JP6922151B2 - Ophthalmology analyzer, ophthalmology analysis program - Google Patents

Description

The present disclosure relates to an ophthalmology analysis device and an ophthalmology analysis program for analyzing eye data to be inspected including vascular information of the eye to be inspected.

In recent years, a technique for obtaining motion contrast data of an eye to be inspected by using an OCT technique has attracted attention (see Non-Patent Document 1).

Roberto Reif et al. “Quantifying Optical Microangiography I mages Obtained from a Spectral Domain Optical Coherence Tomography System”, International Journal of Biomedical Imaging, Vol.2012, Article ID 509783, p.11

However, although various improvements have been made to the imaging of motion contrast data, there may be room for improvement in various aspects regarding the analysis relationship.

For example, the state of blood vessels is visualized by motion contrast data, but it is not easy to grasp the state of blood vessels only by observing the motion contrast data.

In view of at least one of the problems of the prior art, it is a technical subject of the present disclosure to provide an ophthalmic analysis apparatus and an ophthalmic analysis program capable of suitably performing blood vessel analysis using OCT motion contrast data.

In order to solve the above problems, the present invention is characterized by having the following configurations.

(1)
An ophthalmic analyzer for analyzing OCT motion contrast data including the blood vessel region of the eye to be inspected.
A analysis means for analyzing the OCT motion contrast data, as arteriovenous information on the blood vessel region included in the OCT motion contrast data, at least one blood vessel included in the OCT motion contrast data is arterial Discrimination information for discriminating whether it is a blood vessel or a blood vessel, which is data different from the OCT motion contrast data, acquired by OCT data acquired by an optical interference tomography or by an imaging means different from the optical interference tomometer. An analysis processing means for acquiring discrimination information based on the obtained image data and assigning whether the blood vessel region of the OCT motion contrast data is an artery or a vein based on the acquired discrimination information.
Based on the acquired discrimination information, the OCT motion contrast data is subjected to image processing, and the OCT motion contrast data including whether it is an artery or a vein with respect to the blood vessel region is displayed on the display unit. Control means and
Ophthalmic analysis apparatus, characterized in that it comprises a.
(2)
An ophthalmic analysis program for operating a computer as various processing means of the ophthalmic analysis apparatus according to (1).

Hereinafter, the ophthalmic analyzer of this embodiment will be described with reference to the drawings. In the following, the OCT motion contrast data analysis device will be described as an example of the ophthalmology analysis device. The OCT motion contrast data analysis device (hereinafter, OCT analysis device) 1 shown in FIG. 1 analyzes the motion contrast data acquired by the OCT device 10. The OCT motion contrast data includes, for example, blood vessel information of the eye to be inspected.

The OCT analysis device 1 includes, for example, a control unit 70. The control unit 70 is realized by, for example, a general CPU (Central Processing Unit) 71, a ROM 72, a RAM 73, or the like. The ROM 72 stores, for example, an analysis processing program for processing motion contrast data, a program for controlling the operation of the OCT device 10 to obtain motion contrast data, initial values, and the like. The RAM 73 temporarily stores various types of information, for example.

As shown in FIG. 1, the control unit 70 is electrically connected to, for example, a storage unit (for example, a non-volatile memory) 74, an operation unit 76, a display unit 75, and the like. The storage unit 74 is, for example, a non-transient storage medium capable of holding the stored contents even when the power supply is cut off. For example, a hard disk drive, a flash ROM, a detachable USB memory, or the like can be used as the storage unit 74.

Various operation instructions by the inspector are input to the operation unit 76. The operation unit 76 outputs a signal corresponding to the input operation instruction to the CPU 71. For the operation unit 76, for example, at least one user interface such as a mouse, a joystick, a keyboard, and a touch panel may be used.

The display unit 75 may be a display mounted on the main body of the device 1 or a display connected to the main body. For example, a display of a personal computer (hereinafter referred to as "PC") may be used. The display unit 75 displays, for example, OCT data, motion contrast data, and the like acquired by the OCT device 10.

