Analysis area setting; Evaluation of analysis data; Evaluation area setting; Inspection area setting; Imaging area setting; Comparison with other inspection data.

Some exemplary embodiments are modality devices (including at least OCT devices) that are capable of implementing any of the exemplary OCT data processing methods described above, applying OCT scans to samples to create a 3D dataset. It has a function to collect. Also, some exemplary embodiments are methods of controlling such OCT devices.

Some exemplary embodiments are information processing devices (OCT data processing devices) capable of implementing any of the above exemplary OCT data processing methods, and OCT data (three-dimensional data sets) collected from samples. Has a function to accept. Also, some exemplary embodiments are methods of controlling such an OCT data processor.

Some exemplary embodiments are programs in which any of the above exemplary methods (OCT data processing methods, control methods) are performed on a computer (or at least a modality device that includes a computer and an OCT device). Also, some exemplary embodiments are computer-readable non-temporary recording media on which such programs are recorded.

In some exemplary embodiments, the "processor" is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), a programmable logic device (eg, a Programmable Logic Device). ), CPLD (Complex Programmable Logic Device), FPGA (Field Programmable Gate Array)) and the like. The processor provides some examples for achieving the desired function, for example, by reading a program stored in a storage circuit or a storage device and executing the program.

The type of OCT applicable to some exemplary embodiments is arbitrary, typically a swept source OCT or a spectral domain OCT, but may be of any other type. ..

The swept source OCT divides the light from the variable wavelength light source into measurement light and reference light, superimposes the return light of the measurement light from the sample on the reference light to generate interference light, and this interference light is used as an optical detector. This is a method of constructing an image by performing Fourier transformation or the like on the detection data collected in response to the sweeping of the wavelength and the scanning of the measurement light.

Spectral domain OCT divides the light from a low coherence light source (broadband light source) into measurement light and reference light, and superimposes the return light of the measurement light from the sample on the reference light to generate interference light. This is a method of constructing an image by detecting the spectral distribution of light source with a spectroscope and performing Fourier transform or the like on the detected spectral distribution.

That is, the swept source OCT is an OCT method for acquiring the spectral distribution of interference light by time division, and the spectral domain OCT is an OCT method for acquiring the spectral distribution of interference light by spatial division.

Examples of types other than such Fourier domain OCT include time domain OCT in which scanning in the axial direction (Z direction) is performed mechanically and sequentially, and XY planes orthogonal to the Z direction are imaged in two dimensions. There are en-face OCT or full field OCT and the like.

The exemplary embodiments described below are used in the field of ophthalmology for eye imaging, analysis, measurement, evaluation and the like. Note that some exemplary embodiments may be used in other fields. Examples of other fields include medical fields other than ophthalmology (dermatology, dentistry, surgery, etc.) and industrial fields (non-destructive inspection, etc.).

<Example of the first aspect>
1 and 2 show the configuration of an OCT device (ophthalmic device) 100 according to one exemplary embodiment. Ophthalmic apparatus 100 provides OCT data processing in addition to OCT imaging.

More specifically, the ophthalmic apparatus 100 is configured to target a 3D region of a sample (eye 120) and apply an OCT scan to collect a 3D data set. Here, the three-dimensional region is set to an arbitrary range. In addition, considering eye movements, the area to which the OCT scan targeting the three-dimensional area is actually applied does not have to match this three-dimensional area, but by using fixation or tracking, etc. It is possible to scan an area that almost matches the three-dimensional area.

Further, the ophthalmic apparatus 100 is configured to create a two-dimensional map based on the representative intensity value of each of the plurality of A scan data included in the three-dimensional data set collected from the sample. Here, the three-dimensional data set is the data before the imaging process (for example, Fourier transform) is performed. A three-dimensional dataset typically consists of a plurality of A-scan data arranged two-dimensionally on the XY plane, each A-scan data having a spectral intensity distribution (eg, the relationship between wave values and intensity values). Distribution data representing). By applying a Fourier transform or the like to the A scan data, A scan image data representing the reflection intensity distribution (backscatter intensity distribution) along the Z direction is generated. The process of creating a two-dimensional map from a three-dimensional dataset may include, for example, the process disclosed in Japanese Patent Application Publication No. 6230023 (US Patent Application Publication No. 2014/0293289).

Further, the ophthalmic apparatus 100 is configured to position the 3D data set based on the 2D map. This positioning includes, for example, a process of determining the correspondence (positional relationship) between the 3D data set and the sample area, and a process of determining the correspondence (positional relationship) between the 3D data set and another 3D data set. 3. Any of the processes for determining the correspondence (positional relationship) between the three-dimensional data set and the inspection data may be included. The positional relationship is determined, for example, by associating the coordinates of a three-dimensional data set with a predetermined identifier (such as the name of a part), associating the coordinates with each other, associating the coordinates with each other defined, and so on. It may be either of both representations in a common coordinate system.

In one example, the positional relationship between a specific part of the eye 120 and a three-dimensional data set can be determined. Specific sites of the eye 120 include, for example, lesions, blood vessels, optic nerve papilla, vitreous, subtissues of the fundus (inner limiting membrane, nerve fiber layer, ganglion cell layer, inner reticulum layer, inner nuclear layer, outer reticulum layer, etc. Outer nuclear layer, outer limiting membrane, photoreceptor layer, retinal pigment epithelial layer, Bruch's membrane, choroid, sclera, etc.), corneal subtissues (corneal epithelium, Bowman's membrane, intrinsic layer, dual layer, desme membrane, corneal endothelium, etc.) ), Iris, crystal body, chin zonule, ciliary body, vitreous body, and any of other ocular tissues. As a typical example, a portion of the three-dimensional data set corresponding to the optic nerve head is determined, or a portion of the three-dimensional data set corresponding to the retinal pigment epithelial layer is determined.

In another example, the positional relationship between the 3D data set of the eye 120 and another 3D data set (OCT data collected from the eye 120 or another eye) can be determined. As an example, the positional relationship between the two three-dimensional data sets can be determined so that the regions corresponding to a common specific part are matched with each other. For example, the positional relationship of these three-dimensional data sets can be determined so that the region of the three-dimensional data set corresponding to the optic disc and the region of another three-dimensional data set corresponding to the optic disc coincide with each other. it can. Alternatively, the positional relationship between the two three-dimensional data sets can be determined so that the regions corresponding to the specific sites that are different from each other match the positional relationship between the specific sites. For example, the region of the three-dimensional data set corresponding to the optic disc and the region of the other three-dimensional data set corresponding to the macula are three-dimensional so as to match the positional relationship between the optic disc and the macula. The positional relationship of the dataset can be determined.

In yet another example, the positional relationship between the three-dimensional data set of the eye 120 and the examination data (data obtained by applying a predetermined examination to the eye 120 or another eye) can be determined. For example, the positional relationship between the three-dimensional data set of the fundus of the eye 120 and the sensitivity distribution data obtained by the visual field test of the eye 120 (or the electroretinogram (EGR) obtained by the electrophysiological test) is determined. be able to. Alternatively, the positional relationship between the three-dimensional data set of the fundus of the eye 120 and the standard data (normalistic data) of the retinal layer thickness distribution can be determined.

In addition, the ophthalmic apparatus 100 is configured to perform processing based on some or all of the positioned 3D data sets. This process may be performed, for example, on one or more 3D datasets and may include any of the following: analysis; analysis area setting; analysis data evaluation; evaluation area setting; Inspection area setting; Imaging area setting; Comparison with inspection data. A part of the three-dimensional data set is called a partial data set. Also, for convenience of explanation, the partial data set may be the entire three-dimensional data set.

The data that can be used in the processing of the partial data set is the three-dimensional region of the eye 120, the three-dimensional data set, the two-dimensional map, or the data generated from any one of them, or any two or more of these. The data may be a combination of the above, or data generated from any two or more combinations of these.

As shown in FIG. 1, the ophthalmic apparatus 100 includes a light source 102 (for example, a broadband light source or a tunable light source) for generating a light beam. The beam splitter (BS) 104 divides the light beam from the light source 102 into a sample light beam (measurement light) and a reference light beam (reference light). In other words, the beam splitter 104 directs a part of the light beam from the light source 102 to the sample arm 106 and the other part to the reference arm 108.

The reference arm 108 includes a polarization controller 110 for adjusting the reference light beam (for example, for maximizing interference efficiency) and a collimator 112 for outputting the reference light beam as a parallel light beam. The reference light beam output from the collimator 112 is converted into a focused light beam by the lens 114 and projected onto the reflector 115. The reference light beam reflected by the reflector 115 returns to the beam splitter 104 through the reference arm 108. The lens 114 and the reflector 115 are integrally movable, thereby changing the distance from the collimator 112 (in other words, changing the length of the path of the reference light beam).

The sample arm 106 projects a sample light beam onto the eye 120 as a sample via the collimator 117, the two-dimensional scanner 116, and one or more objective lenses 118. The two-dimensional scanner 116 is, for example, a galvanometer mirror scanner or a MEMS scanner. The return light of the sample light beam projected onto the eye 120 returns to the beam splitter 104 through the sample arm 106. The two-dimensional scanner 116 enables OCT scanning of the three-dimensional region of the eye 120.

The beam splitter 104 superimposes the return light of the reference light beam and the return light of the sample light beam to generate an interference light beam. The interference light beam is guided by the detection unit 122 and detected. As a result, the echo time delay of light is measured from the interference spectrum.

The detector 122 sets a plurality of output sets based on the composition (that is, interferogram data) of the return light of the sample light beam supplied from the sample arm 106 and the return light of the reference light beam supplied from the reference arm 108. To generate. For example, each of the plurality of output sets generated by the detection unit 122 can correspond to the light intensity received at different wavelengths output from the light source 102. When the sample light beam is sequentially projected to a plurality of XY positions by the two-dimensional scanner 116, the detected light intensity is the reflection intensity distribution (backscattering) inside the eye 120 in the depth direction (Z direction) at each XY position. Contains information about intensity distribution).

In this way, a three-dimensional data set is obtained. The three-dimensional data set includes a plurality of A scan data corresponding to a plurality of XY positions. Each A scan data represents a spectral intensity distribution at the corresponding XY position. The three-dimensional data set collected by the detection unit 122 is sent to the processing device 124.

The processing device 124, for example, creates a two-dimensional map based on a three-dimensional data set, positions the three-dimensional data set based on the two-dimensional map, and processes based on a partial data set of the positioned three-dimensional data set. Configured to run. The processing device 124 includes a processor that operates according to the processing program. A specific example of the processing device 124 will be described later.

The control device 126 controls each part of the ophthalmic device 100. For example, the control device 126 performs various controls for applying an OCT scan to a preset region of the eye 120. The control device 126 includes a processor that operates according to a control program. A specific example of the control device 126 will be described later.

Although not shown, the ophthalmic apparatus 100 may further include a display device, an operation device, a communication device, and the like.

The processing device 124 and the control device 126 will be further described with reference to FIG. The processing device 124 includes a map creation unit 202, a positioning unit 204, and a processing execution unit 206. The control device 126 includes a scan control unit 210.

The OCT scanner 220 shown in FIG. 2 applies an OCT scan to a sample (eye 120). The OCT scanner 220 of this embodiment refers to, for example, the optical element group shown in FIG. 1, that is, a light source 102, a beam splitter 104, and a sample arm 106 (collimator 117, two-dimensional scanner 116, objective lens 118, etc.). It includes an arm 108 (collimator 112, lens 114, reflector 115, etc.) and a detection unit 122. In some exemplary embodiments, the OCT scanner may have other configurations.

The control device 126 controls each part of the ophthalmic device 100. Of the various controls, the control related to the OCT scan is executed by the scan control unit 210. The scan control unit 210 of this embodiment is configured to execute control of the OCT scanner 220, for example, control of the light source 102, control of the two-dimensional scanner 116, movement control of the lens 114 and the reflector 115, and the like. .. The scan control unit 210 includes a processor that operates according to the scan control program.

The processing device 124 executes various data processing (calculation, analysis, measurement, image processing, etc.). The above-mentioned three processes, that is, creation of a two-dimensional map based on a three-dimensional data set, positioning of the three-dimensional data set based on the two-dimensional map, and processing based on the partial data set of the positioned three-dimensional data set are performed. , The map creation unit 202, the positioning unit 204, and the processing execution unit 206, respectively.

The map creation unit 202 includes a processor that operates according to the map creation program. The positioning unit 204 includes a processor that operates according to the positioning program. The process execution unit 206 includes a processor that operates according to the process execution program.

The three-dimensional data collected from the eye 120 by the OCT scan is input to the map creation unit 202 from the OCT scanner 220. This OCT scan is performed by the OCT scanner 220 under the control of the scan control unit 210, targeting a preset three-dimensional region of the eye 120. As a result, the three-dimensional data set is collected and supplied to the map creation unit 202.