An OCT device 10 is connected to the OCT analysis device 1 of this embodiment, for example. The OCT analysis device 1 may have an integrated configuration housed in the same housing as the OCT device 10, or may have a separate configuration. The control unit 70 may acquire motion contrast data from the connected OCT device 10. The control unit 70 may acquire the motion contrast data acquired by the OCT device 10 via the storage medium.

<OCT device>
Hereinafter, the outline of the OCT device 10 will be described with reference to FIG. For example, the OCT device 10 irradiates the eye E to be inspected with the measurement light, and acquires the OCT signal acquired by the reflected light and the reference light. The OCT device 10 mainly includes, for example, an OCT optical system 100.

<OCT optical system>
The OCT optical system 100 irradiates the eye E to be inspected with the measurement light, and detects an interference signal between the reflected light and the reference light. The OCT optical system 100 mainly includes, for example, a measurement light source 102, a coupler (optical divider) 104, a measurement optical system 106, a reference optical system 110, a detector 120, and the like. For the detailed configuration of the OCT optical system, refer to, for example, Japanese Patent Application Laid-Open No. 2015-131107.

The OCT optical system 100 is an optical system of a so-called optical coherence tomography (OCT). The OCT optical system 100 divides the light emitted from the measurement light source 102 into the measurement light (sample light) and the reference light by the coupler 104. The divided measurement light is guided to the measurement optical system 106, and the reference light is guided to the reference optical system 110. The measurement light is guided to the fundus Ef of the eye E to be inspected via the measurement optical system 106. After that, the detector 120 receives the interference light obtained by combining the measurement light reflected by the eye E to be inspected with the reference light.

The measurement optical system 106 includes, for example, a scanning unit (for example, an optical scanner) 108. The scanning unit 108 may be provided, for example, to scan the measurement light on the fundus in the XY direction (transverse direction). For example, the CPU 71 controls the operation of the scanning unit 108 based on the set scanning position information, and acquires the OCT signal based on the received light signal detected by the detector 120. The reference optical system 110 generates a reference light that is combined with the reflected light acquired by the reflection of the measurement light at the fundus Ef. The reference optical system 110 may be of the Michaelson type or the Machzenda type.

The detector 120 detects an interference state between the measurement light and the reference light. In the case of the Fourier domain OCT, the spectral intensity of the interference light is detected by the detector 120, and the depth profile (A scan signal) in a predetermined range is acquired by the Fourier transform on the spectral intensity data.

As the OCT device 10, for example, Spectral-domain OCT (SD-OCT), Swept-source OCT (SS-OCT), Time-domain OCT (TD-OCT) and the like may be used.

<Front photography optical system>
The frontal imaging optical system 200, for example, photographs the fundus Ef of the eye E to be inspected from the front direction (for example, the optical axis direction of the measurement light) to obtain a frontal image of the fundus Ef. The frontal photographing optical system 200 may have, for example, a device configuration of a scanning laser ophthalmoscope (SLO) (see, for example, Japanese Patent Application Laid-Open No. 2015-666242), or may have a so-called fundus camera type configuration. (See Japanese Patent Application Laid-Open No. 2011-10944). As the frontal photographing optical system 200, the OCT optical system 100 may also be used, or a frontal image may be acquired based on the detection signal from the detector 120.

<Fixed target projection unit>
The fixation target projection unit 300 has an optical system for guiding the line-of-sight direction of the eye E. The projection unit 300 has a fixation target presented to the eye E and can guide the eye E. For example, the fixation target projection unit 300 has a visible light source that emits visible light, and changes the presentation position of the fixation target two-dimensionally. As a result, the line-of-sight direction is changed, and as a result, the acquisition site of OCT data is changed.

<Acquisition of motion contrast data>
The OCT analysis device 1 of this embodiment may, for example, process the OCT data detected by the OCT device 10 to acquire motion contrast data. The CPU 71 controls the driving of the scanning unit 108 to scan the measurement light in the region A1 on the fundus Ef. In FIG. 3A, the direction of the z-axis is the direction of the optical axis of the measurement light. The direction of the x-axis is perpendicular to the z-axis and is the left-right direction of the subject. The direction of the y-axis is perpendicular to the z-axis and is the vertical direction of the subject.