The map creation unit 202 creates a two-dimensional map based on the representative intensity values of each of the plurality of A scan data included in the three-dimensional data set. The three-dimensional data set is, for example, data before being subjected to imaging processing (Fourier transform or the like) by the processing execution unit 206 or another imaging processor. The A scan data is a spectral intensity distribution.

The process executed by the map creation unit 202 may be a process based on the method disclosed in the above-mentioned Japanese Patent No. 6230023. Briefly, this method involves applying a high-pass filter to the A scan data representing the spectral intensity distribution corresponding to a specific XY position to extract the amplitude component, and the inverse cumulative distribution function (inverse cumulative distribution function) from the extracted amplitude component. It includes a step of determining a single estimated intensity value (representative intensity value) based on the inverse CDF).

More specifically, as described in FIG. 5 and description thereof of Patent No. 6230023, in some exemplary embodiments, the mapping unit 202 applies high frequency filtering to the A scan data. A step, a step of downsampling the filtered A scan data (or a step of truncating), a step of squaring the downsampled A scan data (or a step of taking an absolute value), and a step of sorting the results (a step of sorting the results). Alternatively, it may be configured to execute a step of selecting a division point), a step of performing an calculation by the inverse CDF method, and a step of determining a single estimated strength value (representative strength value) from the result. ..

In some other exemplary embodiments, the mapping unit 202 includes a step of applying high frequency filtering to the A scan data, a step of downsampling (or truncating) the filtered A scan data, and a down step. A process of squaring the sampled A scan data (or a process of taking an absolute value), a process of selecting the maximum percentile value in the result, and a single estimated intensity value (representative intensity value) from the selected maximum percentile value. ) And may be configured to perform.

In still some other exemplary embodiments, the mapping unit 202 comprises applying high frequency filtering to the A scan data, downsampling (or truncating) the filtered A scan data, and the like. A process of selecting the minimum percentile value and the maximum percentile value from the downsampled A scan data, a process of squaring the selected minimum percentile value and the maximum percentile value, respectively (or a process of taking an absolute value), and combining them. It may be configured to perform steps (eg, calculating a mean value or selecting a default percentile value using the inverse CDF method).

For details of the map creation method exemplified above, refer to Japanese Patent No. 6230023. Further, the applicable mapping method is not limited to the above examples, and any method within the scope of the invention described in Japanese Patent No. 6230023 or any modification thereof can be applied.

By applying such a series of steps to each of the plurality of A scan data included in the three-dimensional data set, a plurality of representative intensity values corresponding to the plurality of XY positions can be obtained. By mapping the correspondence between the obtained plurality of XY positions and the plurality of representative intensity values, a two-dimensional map expressing the distribution of the representative intensity values on the XY plane can be obtained.

The two-dimensional map created by the map creation unit 202 is input to the positioning unit 204. The positioning unit 204 positions the 3D data set based on the 2D map.

For example, the positioning unit 204 determines the correspondence (positional relationship) between the three-dimensional data set and the sample region by analyzing the two-dimensional map and detecting the image of the predetermined position of the eye 120. Alternatively, the positioning unit 204 analyzes a two-dimensional map based on the three-dimensional data set to detect an image at a predetermined location, and analyzes a two-dimensional map based on another three-dimensional data set to detect an image at the predetermined location. Then, the correspondence (positional relationship) of the two three-dimensional data sets is determined based on the two detected images. Alternatively, the positioning unit 204 analyzes a two-dimensional map based on the three-dimensional data set to detect an image of a predetermined location, analyzes a two-dimensional map based on another three-dimensional data set, and obtains an image of another predetermined location. It is detected, and the correspondence (positional relationship) of the two three-dimensional data sets is determined based on the two detected images. Alternatively, the positioning unit 204 analyzes a two-dimensional map based on the three-dimensional data set to detect an image at a predetermined location, and associates the detected image with a specific part of the inspection data to associate the three-dimensional data set with the inspection data. Determine the correspondence (positional relationship) with.

Predetermined sites for image detection include, for example, lesions, blood vessels, optic nerve papilla, vitreous, subtissues of the fundus (inner limiting membrane, nerve fiber layer, ganglion cell layer, inner retinal layer, inner nuclear layer, etc. Outer reticular layer, outer nuclear layer, outer limiting membrane, photoreceptor layer, retinal pigment epithelial layer, Bruch's membrane, choroid, strong membrane, etc.), corneal subtissues (corneal epithelium, Bowman's membrane, intrinsic layer, dua layer, desme membrane, etc.) , Corneal endothelium, etc.), iris, crystalline body, chin zonule, ciliary body, vitreous body, and any of the other ocular tissues.

An arbitrary image processing technique is applied to detect an image at a predetermined location, and for example, an image classification method, an image detection method, an image recognition method, an image segmentation method, deep learning, or the like can be applied. As an example, the positioning unit 204 detects an image of the optic disc by analyzing a two-dimensional map based on a three-dimensional data set collected by applying an OCT scan to the fundus, and three-dimensionally based on the detected optic disc image. Data set positioning can be performed.

In another example, the control device 126 causes a display device (not shown) to display a two-dimensional map. The user specifies a desired area in the displayed 2D map using an operating device (not shown). The positioning unit 204 can position the 3D data set based on the area specified by the user on the displayed 2D map.

The data that can be used for positioning the 3D data set is not limited to the 2D map. For example, the data generated from the 2D map, the data used in the previous process of creating the 2D map (3D area, 3D data set, etc.) and / or the data generated from this data, etc. It may be referenced for positioning the dimensional data set.

The three-dimensional data set positioned by the positioning unit 204 is input to the processing execution unit 206. The processing execution unit 206 executes processing based on the partial data set of this three-dimensional data set.

Hereinafter, among various processes that can be executed by the process execution unit 206, analysis, evaluation, setting of an area of interest (analysis target area, evaluation target area), inspection area setting, imaging area setting, and inspection data Each example of the comparison will be described. It is possible to combine any two or more of these examples. The processing execution unit 206 includes the elements shown in the two or more examples to be combined. For example, the processing execution unit 206 may include a part or all of the elements shown in FIGS. 3A to 6.

The process execution unit 206A shown in FIG. 3A includes an analysis unit 2061 configured to execute a predetermined analysis process based on a partial data set of the three-dimensional data set.

For example, the analysis unit 2061 executes the layer thickness analysis. The target sites for layer thickness analysis are, for example, the retina, one subtissue of the retina, a combination of two or more subtissues of the retina, the choroid, one subtissue of the choroid, and two or more subtissues of the choroid. It may be any ocular tissue such as a combination, the cornea, one subtissue of the cornea, a combination of two or more subtissues of the cornea, the crystalline lens. The layer thickness analysis includes, for example, segmentation for identifying a region (partial data set) of a three-dimensional data set corresponding to such a target site, and measurement of thickness at at least one of the specified partial data sets. Including. Further, the analysis unit 2061 can determine the position in the eye 120 corresponding to each layer thickness measurement position based on the result of the positioning process executed by the positioning unit 204. This makes it possible to grasp which part of the eye 120 the layer thickness has been measured.

The analysis process that can be performed by the analysis unit 2061 is not limited to the layer thickness analysis. One example is dimensional analysis for measuring tissue dimensions. Tissues to be dimensionally analyzed include, for example, optic disc (cup diameter, disk diameter, rim diameter, depth, etc.), lesion (area, volume, length, etc.), blood vessels (thickness, length, etc.), etc. It may be. The dimensional analysis includes, for example, segmentation for identifying a region (partial data set) of the three-dimensional data set corresponding to such a target tissue, and measurement of the dimensions of the specified partial data set. Further, the analysis unit 2061 can determine the position in the eye 120 corresponding to the dimensional measurement position (target tissue) based on the result of the positioning process executed by the positioning unit 204. This makes it possible to grasp at which point in the eye 120 the dimension was measured.

Another example of the analysis process is shape analysis for measuring the shape of a tissue. The tissue to be analyzed for shape analysis may be, for example, an optic disc, a lesion, a blood vessel, or the like. The shape analysis includes, for example, segmentation for identifying a region (partial data set) of a three-dimensional data set corresponding to such a target tissue, and identification of the shape of the specified partial data set. The shape specification includes, for example, extraction of the contour of the partial data set and processing for obtaining the shape of the contour (circularity, roundness, ellipticity, cylindricity, etc.). Further, the analysis unit 2061 can determine the position in the eye 120 corresponding to the shape measurement position (target tissue) based on the result of the positioning process executed by the positioning unit 204. This makes it possible to grasp at which point in the eye 120 the shape was measured.

In addition to such shape measurement, orientation analysis can be performed to measure the orientation of the target tissue. The orientation analysis includes, for example, a process of obtaining a figure that approximates the shape of the contour of the partial data set (for example, an approximate ellipse) and a process of obtaining the direction of the approximate figure (for example, the direction of the major axis of the approximate ellipse). .. In another example, the orientation analysis includes the process of finding a specific parameter (eg, maximum diameter) of a partial data set and the process of finding the orientation based on this parameter (eg, the direction of a line segment indicating the maximum diameter). ..

The processing execution unit 206B shown in FIG. 3B includes an analysis unit 2061 configured to execute a predetermined analysis process based on a partial data set of the three-dimensional data set, and a predetermined evaluation based on the data obtained by this analysis process. Includes an evaluation unit 2062 configured to perform processing. The analysis unit 2061 may be the same as the analysis unit 2061 in FIG. 3A.

The evaluation unit 2062 evaluates whether or not the data of the eye 120 is normal (whether or not there is a suspicion of a disease) by comparing the data obtained by the analysis unit 2061 with the normalative data, for example. It is possible to determine the degree of illness, the degree of suspicion of illness, and so on.

The evaluation process is not limited to comparison with normal data, and may include an arbitrary evaluation process using statistics, an arbitrary evaluation process using arithmetic operations, and the like.

The processing execution unit 206C shown in FIG. 3C is set as an interest area setting unit 2063 configured to set a partial data set (a region of interest that is at least a part of a three-dimensional data set) to which analysis processing is applied. It includes an analysis unit 2061 configured to perform a predetermined analysis process based on the partial data set. The analysis unit 2061 may be the same as the analysis unit 2061 in FIG. 3A.

The region of interest setting unit 2063 sets the region of interest, for example, by analyzing a three-dimensional data set. Setting the region of interest includes, for example, segmentation to identify the region of interest in a 3D dataset.

In another example, control device 126 causes a display device (not shown) to display a 2D map (or any image based on a 3D data set). The user specifies a desired area in the displayed 2D map (or image) using an operating device (not shown). The region of interest setting unit 2063 can set the region of interest in the 3D data set based on the region specified by the user on the displayed 2D map (or image).

Areas of the eye corresponding to the area of interest include, for example, lesions, blood vessels, optic nerve papilla, vitreous, subtissues of the fundus (inner limiting membrane, nerve fiber layer, ganglion cell layer, inner reticular layer, inner nuclear layer, outer nuclear layer, outer). Reticulated layer, outer nuclear layer, outer limiting membrane, photoreceptor layer, retinal pigment epithelial layer, Bruch's membrane, choroid, strong membrane, etc.), corneal subtissues (corneal epithelium, Bowman's membrane, intrinsic layer, dua layer, desme membrane, etc. It may include any of), iris, crystalline body, chin zonule, ciliary body, vitreous body, and other ocular tissues (such as the corneal endothelium).

The evaluation unit 2062 can be combined with the processing execution unit 206C shown in FIG. 3C. The evaluation unit 2062 of this example executes a predetermined evaluation process based on the data obtained by the analysis process executed by the analysis unit 2061 based on the partial data set set by the area of interest setting unit 2063. The evaluation unit 2062 of this example may be the same as the evaluation unit 2062 of FIG. 3B.

The processing execution unit 206D shown in FIG. 3D applies the evaluation processing to the analysis unit 2061 configured to execute a predetermined analysis processing based on the partial data set of the three-dimensional data set, and the data obtained by this analysis processing. The area of interest setting unit 2063 configured to set the partial data to be performed (the area of interest that is at least a part of the analysis data) and the predetermined evaluation process based on the set area of interest are executed. Includes evaluation unit 2062. The analysis unit 2061 may be the same as the analysis unit 2061 in FIG. 3A. The evaluation unit 2062 may be the same as the evaluation unit 2062 in FIG. 3B.

The area of interest setting unit 2063 identifies a partial data set by analyzing, for example, a three-dimensional data set, and sets the partial data of the analysis data corresponding to this partial data set in the area of interest. Sub-dataset settings include, for example, segmentation.