For example, the CPU 71 scans the measurement light in the x direction along the scanning lines SL1, SL2, ..., SLn in the area A1. Scanning the measurement light in a direction intersecting the optical axis direction of the measurement light (for example, the x direction) is called "B scan". Then, the two-dimensional OCT data obtained by one B scan will be described as one frame of two-dimensional OCT data. The CPU 71 may, for example, scan the measurement light two-dimensionally in the xy direction to obtain an A scan signal in the z direction at each scanning position.

The CPU 71 may acquire motion contrast data based on the OCT data. The motion contrast may be, for example, information that captures the blood flow of the eye to be inspected, changes in the retinal tissue, and the like. When acquiring motion contrast data, the CPU 71 acquires at least two OCT data that are temporally different with respect to the same position of the eye to be inspected. For example, in each scanning line, the CPU 71 performs a plurality of B scans at different times and acquires a plurality of OCT data at different times.

For example, FIG. 3B shows an OCT signal acquired when a plurality of B scans having different times are performed on the scanning lines SL1, SL2, ..., SLn. For example, FIG. 3B scans the scanning line SL1 at time T11, T12, ..., T1N, scans scanning line SL2 at time T21, T22, ..., T2N, and scans scanning line SLn for time. The case where scanning is performed with Tn1, Tn2, ..., TnN is shown. For example, the CPU 71 acquires a plurality of OCT data having different times in each scanning line, and stores the OCT data in the storage unit 74.

As described above, when the CPU 71 acquires a plurality of OCT data that are temporally different with respect to the same position, the CPU 71 processes the OCT data to acquire the motion contrast data. Examples of the OCT data calculation method for acquiring motion contrast include a method of calculating the intensity difference or amplitude difference of complex OCT data, and a method of calculating the intensity or amplitude variance or standard deviation of complex OCT data (Speckle variance). ), A method of calculating the phase difference or dispersion of the complex OCT data, a method of calculating the vector difference of the complex OCT data, and a method of multiplying the phase difference and the vector difference of the complex OCT signal. As one of the calculation methods, refer to, for example, Japanese Patent Application Laid-Open No. 2015-131107.

The CPU 71 may acquire the three-dimensional motion contrast data of the eye E to be inspected by arranging the motion contrast data on different scanning lines. As described above, as the motion contrast data, not only the phase difference but also the intensity difference, the vector difference, and the like may be acquired.

<Analysis processing of motion contrast data>
An example of the analysis processing of the motion contrast data acquired as described above will be described below.

The CPU 71 may acquire at least one analysis result by setting an analysis area for the motion contrast data and performing analysis processing on the set analysis area. In this case, the CPU 71 may set the analysis area for the motion contrast data with reference to the position information of the analysis chart on the second image data which is the image data different from the motion contrast data.

Hereinafter, as an example of the analysis result, a case where the blood vessel region of the eye to be inspected is extracted by the analysis processing for the motion contrast data will be described. In this case, a blood vessel analysis region may be set as an analysis region for the motion contrast data, and analysis processing for extracting the blood vessel region may be performed at least in the blood vessel analysis region.

FIG. 4 is a diagram showing an example of an analysis screen when extracting a blood vessel region. For example, the CPU 71 may display the motion contrast display area (hereinafter, MC display area) 400 and the second image display area 500 on the display screen of the display unit 75 on the analysis screen. In this case, the MC display area 400 and the second image display area 500 may be displayed at the same time, or may be displayed at different timings.

The MC display area 400 is an area for displaying motion contrast data (hereinafter, MC data) 402. For example, as MC data 402, as shown in FIG. 4, front MC data (also referred to as face MC data). May be displayed. The frontal MC data may be obtained, for example, by extracting three-dimensional MC data for at least a part of the region in the depth direction (see, for example, Japanese Patent Application Laid-Open No. 2015-121574). For example, the front MC data may be generated by the integrated value or the maximum value in the depth direction in the MC data. Of course, as the MC data 402, one-dimensional MC data, two-dimensional MC data, and three-dimensional MC data may be displayed.