In another example, the region of interest setting unit 2063 sets the region of interest by analyzing the analysis data obtained by the analysis unit 2061. As an example, the region of interest setting unit 2063 executes a process of detecting characteristic partial data in the analysis data and a process of setting the region of interest based on the detected partial data.

In yet another example, the control device 126 causes a display device (not shown) to display a 2D map (or any image or analysis data based on a 3D data set). The user specifies a desired area in the displayed 2D map (or image or analysis data) using an operating device (not shown). The area of interest setting unit 2063 can set an area of interest in the analysis data based on the area specified by the user on the displayed two-dimensional map (or image or analysis data).

The processing execution unit 206E shown in FIG. 4 includes an examination area setting unit 2064 configured to execute a setting of a predetermined examination application area (examination area) for the eye 120. The predetermined test may be any test, such as OCT scan, visual field test, microperimetry, electrophysiological test and the like.

For example, the inspection area setting unit 2064 analyzes one or more of the two-dimensional map, the three-dimensional data set, and the data generated based on at least one of them to determine the region of interest of the eye 120. The process and the process of setting the inspection area based on the determined region of interest are executed. Areas of interest are, for example, lesions, specific sites, specific tissues, and the like. Typically, the examination area is set to include at least a portion of the area of interest.

In one example, the inspection area setting unit 2064 focuses on the segmentation of the 2D map (or 3D data set) and the area in the 2D map identified by the segmentation in the eye 120 based on the result of the positioning process. It includes a process of converting to an area and a process of setting an inspection area based on this area of interest.

When the inspection area set by the inspection area setting unit 2064 is the OCT scan application area, information indicating this inspection area can be provided to the scan control unit 210. The scan control unit 210 controls the OCT scanner 220 so as to apply the OCT scan to this inspection area.

When the inspection area set by the inspection area setting unit 2064 is the OCT scan application area, the information indicating the inspection area can be provided to another OCT apparatus (inspection apparatus 130) via the communication device described above.

When the inspection area set by the inspection area setting unit 2064 is an application area of a certain inspection, information indicating the inspection area can be provided to the inspection device 130 corresponding to the inspection via the above-mentioned communication device. ..

The processing execution unit 206F shown in FIG. 5 has an imaging area setting unit 2065 configured to set a partial data set (an imaged area that is at least a part of a three-dimensional data set) to which the imaging process is applied. , Includes an image data generator 2066 configured to generate image data for the set imaging area.

The imaging process includes at least the Fourier transform. Examples of the imaging process include general OCT image construction, motion contrast (OCT angiography, etc.), phase image construction, polarized image construction, and the like.

For example, the imaging area setting unit 2065 analyzes one or more of the two-dimensional map, the three-dimensional data set, and the data generated based on at least one of them to determine the region of interest of the eye 120. The process of setting the imaged area and the process of setting the imaged area based on the determined region of interest are executed. Areas of interest are, for example, lesions, specific sites, specific tissues, and the like. Typically, the imaging area is set to include at least a portion of the area of interest.

In one example, the imaging area setting unit 2065 in the eye 120 based on the segmentation of the 2D map (or 3D data set) and the region in the 2D map identified thereby in the eye 120. It includes a process of converting to a region of interest and a process of setting an imaging area based on the region of interest.

The image data generation unit 2066 is configured to generate image data based on the data collected by the OCT scanner 220. For example, the image data generation unit 2066 forms image data of a tomographic image of the eye 120 based on the output (sampling data, interference signal data) from the OCT scanner 220. This image data generation process includes filtering, a fast Fourier transform (FFT), and the like, as in the conventional (swept source or spectral domain) OCT. By such processing, the reflection intensity profile (reflection intensity profile along the Z direction) in the A line (scan path of the measured light beam in the eye 120) corresponding to each XY position is acquired, and this reflection intensity profile is imaged. The image data of this A line (A scan image data) is formed by the conversion.

Further, the image data generation unit 2066 forms a plurality of A-scan image data according to the mode of OCT scan (deflection of the measurement light beam, movement of the A-scan position), and arranges these A-scan image data in two dimensions. Image data and three-dimensional image data can be constructed.

When a plurality of tomographic image data are obtained by raster scanning or the like, the image data generation unit 2066 embeds these tomographic image data in a single three-dimensional coordinate system to construct stack data, and voxelizes the stack data. Can be applied to construct voxel data (volume data).

The image data generation unit 2066 can render stack data or volume data. The rendering method is arbitrary and may include, for example, volume rendering, multi-section reconstruction (MPR), surface rendering, and the like. Further, the image data generation unit 2066 can construct a plane image (for example, a front image, an unfass image) from the stack data or the volume data. For example, the image data generation unit 2066 can construct a projection image by integrating stack data or volume data along each A line.

In this example, the image data generation unit 2066 applies imaging processing to the OCT data set (partial data set of the three-dimensional data set) included in the imaging area set by the imaging area setting unit 2065 to obtain image data. To generate.

In another example, the image data generation unit 2066 applies an imaging process to a three-dimensional data set to generate image data. Further, the processing execution unit 206F extracts the partial image data corresponding to the imaging area set by the imaging area setting unit 2065 from the image data. Extraction of partial image data includes, for example, clipping, cropping, or trimming.

The processing execution unit 206F shown in FIG. 5 has an image construction function (image data generation unit 2066), but the OCT device (ophthalmology device) of another exemplary embodiment does not have to have an image construction function. .. In this case, information indicating the imaging area set by the imaging area setting unit 2065 can be provided to an external device (including an imaging processor) via a communication device (not shown).

In the example shown in FIG. 6, a reception unit 140 for receiving data (examination data) acquired from the eye 120 by a predetermined examination different from OCT is provided. Further, the processing execution unit 206G of this example includes a comparison unit 2067. The comparison unit 2067 is configured to perform a predetermined comparison process between at least a part of the inspection data received by the reception unit 140 and at least a part of the three-dimensional data set collected by the OCT scanner 220. ..

The reception unit 140 receives the examination data obtained from the eye 120 from the outside (for example, an ophthalmic apparatus, an image archiving system, a recording medium). The reception unit 140 may include, for example, a communication device or a drive device.

The test data may be any modality or data obtained by the test. Examples of test data are sensitivity distribution data obtained by visual field test of eye 120, electroretinogram (EGR) obtained by electrophysiological test, and tear distribution data obtained by tear liquor imaging (anterior ocular segment imaging). and so on.

The comparison unit 2067, for example, has registration between the two-dimensional map and inspection data based on the result of the positioning process executed by the positioning unit 204, and the three-dimensional data set and inspection data based on the result of this registration. It may be configured to perform an intervening registration and a predetermined comparison process based on the registered 3D dataset (at least a portion thereof) and the inspection data.

In this comparison process, for example, a comparison between a three-dimensional data set and inspection data, a comparison between a two-dimensional map based on the three-dimensional data set and inspection data, and a comparison between image data and inspection data based on the three-dimensional data set, 2 Comparison of dimensional map analysis data and inspection data, comparison of image data analysis data and inspection data, comparison of 3D data set and inspection data processing data, 2D map and inspection data based on 3D data set Comparison with the processing data of the two-dimensional map, comparison of the image data based on the three-dimensional data set with the processing data of the inspection data, comparison of the analysis data of the two-dimensional map with the processing data of the inspection data, and the analysis data and inspection of the image data. Any one or more of comparisons of the data with the processed data may be included.

The processing device 124 may be capable of performing various data processing other than the data processing described above. The processing device 124 can process the data (OCT data) acquired by using the OCT scan. The OCT data is, for example, interference signal data (eg, at least a portion of a 3D dataset), reflection intensity profile, or image data.

The processing device 124 may be capable of processing data other than OCT data. For example, when the ophthalmic apparatus 100 has a data acquisition device other than the OCT scanner 220, the processing apparatus 124 can process the data acquired by this data acquisition apparatus. The ophthalmic apparatus adopted as the data acquisition apparatus may be an ophthalmologic imaging apparatus (ophthalmic imaging apparatus) such as a fundus camera, a scanning laser ophthalmoscope (SLO), a surgical microscope, and a slit lamp microscope. Other examples include ophthalmic measuring devices such as refractometers, keratometers, tonometers, axial length measuring devices, specular microscopes, wavefront analyzers, perimeters, and the like. Further, when the OCT device is an arbitrary medical device (that is, when the OCT device is a device used in any clinical department), the medical device adopted as the data acquisition device is, for example, an arbitrary medical imaging device. And / or any medical examination device. Further, for the OCT device used in a field other than medical treatment, a data acquisition device suitable for the field can be adopted.

Some examples of the operations that can be performed by the ophthalmic apparatus 100 having the configuration illustrated above will be described.

See FIG. 7. In this example, first, the scan control unit 210 controls the OCT scanner 220 so as to apply the OCT scan to the eye 120 and collect the three-dimensional data set (S1).

Next, the map creation unit 202 creates a two-dimensional map based on the representative intensity values of the plurality of A scan data included in the three-dimensional data set collected in step S1 (S2).

Next, the positioning unit 204 positions the three-dimensional data set collected in step S1 based on the two-dimensional map created in step S2 (S3).

Next, the process execution unit 206 executes a process based on the partial data set of the three-dimensional data set positioned in step S3 (S4).

The process of step S4 includes, for example, a predetermined analysis process, a predetermined evaluation process, an area of interest setting, an inspection area setting, an imaging area setting, an image data generation, and a predetermined comparison process with the inspection data. Any one or more of them may be included. In other words, the processing execution unit 206 is among the analysis unit 2061, the evaluation unit 2062, the region of interest setting unit 2063, the inspection area setting unit 2064, the imaging area setting unit 2065, the image data generation unit 2066, and the comparison unit 2067. Any one or more may be included.

In one example, when the process of step S4 includes the setting of the OCT scan application area (setting of the inspection area), the scan control unit 210 is provided with the information indicating the set inspection area. The scan control unit 210 controls the OCT scanner 220 so as to apply an OCT scan to a set inspection area and collect a data set. The processing execution unit 206 (image data generation unit 2066) generates image data from the collected data set. The control device 126 can display the generated image data on a display device (not shown). The display device may be, for example, any of an element of the ophthalmic device 100, a peripheral device of the ophthalmic device 100, and a device (such as a telemedicine device) that can be connected to the ophthalmic device 100 via a communication line. Further, the control device 126 can store the generated image data in a storage device (not shown). The storage device may be, for example, any of an element of the ophthalmic device 100, a peripheral device of the ophthalmic device 100, a device that can be connected to the ophthalmic device 100 via a communication line, and a portable recording medium.

Some effects of the ophthalmic apparatus (OCT apparatus) 100 of this embodiment will be described.

The ophthalmic apparatus 100 of this embodiment includes an OCT scanner 220, a map creation unit 202, a positioning unit 204, and a processing execution unit 206. The OCT scanner 220 applies an OCT scan to a sample (eye 120) to collect a three-dimensional data set. The map creation unit 202 creates a two-dimensional map based on the representative intensity values of the plurality of A scan data included in the three-dimensional data set. The positioning unit 204 positions the three-dimensional data set based on the two-dimensional map. The processing execution unit 206 executes processing based on at least a partial data set of the positioned three-dimensional data set.

According to such an ophthalmic apparatus 100, the 3D data set is positioned based on the 2D map created from the 3D data set collected by the OCT scan, and the processing based on the 3D data set in which the positioning is performed. Therefore, 3D image construction and landmarks such as those of the inventions described in Patent Document 1 (US Patent No. 78894945) and Patent Document 2 (US Patent No. 8405834) can be performed. It is possible to perform processing on the desired region of the sample. Therefore, it is possible to improve the efficiency of resources required for processing and shorten the processing time, and it is possible to improve the efficiency of OCT data processing. Thereby, for example, real-time processing can be preferably performed.

In the ophthalmic apparatus 100 of this aspect, the processing execution unit 206 may include an analysis unit 2061 configured to perform a predetermined analysis processing based on at least a partial data set of the positioned three-dimensional data set.

With such a configuration, it is possible to perform an analysis on a desired region of the sample without performing three-dimensional image construction or landmarks as in the prior art. Therefore, it is possible to improve the efficiency of resources required for analysis and shorten the processing time, and it is possible to improve the efficiency of analysis. Thereby, for example, it is possible to preferably perform real-time analysis.

In the ophthalmic apparatus 100 of this embodiment, the processing execution unit 206 may include an evaluation unit 2062 configured to execute a predetermined evaluation process based on the analysis data obtained by the analysis unit 2061.

With such a configuration, it is possible to perform an evaluation on a desired region of a sample without performing three-dimensional image construction or landmarks as in the prior art. Therefore, it is possible to improve the efficiency of resources required for evaluation and shorten the processing time, and it is possible to improve the efficiency of evaluation. Thereby, for example, it is possible to preferably perform evaluation in real time.