The CPU 71 may display a display (for example, graphic 404) indicating the blood vessel analysis region on the MC data 402 on the MC data 402. The display showing the blood vessel analysis area may be a frame display showing the outer edge of the blood vessel analysis area as shown in graphic 404 of FIG. 4, or a color-coded display of the blood vessel analysis area and the non-blood vessel analysis area. May be good.

The second image display area 500 may be an area for displaying the second image data 502, which is image data different from the MC data 402. In the second image display area, for example, at least one of a front image and an analysis map may be displayed. As the second image data 502, image data that at least partially overlaps with the MC data 402 is used for the acquisition site. For example, when the MC data 402 is data centered on the macula, the second image data 502 regarding the macula is displayed, and when the MC data 402 is data centered on the papilla, the second image data 502 regarding the papilla is displayed. May be displayed.

The analysis map may be, for example, a map showing a two-dimensional distribution of measurement results relating to the fundus. In this case, for example, the color map may be color-coded according to the measured value. The analysis map includes, for example, a thickness map showing the layer thickness, a comparison map showing the comparison result between the layer thickness of the eye to be examined and the layer thickness of the normal eye stored in the normal eye database, and the layer thickness of the eye to be examined and the normal eye database. It may be a deviation map showing the deviation from the layer thickness of the normal eye stored in the standard deviation, or an examination date comparison thickness difference map showing the difference in thickness from each examination date. When determining the layer thickness, for example, the OCT data is divided into layers by image processing (for example, segmentation processing) on the OCT data, and the thickness of each layer is measured based on the interval between the layer boundaries. Of course, it is not limited to the layer thickness, and the analysis map is not limited to the layer thickness, and may be, for example, a map showing the curvature distribution of the fundus.

The front image may be, for example, a front image taken by the front photographing optical system 200, or may be front OCT data (also referred to as face OCT data) generated from OCT three-dimensional data. In the case of 3D OCT data, it may be 3D OCT data that is the basis of 3D motion contrast data. The analysis map may be, for example, a color map that two-dimensionally expresses the analysis result on the eye to be inspected (for example, the thickness and curvature of the fundus layer). In FIG. 4, as the second image data 502, an image in which the analysis map is superimposed on the front image is displayed.

<Analysis chart>
The CPU 71 may superimpose and display the analysis chart 504 on the second image data 502. In this case, the second image data 502 may be associated with the measurement result based on the analysis result for the three-dimensional OCT data (for example, the measurement data of the analysis map) in advance (registration), and the analysis chart 504 is set. The measurement result corresponding to the area is output. In this case, the second image data 502 may be associated with the three-dimensional OCT data, the analysis process may be executed for the region in which the analysis chart is set, or the measurement result may be output based on the analysis result. In this case, the three-dimensional OCT data is preferably the three-dimensional OCT data that is the basis of the MC data. This is because the positional correspondence (registration) between the MC data and the second image data is easy and accurate.

For example, the analysis chart 504 may be an analysis chart for measuring the basic statistic of the measurement result in the preset section, or the basic statistic in the section may be measured. The sections forming the analysis chart 504 may be one region or a plurality of sections. In the case of a plurality of sections, the basic statistic may be measured for each divided section. The basic statistic may be a representative value (mean value, median value, mode value, maximum value, minimum value, etc.), dispersal degree (variance, standard deviation, coefficient of variation), or the like.

For example, the analysis chart 504 may be a chart for obtaining the average for each region with respect to the two-dimensional distribution of the layer thickness of the fundus. Further, the analysis chart 504 may be provided with a numerical display area for displaying the layer thickness in a predetermined area numerically. Instead of the numerical display, color coding may be performed for each section according to the measurement result. The layer thickness data may be the sum of each layer or the thickness of a certain layer (for example, the optic nerve fiber layer).

The analysis chart 504 can be arbitrarily selected, and when the thickness map is a macula map, the analysis chart 504 is used, for example, the examiner is divided into three parts in the radial direction and vertically and horizontally. It can be selected from (divided chart), S / I chart, and ETDRS. When the thickness map is a papillary map, the analysis chart can be selected from, for example, an overall chart, an upper and lower chart (2 divisions), a TSNIT (Temporal Superior Nasal Inferior Temporal) chart (4 divisions), and a LockHour chart (12 divisions). be.

The CPU 71 may receive the operation signal from the operation unit 76 and change the display position of the analysis chart 504 on the second image data 502. As a result, the analysis area according to the analysis chart 504 on the second image data 502 is changed. The CPU 71 may obtain the analysis result in the changed analysis area in conjunction with the change of the display position of the analysis chart 504. A part of the analysis chart may extend beyond the second image data 502.

For example, the examiner moves the analysis chart 504 using the operation unit 76 to move the center 504c of the analysis chart to the reference site of the fundus (for example, the center of the fovea (see M in FIG. 4), the center of the optic nerve head). , Abnormal part). Thereby, for example, the measurement result by the analysis chart 504 can be obtained centering on the reference portion of the fundus. For example, the average layer thickness of the entire chart centered on the reference part, the layer thickness of the reference part, the average layer thickness in a predetermined area centered on the reference part (for example, 1, 2, 3 mm), etc. are measured. May be good. As a result, measurement results centered on the reference site on the fundus can be obtained, which is clinically useful.

<Linkage between analysis chart and blood vessel analysis area>
For example, the CPU 71 may extract the blood vessel region on the MC data by using the position information of the analysis chart 504. More specifically, the CPU 71 may move the position of the blood vessel analysis region on the MC data in conjunction with the movement of the analysis chart 504. In this case, the CPU 71 may move the display (for example, graphic 404) indicating the blood vessel analysis region on the MC data 402. That is, the position of the blood vessel analysis region changes in conjunction with the movement of the analysis region according to the analysis chart 504.

As a result of the movement of the blood vessel analysis area, the blood vessel analysis area on the MC data is changed. Therefore, the CPU 71 may obtain the analysis result in the changed blood vessel analysis region in conjunction with the change in the blood vessel analysis region (the method of blood vessel analysis will be described later).

When linking the analysis chart and the blood vessel analysis region, the CPU 71 has the center 504c of the analysis chart 504 in the second image data 502 and the center 404c of the blood vessel analysis region in the MC data 402 at the same position in the analysis. The vascular analysis area may be moved so that it is located. That is, the CPU 71 may set the center of the analysis region at a position corresponding to the center position of the analysis chart 504 in the second image data 502 in the MC data 402. In this case, it is preferable that the second image data 502 and the MC data 402 are positionally associated (registered).

Here, for example, when the examiner sets the center 504c of the analysis chart 504 to the reference site of the fundus (for example, the center of the fovea centralis, the center of the optic nerve head, the abnormal site), the center of the blood vessel analysis region is the reference of the fundus. It is automatically set for the part. As a result, the reference position of the analysis can be matched between the analysis by the analysis chart 504 and the analysis for the MC data 402 without changing the analysis area on the MC data 402 again.

<Setting of blood vessel analysis area>
The blood vessel analysis area may be arbitrarily set by the examiner. For example, the CPU 71 may receive an operation signal from the operation unit 76 and change the position of the blood vessel analysis region on the MC data. In this case, the position of the display indicating the blood vessel analysis area may be changed. The CPU 71 may obtain the analysis result in the changed blood vessel analysis area according to the change in the blood vessel analysis area. In this case, the CPU 71 may move the position of the analysis chart 504 on the second image data 502 in conjunction with the movement of the blood vessel analysis region. As a result, it is possible to save the trouble of adjusting the position of the analysis chart 504.