In the ophthalmic apparatus 100 of this embodiment, the processing execution unit 206 may include an interest region setting unit 2063 configured to set an interest region (analysis target region and / or evaluation target region).

With such a configuration, it is possible to set an area of interest without performing three-dimensional image construction or landmarks as in the prior art. Therefore, it is possible to improve the efficiency of the resources required for setting the area of interest and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, it is possible to preferably set the region of interest in real time.

In the ophthalmic apparatus 100 of this aspect, the processing execution unit 206 may include an examination area setting unit 2064 configured to set a predetermined examination application area (examination area) for the sample.

According to such a configuration, it is possible to set the inspection area without performing the three-dimensional image construction and landmarks as in the prior art. Therefore, it is possible to improve the efficiency of the resources required for setting the inspection area and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, it is possible to preferably set the inspection area in real time.

In the ophthalmic apparatus 100 of this embodiment, the processing execution unit 206 includes an imaging area setting unit 2065 configured to set a partial data set (imaging area) to which a predetermined imaging process is applied. Good.

With such a configuration, it is possible to set an imaging area without performing three-dimensional image construction or landmarks as in the prior art. Therefore, it is possible to improve the efficiency of the resources required for setting the imaging area and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, it is possible to preferably set the imaging area in real time.

The ophthalmic apparatus 100 of this embodiment may include means for preparing examination data obtained from a sample by a predetermined examination different from OCT. The inspection data preparation means includes, for example, a means for receiving inspection data (reception unit 140) or a means for applying inspection to a sample and acquiring inspection data. Further, the processing execution unit 206 may include a comparison unit 2067 configured to execute a predetermined comparison processing between the inspection data and at least a part of the three-dimensional data set.

According to such a configuration, it is possible to set the comparison process between the inspection data and the OCT data without performing the three-dimensional image construction and the landmark as in the prior art. Therefore, it is possible to improve the efficiency of the resources required for the comparison processing and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, it is possible to preferably perform real-time comparison processing.

As described above, the sample of this embodiment is a living eye, but the same function and configuration can be applied to an OCT device for a sample other than the living eye. That is, it is possible to combine any matter (function, hardware configuration, software configuration, etc.) relating to the ophthalmic apparatus 100 with the OCT apparatus of any aspect. At this time, the action and effect according to the combined items are exhibited.

Some exemplary embodiments relate to methods of controlling an OCT apparatus including an OCT scanner and a processor that apply an OCT scan to a sample. This control method may include at least the following steps: controlling the OCT scanner to collect 3D data sets from the sample; representative of each of the multiple A scan data contained in this 3D data set. The step of controlling the processor to create a 2D map based on the intensity value; the step of controlling the processor to position the 3D data set based on this 2D map; the 3D positioned A step that controls a processor to perform processing based on at least a partial dataset of a dimensional dataset.

It is possible to combine any matter (function, hardware configuration, software configuration, etc.) relating to the ophthalmic apparatus 100 with the control method of this embodiment. At this time, the action and effect according to the combined items are exhibited.

Some exemplary embodiments relate to a program that causes a computer to perform such a method of controlling an OCT apparatus. It is possible to combine any of the items described with respect to the ophthalmic apparatus 100 with this program. Also, some exemplary embodiments relate to computer-readable non-temporary recording media on which such programs are recorded. It is possible to combine any of the items described with respect to the ophthalmic apparatus 100 with this recording medium.

Some exemplary embodiments relate to devices (OCT data processing devices) that process data collected using OCT. The OCT data processing device may include at least the following elements: a reception unit that receives a 3D data set collected by applying an OCT scan to a sample; a plurality of A scan data included in this 3D data set. A map creation unit that creates a two-dimensional map based on each representative intensity value of the above; a positioning unit that positions the three-dimensional data set based on the two-dimensional map; at least the positioned three-dimensional data set. A process execution unit that executes processing based on a partial data set.

That is, this OCT data processing device replaces (or in addition to) the OCT scanner 220 of the OCT device (ophthalmic device) 100 described above, and externally (for example, OCT) the three-dimensional data set obtained by the OCT scan. It includes an element (reception unit) that receives from (device, image archiving system, recording medium). The reception unit may include, for example, a communication device or a drive device.

It is possible to combine any matter (function, hardware configuration, software configuration, etc.) relating to the ophthalmic apparatus 100 with the OCT data processing apparatus of this embodiment. At this time, the action and effect according to the combined items are exhibited.

Some exemplary embodiments relate to methods of controlling an OCT data processor, including a processor. This control method may include at least the following steps: applying an OCT scan to the sample and controlling the processor to accept the collected 3D data set; multiple steps contained in this 3D data set. A Step to control the processor to create a 2D map based on each representative intensity value of the scan data; a step to control the processor to position the 3D data set based on this 2D map A step of controlling the processor to perform processing based on at least a partial dataset of the 3D dataset that has been positioned.

It is possible to combine any matter (function, hardware configuration, software configuration, etc.) relating to the ophthalmic apparatus 100 with the control method of this embodiment. At this time, the action and effect according to the combined items are exhibited.

Some exemplary embodiments relate to a program that causes a computer to perform such a control method for an OCT data processor. It is possible to combine any of the items described with respect to the ophthalmic apparatus 100 with this program. Also, some exemplary embodiments relate to computer-readable non-temporary recording media on which such programs are recorded. It is possible to combine any of the items described with respect to the ophthalmic apparatus 100 with this recording medium.

Some exemplary embodiments of the OCT apparatus (eg, ophthalmic apparatus 100), some exemplary embodiments of the OCT apparatus control method, some exemplary embodiments of the OCT data processing apparatus, or some. The control method of the OCT data processing apparatus of the exemplary embodiment provides a method of processing OCT data. This OCT data processing method may include at least the following steps: a step of preparing a 3D data set collected from a sample; a representative intensity value of each of the plurality of A scan data contained in this 3D data set. Steps to create a 2D map based on; Positioning of the 3D data set based on this 2D map; Perform processing based on at least a partial data set of the positioned 3D data set Step.

It is possible to combine any matter (function, hardware configuration, software configuration, etc.) relating to the ophthalmic apparatus 100 with the OCT data processing method of this embodiment. At this time, the action and effect according to the combined items are exhibited.

Some exemplary embodiments relate to programs that cause a computer to perform such an OCT data processing method. It is possible to combine any of the items described with respect to the ophthalmic apparatus 100 with this program. Also, some exemplary embodiments relate to computer-readable non-temporary recording media on which such programs are recorded. It is possible to combine any of the items described with respect to the ophthalmic apparatus 100 with this recording medium.

In some embodiments, the non-temporary recording medium on which the program is recorded may be in any form, and examples thereof include magnetic disks, optical disks, magneto-optical disks, and semiconductor memories.

<Example of the second aspect>
8 and 9 show the configuration of the OCT device (ophthalmic device) 150 according to one exemplary embodiment. Ophthalmic apparatus 150 provides OCT data processing in addition to OCT imaging.

The ophthalmic apparatus 150 is configured to apply an OCT scan to a sample (eye) to collect a first 3D data set and a second 3D data set. Here, the first 3D region, which is the application area (target) of the OCT scan for collecting the first 3D data set, and the application area of the OCT scan for collecting the second 3D data set. The second three-dimensional region, which is the (target), may coincide with each other, may partially coincide with each other, or may not have a common region. In addition, the ophthalmic apparatus 150 may collect three or more three-dimensional data sets. Thus, the ophthalmic apparatus 150 collects at least two 3D data sets by applying an OCT scan to the sample. In addition, considering eye movements, the area to which the OCT scan targeting a certain three-dimensional area is actually applied does not have to match the three-dimensional area, but fixation, tracking, etc. are used. As a result, it is possible to scan an area that substantially matches the three-dimensional area.

Further, the ophthalmic apparatus 150 creates a first two-dimensional map based on the representative intensity value of each of the plurality of A scan data included in the first three-dimensional data set, and also creates a second three-dimensional data set. It is configured to create a second two-dimensional map based on the representative intensity value of each of the plurality of A scan data included in. The process of creating a two-dimensional map from a three-dimensional data set is executed, for example, in the same manner as that of the first embodiment.

Further, the ophthalmic apparatus 150 is based on the first two-dimensional map and the second two-dimensional map, and at least the first partial data set of the first three-dimensional data set and at least the second three-dimensional data set. It is configured to perform processing based on at least one of the second sub-datasets. Hereinafter, such an ophthalmic apparatus 150 will be described in more detail.

As shown in FIG. 8, the ophthalmic apparatus 150 includes the same optical element group as the ophthalmic apparatus 100 (FIG. 1) of the first embodiment. The ophthalmic apparatus 150 includes a light source 102 (eg, a broadband light source or a tunable light source) for generating a light beam. The beam splitter (BS) 104 divides the light beam from the light source 102 into a sample light beam (measurement light) and a reference light beam (reference light). In other words, the beam splitter 104 directs a part of the light beam from the light source 102 to the sample arm 106 and the other part to the reference arm 108.

The reference arm 108 includes a polarization controller 110 for adjusting the reference light beam (for example, for maximizing interference efficiency) and a collimator 112 for outputting the reference light beam as a parallel light beam. The reference light beam output from the collimator 112 is converted into a focused light beam by the lens 114 and projected onto the reflector 115. The reference light beam reflected by the reflector 115 returns to the beam splitter 104 through the reference arm 108. The lens 114 and the reflector 115 are integrally movable, thereby changing the distance from the collimator 112 (in other words, changing the length of the path of the reference light beam).

The sample arm 106 projects a sample light beam onto the eye 120 as a sample via the collimator 117, the two-dimensional scanner 116, and one or more objective lenses 118. The two-dimensional scanner 116 is, for example, a galvanometer mirror scanner or a MEMS scanner. The return light of the sample light beam projected onto the eye 120 returns to the beam splitter 104 through the sample arm 106. The two-dimensional scanner 116 enables OCT scanning of the three-dimensional region of the eye 120.

The beam splitter 104 superimposes the return light of the reference light beam and the return light of the sample light beam to generate an interference light beam. The interference light beam is guided by the detection unit 122 and detected. As a result, the echo time delay of light is measured from the interference spectrum.

The detector 122 sets a plurality of output sets based on the composition (that is, interferogram data) of the return light of the sample light beam supplied from the sample arm 106 and the return light of the reference light beam supplied from the reference arm 108. To generate. For example, each of the plurality of output sets generated by the detection unit 122 can correspond to the light intensity received at different wavelengths output from the light source 102. When the sample light beam is sequentially projected to a plurality of XY positions by the two-dimensional scanner 116, the detected light intensity is the reflection intensity distribution (backscattering) inside the eye 120 in the depth direction (Z direction) at each XY position. Contains information about intensity distribution).

In this way, a three-dimensional data set is obtained. The three-dimensional data set includes a plurality of A scan data corresponding to a plurality of XY positions. Each A scan data represents a spectral intensity distribution at the corresponding XY position. The three-dimensional data set collected by the detection unit 122 is sent to the processing device 160.

The processing device 160 is configured to, for example, create a two-dimensional map based on a three-dimensional data set and process a three-dimensional data set based on the two-dimensional map. The processing device 160 includes a processor that operates according to a processing program. A specific example of the processing device 160 will be described later.

The control device 170 controls each part of the ophthalmic device 150. For example, the control device 170 performs various controls for applying an OCT scan to a preset area of the eye 120. The control device 170 includes a processor that operates according to a control program. A specific example of the control device 170 will be described later.

Although not shown, the ophthalmic apparatus 150 may further include a display device, an operation device, a communication device, and the like.

The processing device 160 and the control device 170 will be further described with reference to FIG. The processing device 160 includes a map creation unit 252 and a processing execution unit 256. The control device 170 includes a scan control unit 260.

The OCT scanner 270 shown in FIG. 9 applies an OCT scan to a sample (eye 120). The OCT scanner 270 of this embodiment includes, for example, the optical element group shown in FIG. 8, that is, a light source 102, a beam splitter 104, a sample arm 106 (collimator 117, a two-dimensional scanner 116, an objective lens 118, etc.), and a reference arm 108 ( It includes a collimator 112, a lens 114, a reflector 115, etc.), and a detection unit 122. In some exemplary embodiments, the OCT scanner may have other configurations.

The control device 170 controls each part of the ophthalmic device 150. Of the various controls, the control related to the OCT scan is executed by the scan control unit 260. The scan control unit 260 of this embodiment is configured to execute control of the OCT scanner 270, for example, control of the light source 102, control of the two-dimensional scanner 116, movement control of the lens 114 and the reflector 115, and the like. .. The scan control unit 260 includes a processor that operates according to the scan control program.

The processing device 160 executes various data processing (calculation, analysis, measurement, image processing, etc.). The above-mentioned two processes, that is, the creation of the two-dimensional map based on the three-dimensional data set and the process related to the three-dimensional data set based on the two-dimensional map are executed by the map creation unit 252 and the process execution unit 252, respectively. Will be done.

The map creation unit 252 includes a processor that operates according to the map creation program. The process execution unit 256 includes a processor that operates according to the process execution program.

The three-dimensional data collected from the eye 120 by the OCT scan is input to the map creation unit 252 from the OCT scanner 270. In this embodiment, the OCT scanner 270 executes an OCT scan under the control of the scan control unit 260, targeting the first three-dimensional region of the preset eye 120, and the preset second 3 The OCT scanner 270 executes an OCT scan under the control of the scan control unit 260, targeting the three-dimensional region. As a result, the first three-dimensional data set and the second three-dimensional data set are collected and supplied to the map creation unit 252.

The map creation unit 252 creates a first two-dimensional map based on the representative intensity values of each of the plurality of A scan data included in the first three-dimensional data set. The first three-dimensional data set is data before the image data generation unit 206 performs an imaging process (Fourier transform or the like). The A scan data is a spectral intensity distribution. In the same way, a second 2D map is created from the 2D data set. The process executed by the map creation unit 252 may be the same as that of the map creation unit 202 of the first embodiment.

The first two-dimensional map and the second two-dimensional map created by the map creation unit 252 are input to the processing execution unit 256. Based on these two-dimensional maps, the processing execution unit 256 executes processing based on the partial data set of the first three-dimensional data set and / or the partial data set of the second three-dimensional data set. As in the first embodiment, in this embodiment as well, the partial data set of the three-dimensional data set may be a part or the whole of the three-dimensional data set.

Below, among various processes that can be executed by the process execution unit 256, analysis, evaluation, setting of interest area (analysis target area, evaluation target area), inspection area setting, imaging area setting, comparison with inspection data , Registration, tracking, and panoramic OCT imaging (mosaic OCT imaging, montage OCT imaging) will be described. It is possible to combine any two or more of these examples. The processing execution unit 256 includes the elements shown in the two or more examples to be combined. For example, the processing execution unit 256 may include a part or all of the elements shown in FIGS. 10A to 16.

The processing execution unit 256A shown in FIG. 10A includes an analysis unit 2561. The analysis unit 2561 is based on the first 2D map and the 2nd 2D map, and the analysis unit 2561 is the first partial data set of the first 3D data set and the second part of the second 3D data set. It is configured to perform a predetermined analysis process based on at least one of the datasets.

For example, the analysis unit 2561 executes the layer thickness analysis. The target sites for layer thickness analysis are, for example, the retina, one subtissue of the retina, a combination of two or more subtissues of the retina, the choroid, one subtissue of the choroid, and two or more subtissues of the choroid. It may be any ocular tissue such as a combination, the cornea, one subtissue of the cornea, a combination of two or more subtissues of the cornea, the crystalline lens. The layer thickness analysis includes, for example, segmentation for identifying a region (partial data set) of a three-dimensional data set corresponding to such a target site, and measurement of thickness at at least one of the specified partial data sets. Including.

The analysis unit 2561 may be able to perform the same positioning process as the positioning unit 204 of the first embodiment, and based on the result, the position in the eye 120 corresponding to each layer thickness measurement position can be determined. it can. This makes it possible to grasp which part of the eye 120 the layer thickness has been measured.

The analysis unit 2561 can apply such a layer thickness analysis to at least one of the first partial data set and the second partial data set. The layer thickness analysis applied to only one of the first partial data set and the second partial data set may be similar to, for example, the layer thickness analysis of the first embodiment.

When applying the layer thickness analysis to both the first partial data set and the second partial data set, at least a part of the region of the eye 120 corresponding to the first partial data set and the second partial data set. When at least a part of the region of the eye 120 corresponding to is the same, the analysis unit 2561 can obtain, for example, a change in the layer thickness in the common region. That is, the analysis unit 2561 may obtain the difference (difference, ratio, etc.) between the layer thickness of the common region based on the first partial data set and the layer thickness of the common region based on the second partial data set. it can. When the time when the first 3D data set is collected and the time when the second 3D data set is collected are different, the difference obtained in this way is the time change (time series change, aging) of the layer thickness. Change).

Here, a time-series change of a predetermined parameter value (layer thickness value) based on two 3D data sets (1st 3D data set and 2nd 3D data set) collected at different time points. Although the example of obtaining the above is described, it is also possible to perform the same time series analysis based on three or more three-dimensional data sets.

When applying the layer thickness analysis to both the first and second partial data sets, at least a portion of the area of the eye 120 corresponding to the first partial data set and the second partial data set. When at least a part of the region of the eye 120 corresponding to is different, the analysis unit 2561 synthesizes, for example, a layer thickness distribution based on the first partial data set and a layer thickness distribution based on the second partial data set. Therefore, the layer thickness distribution in a wider range can be obtained.

The analysis process that can be performed by the analysis unit 2561 is not limited to the layer thickness analysis. One example is dimensional analysis for measuring tissue dimensions. Tissues to be dimensionally analyzed include, for example, optic disc (cup diameter, disk diameter, rim diameter, depth, etc.), lesion (area, volume, length, etc.), blood vessels (thickness, length, etc.), etc. It may be. The dimensional analysis includes, for example, segmentation for identifying a region (partial data set) of the three-dimensional data set corresponding to such a target tissue, and measurement of the dimensions of the specified partial data set. Any of the above items relating to layer thickness analysis can be applied to dimensional analysis.

Another example of the analysis process is shape analysis for measuring the shape of a tissue. The tissue to be analyzed for shape analysis may be, for example, an optic disc, a lesion, a blood vessel, or the like. The shape analysis includes, for example, segmentation for identifying a region (partial data set) of a three-dimensional data set corresponding to such a target tissue, and identification of the shape of the specified partial data set. The shape specification includes, for example, extraction of the contour of the partial data set and processing for obtaining the shape of the contour (circularity, roundness, ellipticity, cylindricity, etc.). Any of the above items relating to layer thickness analysis can be applied to shape analysis.

In addition to such shape measurement, orientation analysis can be performed to measure the orientation of the target tissue. The orientation analysis includes, for example, a process of obtaining a figure that approximates the shape of the contour of the partial data set (for example, an approximate ellipse) and a process of obtaining the direction of the approximate figure (for example, the direction of the major axis of the approximate ellipse). .. In another example, the orientation analysis includes the process of finding a specific parameter (eg, maximum diameter) of a partial data set and the process of finding the orientation based on this parameter (eg, the direction of a line segment indicating the maximum diameter). ..

The process execution unit 256B shown in FIG. 10B executes a predetermined analysis process based on at least one of the first partial data set of the first three-dimensional data set and the second partial data set of the second three-dimensional data set. The analysis unit 2561 configured to perform the analysis process and the evaluation unit 2562 configured to execute a predetermined evaluation process based on the data obtained by the analysis process are included. The analysis unit 2561 may be the same as the analysis unit 2561 of FIG. 10A.

The evaluation unit 2562 evaluates whether or not the data of the eye 120 is normal (whether or not there is a suspicion of a disease) by comparing the data obtained by the analysis unit 2561 with the normalative data, for example. It is possible to determine the degree of illness, the degree of suspicion of illness, and so on.

The evaluation process is not limited to comparison with normal data, and may include an arbitrary evaluation process using statistics, an arbitrary evaluation process using arithmetic operations, and the like.

The combination of analysis processing and evaluation processing applied to only one of the first partial data set and the second partial data set is, for example, the same as the combination of analysis processing and evaluation processing of the first embodiment. You can.

When the combination of the analysis process and the evaluation process is applied to both the first partial data set and the second partial data set, the processing execution unit 256B obtains, for example, the time change of the analysis data, or is based on this. It is possible to obtain the time change of evaluation.

The processing execution unit 256C shown in FIG. 10C is set with an interest area setting unit 2563 configured to set a partial data set (a region of interest that is at least a part of a three-dimensional data set) to which analysis processing is applied. It includes an analysis unit 2561 configured to perform a predetermined analysis process based on the partial data set. The analysis unit 2561 may be the same as the analysis unit 2561 of FIG. 10A.

The region of interest setting unit 2563 sets the region of interest by, for example, analyzing a three-dimensional data set. Setting the region of interest includes, for example, segmentation to identify the region of interest in a 3D dataset. In this example, the region of interest setting unit 2563 sets the first region of interest in the first 3D data set and the second region of interest in the second 3D data set. The region of the eye 120 corresponding to the first region of interest and the region of the eye 120 corresponding to the second region of interest may be the same or different from each other.

In another example, the control device 170 causes a display device (not shown) to display a 2D map (or any image based on a 3D data set). The user specifies a desired area in the displayed 2D map (or image) using an operating device (not shown). The region of interest setting unit 2563 can set the region of interest in the 3D data set based on the region specified by the user on the displayed 2D map (or image). As a result, the first region of interest in the first 3D data set and the second region of interest in the second 3D data set are set.

Areas of the eye corresponding to the area of interest include, for example, lesions, blood vessels, optic nerve papilla, vitreous, subtissues of the fundus (inner limiting membrane, nerve fiber layer, ganglion cell layer, inner reticular layer, inner nuclear layer, outer nuclear layer, outer). Reticulated layer, outer nuclear layer, outer limiting membrane, photoreceptor layer, retinal pigment epithelial layer, Bruch's membrane, choroid, strong membrane, etc.), corneal subtissues (corneal epithelium, Bowman's membrane, intrinsic layer, dua layer, desme membrane, etc. It may include any of), iris, crystalline body, chin zonule, ciliary body, vitreous body, and other ocular tissues (such as the corneal endothelium).

The evaluation unit 2562 can be combined with the processing execution unit 256C shown in FIG. 10C. The evaluation unit 2562 of this example is executed by the analysis unit 2561 based on the first area of interest and the second area of interest (first partial data set and second partial data set) set by the area of interest setting unit 2563. A predetermined evaluation process is executed based on the data obtained by the analysis process. The evaluation unit 2562 of this example may be the same as the evaluation unit 2562 of FIG. 10B.

The process execution unit 256D shown in FIG. 10D executes a predetermined analysis process based on at least one of the first partial data set of the first three-dimensional data set and the second partial data set of the second three-dimensional data set. The analysis unit 2561 configured to perform the above, and the region of interest configured to set the partial data (the region of interest that is at least a part of the analysis data) to which the evaluation process is applied to the data obtained by this analysis processing. A predetermined evaluation based on at least one of the setting unit 2563 and the first area of interest set for the analysis data of the first partial data set and the second area of interest set for the analysis data of the second partial data set. Includes an evaluation unit 2562 configured to perform processing. The analysis unit 2561 may be the same as the analysis unit 2561 of FIG. 10A. The evaluation unit 2562 may be the same as the evaluation unit 2562 in FIG. 10B.

The area of interest setting unit 2563 identifies a partial data set by analyzing, for example, a three-dimensional data set, and sets the partial data of the analysis data corresponding to this partial data set in the area of interest. Sub-dataset settings include, for example, segmentation.

In another example, the region of interest setting unit 2563 sets the region of interest by analyzing the analysis data obtained by the analysis unit 2561. As an example, the region of interest setting unit 2563 executes a process of detecting characteristic partial data in the analysis data and a process of setting the region of interest based on the detected partial data.

In yet another example, the control device 170 causes a display device (not shown) to display a 2D map (or any image or analysis data based on a 3D data set). The user specifies a desired area in the displayed 2D map (or image or analysis data) using an operating device (not shown). The area of interest setting unit 2563 can set an area of interest in the analysis data based on the area specified by the user on the displayed two-dimensional map (or image or analysis data).

The processing execution unit 256E shown in FIG. 11 includes an examination area setting unit 2564 configured to execute a setting of a predetermined examination application area (examination area) for the eye 120. The predetermined test may be any test, such as OCT scan, visual field test, microperimetry, electrophysiological test and the like.

For example, the inspection area setting unit 2564 analyzes one or more of the two-dimensional map, the three-dimensional data set, and the data generated based on at least one of them to determine the region of interest of the eye 120. The process and the process of setting the inspection area based on the determined region of interest are executed. Areas of interest are, for example, lesions, specific sites, specific tissues, and the like. Typically, the examination area is set to include at least a portion of the area of interest.

In one example, the inspection area setting unit 2564 focuses on the segmentation of the 2D map (or 3D data set) and the region in the 2D map identified by the segmentation in the eye 120 based on the result of the positioning process. It includes a process of converting to an area and a process of setting an inspection area based on this area of interest.