When analyzing a plurality of motion contrast data, it may be used to set the position of the blood vessel analysis region in the second motion contrast data in conjunction with the change in the position of the blood vessel analysis region in the first motion contrast data. good. For example, it can be applied in the analysis of a plurality of motion contrast data different in the depth direction of the fundus. Further, the CPU 71 utilizes the first data area set on the first motion contrast data, and the second data that corresponds to the first data area in the second motion contrast data. The artifacts in the region may be removed (eg, the brightness of each pixel in the second data region is subtracted from the brightness of each brightness in the first data region).

The range (size) of the blood vessel analysis area may be set by the examiner. The range of the blood vessel analysis region may be set according to the acquisition region of MC data 402 on the fundus. The acquisition region may be set according to different acquisition regions with respect to the surface direction of the fundus. For example, the range may be set for the macula site and the papillary site, respectively.

As the setting according to the acquisition area, it may be possible to set according to different acquisition areas in the depth direction of the fundus. For example, ranges may be set for different vascular layers. Of course, the range is not limited to the vascular layer, and the range may be set for different retinal layers (or choroidal layers). This enables blood vessel analysis according to the acquisition region on the fundus. When a plurality of frontal MC data are generated for each blood vessel layer, the range of the blood vessel analysis region may be preset in the frontal MC data of each blood vessel layer. This enables blood vessel analysis according to each blood vessel layer.

FIG. 5 is an example of a setting screen, in which the macula map shows motion contrast data centered on the macula and the papillary map shows motion contrast data centered on the papillary, and the range (for example, diameter) is shown in map units. Is set. When setting the range, for example, the CPU 71 may change the range (size) of the graphic 404 by receiving an operation signal from the operation unit 76. Further, the range of the blood vessel analysis region may be the same as that of the analysis chart 504. Further, the range of the blood vessel analysis region may be set based on the range of the analysis chart 504.

In the above description, the position and range (size) of the blood vessel analysis region can be set as the change parameters of the blood vessel analysis region, but the present invention is not limited to this. For example, the shape of the blood vessel analysis region may be set (eg, circle, ellipse, rectangle, etc.). In this case, the shape of the blood vessel analysis region may be set according to the acquisition region of the MC data 402 on the fundus.

The blood vessel analysis region may be a region divided into a plurality of sections, and blood vessel analysis may be performed in each section. A plurality of blood vessel analysis regions having different arrangement positions or ranges (sizes) of each section may be selectable.

In this case, the division pattern of the blood vessel analysis region may be set according to the acquisition region of the MC data 402 on the fundus. For example, the Superficial Capillary Plexus may be an entire chart (one section), and the Intermediate Capillary Plexus may be an S / I chart (upper and lower divided sections). In this case, the arrangement position and range of each section of the blood vessel analysis region may be set to be the same as the arrangement position and range of each section of the analysis chart 504. Thereby, the analysis result of each section of the analysis chart 504 and the analysis result of each section in the blood vessel analysis region can be evaluated in association with each other. Therefore, the relationship between the blood vessel analysis result of the fundus and the morphological analysis result (for example, layer thickness) of the fundus can be confirmed in section units.

<Blood vessel extraction processing, blood vessel measurement>
The CPU 71 may display the measurement result in the set blood vessel analysis area on the display unit 75 by analyzing the MC data 402 in the blood vessel analysis area set as described above. In this way, the measurement on the analysis chart and the measurement on the MC data 402 can be smoothly performed. The analysis result may be displayed as a numerical value 406 on the MC display area 400, for example.

For example, the CPU 71 discriminates between a blood vessel region and a non-blood vessel region by performing an analysis by image processing on a region set as a blood vessel analysis region on MC data. The blood vessel region is extracted by the discrimination process. In this case, the non-vascular region may be extracted by the discrimination process.

The discrimination process may be, for example, a threshold value process, and the pixels that satisfy the threshold value may be defined as the blood vessel region, and the pixels that do not satisfy the threshold value may be discriminated as the non-blood vessel region. The threshold value itself may be arbitrarily set by the examiner, or may be predetermined as a fixed value. Further, the threshold value may be set after performing an image analysis process on the MC data 402.