When the inspection area set by the inspection area setting unit 2564 is an OCT scan application area, information indicating this inspection area can be provided to the scan control unit 260. The scan control unit 260 controls the OCT scanner 270 to apply an OCT scan to this inspection area.

When the inspection area set by the inspection area setting unit 2564 is the OCT scan application area, the information indicating the inspection area can be provided to another OCT apparatus (inspection apparatus 180) via the communication device described above.

When the inspection area set by the inspection area setting unit 2564 is an application area of a certain inspection, information indicating the inspection area can be provided to the inspection device 180 corresponding to the inspection via the above-mentioned communication device. ..

When the inspection area setting unit 2564 processes only one of the first partial data set of the first three-dimensional data set and the second partial data set of the second three-dimensional data set, this processing is performed by the first It may be the same as that of the inspection area setting unit 2064 of the embodiment.

When the inspection area setting unit 2564 processes both the first partial data set of the first three-dimensional data set and the second partial data set of the second three-dimensional data set, the inspection area setting unit 2564 may perform a second operation. A first inspection area based on one partial data set and a second inspection area based on the second partial data set can be set, and these inspection areas can be combined to set a wider inspection area. ..

The processing execution unit 256F shown in FIG. 12 has an imaging area setting unit 2565 configured to set a partial data set (an imaged area that is at least a part of a three-dimensional data set) to which the imaging process is applied. , Includes an image data generator 2566 configured to generate image data for the set imaging area.

The imaging process includes at least the Fourier transform. Examples of the imaging process include general OCT image construction, motion contrast (OCT angiography, etc.), phase image construction, polarized image construction, and the like.

For example, the imaging area setting unit 2565 analyzes one or more of the two-dimensional map, the three-dimensional data set, and the data generated based on at least one of them to determine the region of interest of the eye 120. The process of setting the imaged area and the process of setting the imaged area based on the determined region of interest are executed. Areas of interest are, for example, lesions, specific sites, specific tissues, and the like. Typically, the imaging area is set to include at least a portion of the area of interest.

In one example, the imaging area setting unit 2565 in the eye 120 based on the segmentation of the 2D map (or 3D data set) and the region in the 2D map identified by the segmentation based on the result of the positioning process. It includes a process of converting to a region of interest and a process of setting an imaging area based on the region of interest.

The image data generation unit 2566 is configured to generate image data based on the data collected by the OCT scanner 270. For example, the image data generation unit 2566 forms image data of a tomographic image of the eye 120 based on the output (sampling data, interference signal data) from the OCT scanner 270. This image data generation process includes filtering, a fast Fourier transform (FFT), and the like, as in the conventional (swept source or spectral domain) OCT. By such processing, the reflection intensity profile (reflection intensity profile along the Z direction) in the A line (scan path of the measured light beam in the eye 120) corresponding to each XY position is acquired, and this reflection intensity profile is imaged. The image data of this A line (A scan image data) is formed by the conversion.

Further, the image data generation unit 2566 forms a plurality of A-scan image data according to the mode of OCT scan (deflection of the measurement light beam, movement of the A-scan position), and arranges these A-scan image data in two dimensions. Image data and three-dimensional image data can be constructed.

When a plurality of tomographic image data are obtained by raster scanning or the like, the image data generation unit 2566 embeds these tomographic image data in a single three-dimensional coordinate system to construct stack data, and voxelizes the stack data. Can be applied to construct voxel data (volume data).

The image data generation unit 2566 can render stack data or volume data. The rendering method is arbitrary and may include, for example, volume rendering, multi-section reconstruction (MPR), surface rendering, and the like. Further, the image data generation unit 2566 can construct a plane image (for example, a front image, an ENFACE image) from the stack data or the volume data. For example, the image data generation unit 2566 can construct a projection image by integrating stack data or volume data along each A line.

In this example, the image data generation unit 2566 applies the imaging process to the OCT data set (partial data set of the three-dimensional data set) included in the imaging area set by the imaging area setting unit 2565, and the image data. To generate.

In another example, the image data generation unit 2566 applies an imaging process to a three-dimensional data set to generate image data. Further, the processing execution unit 256F extracts partial image data corresponding to the imaging area set by the imaging area setting unit 2565 from the image data. Extraction of partial image data includes, for example, clipping, cropping, or trimming.

The processing execution unit 256F shown in FIG. 12 has an image construction function (image data generation unit 2566), but the OCT device (ophthalmology device) of another exemplary embodiment does not have to have an image construction function. .. In this case, information indicating the imaging area set by the imaging area setting unit 2565 can be provided to an external device (including an imaging processor) via a communication device (not shown).

When the processing execution unit 256F processes only one of the first partial data set of the first three-dimensional data set and the second partial data set of the second three-dimensional data set, this processing is the first aspect. It may be the same as that of the processing execution unit 206F of the example.

When the processing execution unit 256F processes both the first partial data set of the first three-dimensional data set and the second partial data set of the second three-dimensional data set, the imaging area setting unit 2565 is the first. A first imaging area based on the partial data set of 1 and a second imaging area based on the second partial data set are set, and these imaging areas are combined to set a wider imaging area. can do.

In the example shown in FIG. 13, a reception unit 190 that receives data (examination data) acquired from the eye 120 by a predetermined examination different from OCT is provided. Further, the processing execution unit 256G of this example includes a comparison unit 2567. The comparison unit 2567 is configured to perform a predetermined comparison process between at least a part of the inspection data received by the reception unit 190 and at least a part of the three-dimensional data set collected by the OCT scanner 270. ..

The reception unit 190 receives the examination data obtained from the eye 120 from the outside (for example, an ophthalmic apparatus, an image archiving system, a recording medium). The reception unit 190 may include, for example, a communication device or a drive device.

The test data may be any modality or data obtained by the test. Examples of test data are sensitivity distribution data obtained by visual field test of eye 120, electroretinogram (EGR) obtained by electrophysiological test, and tear distribution data obtained by tear liquor imaging (anterior ocular segment imaging). and so on.

The comparison unit 2567 may, for example, perform the same positioning process as the positioning unit 204, the registration between the two-dimensional map and the inspection data based on the result of the positioning process, and the three-dimensional data set based on the result of this registration. It may be configured to perform registration with the inspection data and a predetermined comparison process based on the registered 3D dataset (at least a portion thereof) and the inspection data.

In this comparison process, for example, a comparison between a three-dimensional data set and inspection data, a comparison between a two-dimensional map based on the three-dimensional data set and inspection data, and a comparison between image data and inspection data based on the three-dimensional data set, Comparison of dimensional map analysis data and inspection data, comparison of image data analysis data and inspection data, comparison of 3D data set and inspection data processing data, 2D map and inspection data based on 3D data set Comparison with the processing data of the two-dimensional map, comparison of the image data based on the three-dimensional data set with the processing data of the inspection data, comparison of the analysis data of the two-dimensional map with the processing data of the inspection data, and the analysis data and inspection of the image data. Any one or more of comparisons of the data with the processed data may be included.

When the comparison unit 2567 processes only one of the first partial data set of the first three-dimensional data set and the second partial data set of the second three-dimensional data set, this processing is performed in the first embodiment. It may be the same as that of the comparison unit 2067 of.

When the comparison unit 2567 processes both the first partial data set of the first three-dimensional data set and the second partial data set of the second three-dimensional data set, the comparison unit 2567 uses the first partial data. It is possible to execute a comparison process based on a set and a comparison process based on a second partial data set, and combine the two results obtained by these comparison processes to obtain a wider range of comparison results.

The processing execution unit 256H shown in FIG. 14 is via registration between the first 2D map based on the 1st 3D data set and the 2nd 2D map based on the 2nd 3D data set. Includes a registration unit 2568 configured to perform registration between the first sub-dataset and the second sub-dataset.

The registration unit 2568 first compares the first 2D map and the 2nd 2D map, and based on the result, the registration between the 1st 2D map and the 2nd 2D map. I do.

Comparison of two 2D maps may include an image correlation operation. One method that can be adopted for this image correlation calculation is described in, for example, Japanese Patent No. 6276943 (International Publication No. 2015/029675). When this method is used, the registration unit 2568 applies a phase-only correlation (POC) to the pair of the first two-dimensional map and the second two-dimensional map to obtain the first one. The amount of displacement between the two-dimensional map and the second two-dimensional map can be obtained. This displacement amount includes, for example, either one or both of the parallel movement amount and the rotational movement amount. Registration is performed to change the relative position of the first 2D map and the 2nd 2D map so as to cancel the obtained displacement amount.

For details of the two-dimensional map comparison method using the phase-limited correlation, refer to Japanese Patent No. 6276943. Further, the applicable two-dimensional map comparison method is not limited to the above examples, and any method within the scope of the invention described in Japanese Patent No. 6276943 or any modification thereof can be applied. Further, in order to compare the two two-dimensional maps, it is also possible to use an arbitrary image correlation method other than the phase-limited correlation method, or an arbitrary image comparison method other than the image correlation method.

The processing execution unit 256I shown in FIG. 15 executes registration between the first 2D map based on the 1st 3D data set and the 2nd 2D map based on the 2nd 3D data set. The registration unit 2569 configured as described above is included.

Similar to the registration unit 2568 of FIG. 14, the registration unit 2569 compares the first two-dimensional map and the second two-dimensional map, and based on the result, the first two-dimensional map and the second two-dimensional map are used. Register with the 2D map. Comparison of two 2D maps includes, for example, an image correlation operation. This image correlation calculation may include a phase-limited correlation calculation. The amount of displacement between the first two-dimensional map and the second two-dimensional map can be obtained by the phase-limited correlation calculation. This displacement amount includes, for example, either one or both of the parallel movement amount and the rotational movement amount. Registration is performed to change the relative position of the first 2D map and the 2nd 2D map so as to cancel the obtained displacement amount. The registration method is not limited to these examples.

The output from the registration unit 2569 (information indicating the amount of displacement between the first two-dimensional map and the second two-dimensional map) is input to the scan control unit 260. The scan control unit 260 adjusts the application area of the OCT scan for the eye 120 based on the information input from the registration unit 2569. For example, the scan control unit 260 adjusts the control signal sent to the two-dimensional optical scanner 116 so as to cancel the displacement amount between the first two-dimensional map and the second two-dimensional map.

Typically, the OCT scanner 270 repeatedly applies an OCT scan to the eye 120 to sequentially collect a three-dimensional data set. The three-dimensional data sets collected sequentially are sequentially (in real time) input to the processing apparatus 160. The map creation unit 252 sequentially (in real time) creates a two-dimensional map from the sequentially input three-dimensional data set. The two-dimensional maps created sequentially are sequentially (in real time) input to the registration unit 2569. The registration unit 2569 applies the above-mentioned registration sequentially (in real time) to the sequentially input two-dimensional map. For example, the registration unit 2569 applies registration to the pair of the nth input two-dimensional map and the nth + th input two-dimensional map (n is a positive integer). As a result, the amount of displacement between the two 2D maps obtained in succession, that is, the position of the eye 120 at the time when the nth 3D data set was collected, and the n + 1th 3D data set. The difference (displacement amount) from the position of the eye 120 at the time when the data was collected is acquired in substantially real time. The displacement amounts acquired sequentially are sequentially (in real time) input to the scan control unit 260. The scan control unit 260 adjusts the control signal to the two-dimensional optical scanner 116 so as to cancel the sequentially input displacement amount. By repeating such a series of real-time processing, adjustment (tracking) of the OCT scan application area according to the movement of the eye 120 is realized.

In the example shown in FIG. 16, the first 3D dataset and the 2nd 3D dataset are collected from different 3D regions of the eye 120. That is, the first 3D data set is collected from the 1st 3D region of the eye 120, and the 2nd 3D data set is from the 2nd 3D region different from the 1st 3D region. Will be collected. Typically, a part of the first three-dimensional region and a part of the second three-dimensional region are common.

The processing execution unit 256J shown in FIG. 16 includes a registration unit 2570, an image data generation unit 2571, and an image composition unit 2572.

The registration unit 2570 is configured to perform registration between the first 2D map based on the 1st 3D data set and the 2D map based on the 2nd 3D data set. ing. The process performed by the registration unit 2570 may be the same as that of the registration unit 2569 in FIG.

The image data generation unit 2571 generates the first image data from the first partial data set of the first three-dimensional data set, and also generates the first image data from the second partial data set of the second three-dimensional data set. Generate image data of. The process executed by the image data generation unit 2571 may be the same as that of the image data generation unit 2066 of FIG.

The image composition unit 2572 is a first generated by the image data generation unit 2571 based on the result of registration between the first two-dimensional map and the second two-dimensional map obtained by the registration unit 2570. The image data of the above and the second image data are combined.

The result of registration between the first 2D map and the 2nd 2D map includes the relative displacement between the 1st 2D map and the 2D 2D map. This relative displacement amount indicates the relative positional relationship between the first two-dimensional map and the second two-dimensional map. The relative positional relationship between the first 2D map and the 2nd 2D map is the relative positional relationship between the 1st 3D data set and the 2nd 3D data set (especially). , Relative positional relationship in the XY plane). The image synthesizing unit 2572 arranges and synthesizes the first image data and the second image data according to this relative positional relationship.

The relative positional relationship in the Z direction between the first 3D data set and the second 3D data set can be determined, for example, by analyzing the first 3D data set and the second 3D data set. Obtained or obtained by analyzing the first image data and the second image data. For example, the registration of the first image data and the second image data can be performed by the registration of the common area between the first image data and the second image data.

In another example, if a third 3D dataset is collected that includes at least a portion of the first 3D dataset and at least a portion of the second 3D dataset, then this third The first 3D data set (1st 2D map, 1st image data, etc.) and the 2nd 3D data via the 3D data set or its processed data (2D map, image data, etc.) Registration with the set (second 2D map, second image data, etc.) can be performed.

The processing device 160 may be capable of performing various data processing other than the data processing described above. The processing device 160 can process the data (OCT data) acquired by using the OCT scan. The OCT data is, for example, interference signal data (eg, at least a portion of a 3D dataset), reflection intensity profile, or image data.

The processing device 160 may be capable of processing data other than OCT data. For example, when the ophthalmic apparatus 150 has a data acquisition device other than the OCT scanner 270, the processing apparatus 160 can process the data acquired by this data acquisition apparatus. The ophthalmic apparatus adopted as the data acquisition apparatus may be an ophthalmologic imaging apparatus (ophthalmic imaging apparatus) such as a fundus camera, a scanning laser ophthalmoscope (SLO), a surgical microscope, and a slit lamp microscope. Other examples include ophthalmic measuring devices such as refractometers, keratometers, tonometers, axial length measuring devices, specular microscopes, wavefront analyzers, perimeters, and the like. Further, when the OCT device is an arbitrary medical device (that is, when the OCT device is a device used in any clinical department), the medical device adopted as the data acquisition device is, for example, an arbitrary medical imaging device. And / or any medical examination device. Further, for the OCT device used in a field other than medical treatment, a data acquisition device suitable for the field can be adopted.

Some examples of the operations that can be performed by the ophthalmologic apparatus 150 having the above-exemplified configuration will be described.

See FIG. In this example, first, the scan control unit 260 controls the OCT scanner 270 so as to apply the OCT scan to the eye 120 to collect the first 3D data set and the second 3D data set (S11). .. More generally, two or more 3D datasets can be collected.

Next, the map creation unit 252 creates a first two-dimensional map based on the representative intensity values of the plurality of A scan data included in the first three-dimensional data set collected in step S11, and creates a first two-dimensional map. , A second two-dimensional map is created based on the representative intensity values of each of the plurality of A scan data included in the second three-dimensional data set collected in step S11 (S12).

Next, the processing execution unit 256 is the first part of the first three-dimensional data set collected in step S11 based on the first two-dimensional map and the second two-dimensional map created in step S12. Performs processing based on at least one of the dataset and the second sub-dataset of the second 3D dataset.

The process of step S13 includes, for example, a predetermined analysis process, a predetermined evaluation process, an area of interest setting, an inspection area setting, an imaging area setting, an image data generation, a predetermined comparison process with the inspection data, and registration. , Tracking, and one or more of panoramic OCT imaging. In other words, the processing execution unit 256 includes an analysis unit 2561, an evaluation unit 2562, an area of interest setting unit 2563, an inspection area setting unit 2564, an imaging area setting unit 2565, an image data generation unit 2566, a comparison unit 2567, and a registration unit 2568. , Any one or more of the registration unit 2569, the registration unit 2570, the image data generation unit 2571, and the image composition unit 2572 may be included.

Some effects of the ophthalmic apparatus (OCT apparatus) 150 of this embodiment will be described.

The ophthalmic apparatus 150 of this embodiment includes an OCT scanner 270, a map creation unit 252, and a processing execution unit 256. The OCT scanner 270 applies an OCT scan to a sample (eye 120) to collect a first 3D data set and a second 3D data set. The map creation unit 252 creates a first two-dimensional map based on the representative intensity values of each of the plurality of A scan data included in the first three-dimensional data set. Further, the map creation unit 252 creates a second two-dimensional map based on the representative intensity values of the plurality of A scan data included in the second three-dimensional data set. The processing execution unit 256 is based on the first two-dimensional map and the second two-dimensional map, and at least the first partial data set of the first three-dimensional data set and at least the second of the second three-dimensional data set. Performs processing based on at least one of the two partial datasets.

According to such an ophthalmic apparatus 150, it is possible to perform processing based on two (or more) three-dimensional data sets by using a two-dimensional map created from the three-dimensional data sets collected by the OCT scan. , Collected from samples without 3D image construction or landmarks as in the inventions described in Patent Document 1 (US Pat. No. 7,884,945) and Patent Document 2 (US Pat. No. 8,405,834). It is possible to execute the processing of the OCT data. Therefore, it is possible to improve the efficiency of resources required for processing and shorten the processing time, and it is possible to improve the efficiency of OCT data processing. Thereby, for example, real-time processing can be preferably performed.

In the ophthalmic apparatus 150 of this embodiment, the processing execution unit 256 uses at least the first partial data set and the second partial data set of the first three-dimensional data set based on the first two-dimensional map and the second two-dimensional map. It may include an analysis unit 2561 configured to perform a predetermined analysis process based on at least one of the at least two sub-datasets of the three-dimensional data set of.

With such a configuration, it is possible to analyze the OCT data collected from the sample without performing the three-dimensional image construction and landmarks as in the prior art. Therefore, it is possible to improve the efficiency of resources required for analysis and shorten the processing time, and it is possible to improve the efficiency of analysis. Thereby, for example, it is possible to preferably perform real-time analysis.

In the ophthalmic apparatus 150 of this embodiment, the processing execution unit 256 executes a predetermined evaluation process based on the analysis data obtained by the analysis unit 2561 based on the first two-dimensional map and the second two-dimensional map. The evaluation unit 2562 configured as described above may be included.

With such a configuration, it is possible to perform an evaluation on a desired region of a sample without performing three-dimensional image construction or landmarks as in the prior art. Therefore, it is possible to improve the efficiency of resources required for evaluation and shorten the processing time, and it is possible to improve the efficiency of evaluation. Thereby, for example, it is possible to preferably perform evaluation in real time.

In the ophthalmic apparatus 150 of this embodiment, the processing execution unit 256 uses the first two-dimensional map and the second two-dimensional map as the first three-dimensional data set and / or the second three-dimensional data set. It may include a region of interest setting unit 2563 configured to set the region of interest (area to be analyzed and / or region to be evaluated).

With such a configuration, it is possible to set an area of interest without performing three-dimensional image construction or landmarks as in the prior art. Therefore, it is possible to improve the efficiency of the resources required for setting the area of interest and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, it is possible to preferably set the region of interest in real time.

In the ophthalmic apparatus 150 of this embodiment, the processing execution unit 256 sets a predetermined inspection application area (examination area) for the sample based on the first two-dimensional map and the second two-dimensional map. It may include a configured inspection area setting unit 2564.

According to such a configuration, it is possible to set the inspection area without performing the three-dimensional image construction and landmarks as in the prior art. Therefore, it is possible to improve the efficiency of the resources required for setting the inspection area and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, it is possible to preferably set the inspection area in real time.

In the ophthalmic apparatus 150 of this embodiment, the processing execution unit 256 is a partial data set (imaging area) to which a predetermined imaging process is applied based on the first two-dimensional map and the second two-dimensional map. It may include an imaging area setting unit 2565 configured to perform the setting.

With such a configuration, it is possible to set an imaging area without performing three-dimensional image construction or landmarks as in the prior art. Therefore, it is possible to improve the efficiency of the resources required for setting the imaging area and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, it is possible to preferably set the imaging area in real time.

The ophthalmic apparatus 150 of this embodiment may include means for preparing examination data obtained from a sample by a predetermined examination different from OCT. The inspection data preparation means includes, for example, a means for receiving inspection data (reception unit 190) or a means for applying inspection to a sample and acquiring inspection data. Further, the process execution unit 256 is configured to execute a predetermined comparison process between the inspection data and at least a part of the three-dimensional data set based on the first two-dimensional map and the second two-dimensional map. The comparison unit 2567 may be included.

According to such a configuration, it is possible to set the comparison process between the inspection data and the OCT data without performing the three-dimensional image construction and the landmark as in the prior art. Therefore, it is possible to improve the efficiency of the resources required for the comparison processing and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, it is possible to preferably perform real-time comparison processing.

In the ophthalmic apparatus 150 of this embodiment, the processing execution unit 256 uses at least a first partial data set and at least a second portion via registration between the first two-dimensional map and the second two-dimensional map. It may include a registration unit 2568 that performs registration with the dataset.

With such a configuration, it is possible to perform registration between two (or more) OCT data without performing three-dimensional image construction or landmarks as in the prior art. Therefore, it is possible to improve the efficiency of the resources required for registration and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, real-time registration can be preferably performed.

In the ophthalmic apparatus 150 of this embodiment, the processing execution unit 256 adjusts the application area of the OCT scan on the sample via the registration between the first two-dimensional map and the second two-dimensional map. The registration unit 2569 of the above may be included.

Further, in the ophthalmic apparatus 150 of this embodiment, the processing execution unit 256 (registration unit 2569) sequentially processes the three-dimensional data set sequentially collected from the sample, and sequentially adjusts the application area of the OCT scan for the sample. By doing so, tracking can be performed according to the movement of the sample.

With such a configuration, it is possible to perform tracking according to the movement of the sample without performing three-dimensional image construction and landmarks as in the prior art. Therefore, it is possible to improve the efficiency of the resources required for tracking and shorten the processing time, and it is possible to improve the efficiency. As a result, real-time tracking can be suitably performed.

In the process executed by the process execution unit 256 of the ophthalmic apparatus 150 of this embodiment, the registration (comparison of the two-dimensional maps) between the first two-dimensional map and the second two-dimensional map includes an image correlation calculation. You can go out. This image correlation calculation may obtain the amount of displacement between the first two-dimensional map and the second two-dimensional map, and based on this amount of displacement, the first two-dimensional map and the second two-dimensional map Registration with may be performed. This displacement amount may include at least one of a parallel movement amount and a rotational movement amount.

With such a configuration, the relative positional relationship between two 2D maps by image correlation (typically phase-limited correlation) without the intervention of many resource-intensive processes such as landmark detection. Can be obtained efficiently.

The first three-dimensional data set and the second three-dimensional data set collected by the ophthalmologic apparatus 150 of this embodiment may be collected from different three-dimensional regions of the sample. In this case, the processing execution unit 256 has at least the first image data generated from the first partial data set and at least the first image data via the registration between the first two-dimensional map and the second two-dimensional map. Combining with the second image data generated from the second partial data set may be performed.

According to such a configuration, it is possible to efficiently synthesize a plurality of image data corresponding to a plurality of different regions of a sample without going through a process requiring many resources such as landmark detection.

As described above, the sample of this embodiment is a living eye, but the same function and configuration can be applied to an OCT device for a sample other than the living eye. That is, it is possible to combine any matter (function, hardware configuration, software configuration, etc.) relating to the ophthalmic apparatus 150 with the OCT apparatus of any aspect. At this time, the action and effect according to the combined items are exhibited.

Some exemplary embodiments relate to methods of controlling an OCT apparatus including an OCT scanner and a processor that apply an OCT scan to a sample. This control method may include at least the following steps: controlling the OCT scanner to collect a first 3D data set and a 2nd 3D data set from the sample; 1D data Steps to create a first 2D map based on the representative intensity values of each of the plurality of A scan data contained in the set; each representative intensity value of the plurality of A scan data included in the second 3D data set. Controlling the processor to create a second 2D map based on; at least the first of the first 3D dataset based on the first 2D map and the second 2D map. A step of controlling a processor to perform processing based on at least one of a partial data set and at least one of the second 3D data sets.

It is possible to combine any matter (function, hardware configuration, software configuration, etc.) relating to the ophthalmic apparatus 150 with the control method of this embodiment. At this time, the action and effect according to the combined items are exhibited.

Some exemplary embodiments relate to a program that causes a computer to perform such a method of controlling an OCT apparatus. It is possible to combine this program with any of the items described with respect to the ophthalmic apparatus 150. Also, some exemplary embodiments relate to computer-readable non-temporary recording media on which such programs are recorded. It is possible to combine this recording medium with any of the items described with respect to the ophthalmic apparatus 150.

Some exemplary embodiments relate to devices (OCT data processing devices) that process data collected using OCT. The OCT data processor may include at least the following elements: a reception unit that receives a first 3D data set and a 2nd 3D data set collected by applying an OCT scan to a sample; A first two-dimensional map is created based on the representative intensity value of each of the plurality of A scan data included in the three-dimensional data set of the above, and a plurality of A scan data included in the second three-dimensional data set. A map creation unit that creates a second two-dimensional map based on each representative intensity value; at least the first of the first three-dimensional data sets based on the first two-dimensional map and the second two-dimensional map. A processing execution unit that executes processing based on at least one of the partial data set and at least the second partial data set of the second three-dimensional data set.

That is, this OCT data processing device replaces (or in addition to) the OCT scanner 270 of the OCT device (ophthalmic device) 150 described above, and externally (for example, OCT) the three-dimensional data set obtained by the OCT scan. It includes an element (reception unit) that receives from (device, image archiving system, recording medium). The reception unit may include, for example, a communication device or a drive device.

It is possible to combine any matter (function, hardware configuration, software configuration, etc.) relating to the ophthalmic apparatus 150 with the OCT data processing apparatus of this embodiment. At this time, the action and effect according to the combined items are exhibited.

Some exemplary embodiments relate to methods of controlling an OCT data processor, including a processor. This control method may include at least the following steps: controlling the processor to accept a first 3D data set and a second 3D data set collected by applying an OCT scan to a sample. Steps to create a first 2D map based on the representative intensity values of each of the plurality of A scan data contained in the 1st 3D data set; Multiple A scans contained in the 2nd 3D data set The step of controlling the processor to create a second 2D map based on each representative intensity value of the data; the 1D 3D based on the 1st 2D map and the 2nd 2D map. A step of controlling a processor to perform processing based on at least one of at least one of the first sub-dataset of the dataset and at least the second sub-dataset of the second three-dimensional dataset.

It is possible to combine any matter (function, hardware configuration, software configuration, etc.) relating to the ophthalmic apparatus 150 with the control method of this embodiment. At this time, the action and effect according to the combined items are exhibited.

Some exemplary embodiments relate to a program that causes a computer to perform such a control method for an OCT data processor. It is possible to combine this program with any of the items described with respect to the ophthalmic apparatus 150. Also, some exemplary embodiments relate to computer-readable non-temporary recording media on which such programs are recorded. It is possible to combine this recording medium with any of the items described with respect to the ophthalmic apparatus 150.

Some exemplary embodiments of the OCT apparatus (eg, ophthalmic apparatus 150), some exemplary embodiments of the OCT apparatus control method, some exemplary embodiments of the OCT data processing apparatus, or some. The control method of the OCT data processing apparatus of the exemplary embodiment provides a method of processing OCT data. This OCT data processing method may include at least the following steps: preparing a first 3D data set and a 2nd 3D data set collected from a sample; to the 1st 3D data set. Steps to create a first two-dimensional map based on the representative intensity values of each of the plurality of A scan data contained; based on the representative intensity values of each of the plurality of A scan data contained in the second three-dimensional data set. To create a second 2D map; at least the 1st partial data set and the 2nd 3 of the 1st 3D dataset based on the 1st 2D map and the 2nd 2D map. A step of performing processing based on at least one of the at least second sub-datasets of a dimensional dataset.

It is possible to combine any matter (function, hardware configuration, software configuration, etc.) relating to the ophthalmic apparatus 150 with the OCT data processing method of this embodiment. At this time, the action and effect according to the combined items are exhibited.

Some exemplary embodiments relate to programs that cause a computer to perform such an OCT data processing method. It is possible to combine this program with any of the items described with respect to the ophthalmic apparatus 150. Also, some exemplary embodiments relate to computer-readable non-temporary recording media on which such programs are recorded. It is possible to combine this recording medium with any of the items described with respect to the ophthalmic apparatus 150.

In some embodiments, the non-temporary recording medium on which the program is recorded may be in any form, and examples thereof include magnetic disks, optical disks, magneto-optical disks, and semiconductor memories.

Some of the exemplary embodiments described above are merely exemplary embodiments of the present invention. Therefore, it is possible to make arbitrary modifications (omission, replacement, addition, etc.) within the scope of the gist of the present invention.

100, 150 Ophthalmic equipment (OCT equipment)
124, 160 Processing device 126, 170 Control device 202, 252 Map creation unit 204 Positioning unit 206, 256 Processing execution unit 210, 260 Scan control unit 220, 270 OCT scanner

Claims (29)

A method of applying an optical coherence tomography (OCT) scan to a sample to process the collected data.
Prepare the 3D dataset collected from the sample and
A two-dimensional map is created based on the representative intensity values of each of the plurality of A scan data included in the three-dimensional data set.
Positioning of the 3D data set based on the 2D map
Perform processing based on at least a partial dataset of the positioned three-dimensional dataset.
OCT data processing method. The process includes a predetermined analysis process.
The OCT data processing method according to claim 1. The process includes a predetermined evaluation process based on the data obtained by the analysis process.
The OCT data processing method according to claim 2. The process includes setting a partial dataset to which the analysis process applies.
The OCT data processing method according to claim 2 or 3. The process includes setting an area of application for a predetermined test on the sample.
The OCT data processing method according to any one of claims 1 to 4. The process includes setting a partial dataset to which a predetermined imaging process is applied.
The OCT data processing method according to any one of claims 1 to 5. Prepare the test data obtained from the sample by a predetermined test different from OCT,
The process includes a predetermined comparison process between the inspection data and at least a part of the three-dimensional data set.
The OCT data processing method according to any one of claims 1 to 6. A method of applying an optical coherence tomography (OCT) scan to a sample to process the collected data.
Prepare the first 3D dataset and the second 3D dataset collected from the sample.
A first two-dimensional map is created based on the representative intensity values of each of the plurality of A scan data included in the first three-dimensional data set.
A second two-dimensional map is created based on the representative intensity values of each of the plurality of A scan data included in the second three-dimensional data set.
Based on the first two-dimensional map and the second two-dimensional map, at least the first partial data set of the first three-dimensional data set and at least the second of the second three-dimensional data set. Perform processing based on at least one of the partial datasets,
OCT data processing method. The process is between the at least the first partial data set and the at least the second partial data set via registration between the first two-dimensional map and the second two-dimensional map. Including registration,
The OCT data processing method according to claim 8. The process includes adjusting the area of application of the OCT scan to the sample via registration between the first 2D map and the 2D map.
The OCT data processing method according to claim 8 or 9. The adjustment is sequentially performed by sequentially processing the three-dimensional data set sequentially collected from the sample.
The OCT data processing method according to claim 10. The OCT data processing method according to any one of claims 9 to 11, wherein the registration between the first two-dimensional map and the second two-dimensional map includes an image correlation calculation. In the image correlation calculation, the amount of displacement between the first two-dimensional map and the second two-dimensional map is obtained.
The registration between the first two-dimensional map and the second two-dimensional map is performed based on the displacement amount.
The OCT data processing method according to claim 12. The displacement amount includes at least one of a parallel movement amount and a rotational movement amount.
The OCT data processing method according to claim 13. The first 3D data set and the 2nd 3D data set are collected from different 3D regions of the sample.
The process involves the first image data and at least the first image data generated from the at least the first partial data set via registration between the first two-dimensional map and the second two-dimensional map. Includes compositing with a second image data generated from a partial dataset of 2
The OCT data processing method according to any one of claims 8 to 14. The process includes a predetermined analysis process.
The OCT data processing method according to any one of claims 8 to 15. The process includes a predetermined evaluation process based on the data obtained by the analysis process.
The OCT data processing method according to claim 16. The process includes setting a partial dataset to which the analysis process applies.
The OCT data processing method according to claim 16 or 17. A plurality of 3D data sets corresponding to a plurality of different time points, including the first 3D data set and the second 3D data set, are prepared.
The analysis process includes a process of obtaining a time-series change of a predetermined parameter value.
The OCT data processing method according to any one of claims 16 to 18. An OCT scanner that applies an optical coherence tomography (OCT) scan to a sample to collect a three-dimensional dataset,
A map creation unit that creates a two-dimensional map based on the representative intensity values of each of the plurality of A scan data included in the three-dimensional data set.
A positioning unit that positions the three-dimensional data set based on the two-dimensional map, and a positioning unit.
An OCT apparatus including a processing execution unit that executes processing based on at least a partial data set of the three-dimensional data set in which the positioning has been performed. An OCT scanner that applies an optical coherence tomography (OCT) scan to a sample to collect a first 3D and a second 3D data set.
A first two-dimensional map is created based on the representative intensity values of the plurality of A scan data included in the first three-dimensional data set, and a plurality of A scan data included in the second three-dimensional data set. A map creation unit that creates a second 2D map based on each representative intensity value of scan data,
Based on the first two-dimensional map and the second two-dimensional map, at least the first partial data set of the first three-dimensional data set and at least the second of the second three-dimensional data set. An OCT apparatus that includes a process execution unit that executes processing based on at least one of a partial data set. A method of controlling an OCT device that includes an OCT scanner and processor that applies an optical coherence tomography (OCT) scan to a sample.
The OCT scanner was controlled to collect a 3D dataset from the sample.
The processor is controlled to create a two-dimensional map based on the representative intensity value of each of the plurality of A scan data included in the three-dimensional data set.
Based on the 2D map, the processor is controlled to position the 3D data set.
Control the processor to perform processing based on at least a partial dataset of the positioned three-dimensional dataset.
Control method of OCT device. A method of controlling an OCT device that includes an OCT scanner and a processor that applies an optical coherence tomography (OCT) scan to a sample.
The OCT scanner was controlled to collect a first 3D data set and a second 3D data set from the sample.
A first two-dimensional map is created based on the representative intensity values of the plurality of A scan data included in the first three-dimensional data set, and a plurality of A scan data included in the second three-dimensional data set. The processor is controlled to create a second 2D map based on each representative intensity value of the A scan data.
Based on the first two-dimensional map and the second two-dimensional map, at least the first partial data set of the first three-dimensional data set and at least the second of the second three-dimensional data set. Control the processor to perform processing based on at least one of the partial datasets.
Control method of OCT device. A reception unit that accepts 3D data sets collected by applying optical coherence tomography (OCT) scans to samples,
A map creation unit that creates a two-dimensional map based on the representative intensity values of each of the plurality of A scan data included in the three-dimensional data set.
A positioning unit that positions the three-dimensional data set based on the two-dimensional map, and a positioning unit.
An OCT data processing apparatus including a processing execution unit that executes processing based on at least a partial data set of the three-dimensional data set that has been positioned. A reception unit that accepts the first and second 3D data sets collected by applying an optical coherence tomography (OCT) scan to the sample.
A first two-dimensional map is created based on the representative intensity values of the plurality of A scan data included in the first three-dimensional data set, and a plurality of A scan data included in the second three-dimensional data set. A map creation unit that creates a second 2D map based on each representative intensity value of scan data,
Based on the first two-dimensional map and the second two-dimensional map, at least the first partial data set of the first three-dimensional data set and at least the second of the second three-dimensional data set. An OCT data processor that includes a process performer that performs processing based on at least one of the partial datasets. A method of controlling an optical coherence tomography (OCT) data processor, including a processor.
The processor was controlled to apply an OCT scan to the sample and accept the collected 3D dataset.
The processor is controlled to create a two-dimensional map based on the representative intensity value of each of the plurality of A scan data included in the three-dimensional data set.
Based on the 2D map, the processor is controlled to position the 3D data set.
Control the processor to perform processing based on at least a partial dataset of the positioned three-dimensional dataset.
Control method of OCT data processing device. A method of controlling an optical coherence tomography (OCT) data processor, including a processor.
The processor is controlled to accept the first and second 3D data sets collected by applying an optical coherence tomography (OCT) scan to the sample.
A first two-dimensional map is created based on the representative intensity values of the plurality of A scan data included in the first three-dimensional data set, and a plurality of A scan data included in the second three-dimensional data set. The processor is controlled to create a second 2D map based on each representative intensity value of the A scan data.
Based on the first two-dimensional map and the second two-dimensional map, at least the first partial data set of the first three-dimensional data set and at least the second of the second three-dimensional data set. Control the processor to perform processing based on at least one of the partial datasets.
Control method of OCT data processing device. A program that causes a computer to execute any of the methods of claims 1 to 19, 22, 23, 26, and 27. A computer-readable non-temporary recording medium on which the program of claim 28 is recorded.

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