For example, the CPU 71 may perform measurement on the blood vessel region based on the result of the discrimination process. The CPU 71 may measure the blood vessel region based on the blood vessel region extracted by the discrimination process. The measurement result may be, for example, blood vessel density and blood vessel area. As the density of the blood vessel region, for example, the area of the blood vessel (blood vessel volume) per unit area can be obtained by obtaining the ratio of the blood vessel region in the entire blood vessel analysis region. The measurement result is not limited to this, and may be, for example, the total amount of blood vessels, the degree of meandering of blood vessels, the regularity of blood vessels, and the like. When the blood vessel analysis region is divided into a plurality of sections, the CPU 71 may obtain the ratio and difference of the measurement results between the sections. Thereby, for example, the symmetry of blood vessels can be obtained.

<Discrimination of capillaries>
Hereinafter, the process of acquiring the measurement result regarding the capillary region will be described. When measuring the capillary region, the CPU 71 determines the macrovascular region and the non-vascular region, and then analyzes the region determined as the vascular region by image processing to obtain the macrovascular region and the capillary region. The discrimination process may be performed. The capillary region is extracted by the discrimination process.

The discriminating process may be, for example, a discriminating process based on the blood vessel diameter, and a blood vessel having a blood vessel diameter below the threshold value may be defined as a capillary region, and a blood vessel having a blood vessel diameter exceeding the threshold value may be discriminated as a macrovascular region. This utilizes the fact that the capillaries are thin and the large blood vessels are thick in terms of blood vessel diameter. By using the blood vessel diameter, it is possible to accurately distinguish between a capillary having a small blood vessel diameter and a large blood vessel having a large blood vessel diameter. When determining the blood vessel diameter, the CPU 71 may measure each blood vessel diameter included in the blood vessel region by image processing. For example, the CPU 71 may thin the blood vessel region detected from the MC data and measure the original blood vessel diameter from the thinned line. The method for measuring the blood vessel diameter is not limited to this, and for example, the distance between the blood vessel walls (for example, the distance between the intima) may be measured.

The discrimination process may be, for example, a discrimination process based on the number of blood vessel branches. A blood vessel in which the number of blood vessel branches exceeds the threshold is defined as a capillary region, and a blood vessel in which the number of blood vessel branches is below the threshold is defined as a macrovascular region. You may. This utilizes the fact that there are many capillaries and few large blood vessels in terms of the number of branches of blood vessels. By using the number of branches, it is possible to accurately distinguish between capillaries located at the relatively terminal end and large blood vessels located at the relatively base end. In this case, the number of branches of the blood vessel from the papilla may be the reference, or the number of branches from the large blood vessel having the upper size among the large blood vessels may be the reference. When determining the number of branch points of a blood vessel, the CPU 71 may extract the branch points of the blood vessels by image processing and measure the number of branch points for each blood vessel. The discriminant process may be performed by integrating the blood vessel diameter and the number of blood vessel branches. As a result, the discrimination accuracy is improved.

In the above description, the blood vessel diameter and the number of branches have been described as an example as a process for discriminating between a large blood vessel and a capillary, but the present invention is not limited to this. For example, it may be a discrimination process based on the difference in blood flow velocity of blood vessels. More specifically, a blood vessel having a blood flow velocity below the threshold value may be determined as a capillary region, and a blood vessel having a blood flow velocity exceeding the threshold value may be determined as a macrovascular region. This can take advantage of slow capillaries and fast macrovascular blood flow velocities. The blood flow velocity may be detected by using, for example, the difference in brightness between the capillaries and the large blood vessels in the MC data. In this case, the capillaries are imaged relatively brightly and the large blood vessels are imaged relatively darkly.

When a threshold value related to the blood vessel diameter, the number of branches, or the like is used in the discrimination process, the threshold value itself may be arbitrarily set by the examiner or may be predetermined as a fixed value. In the case of a fixed value, the threshold value may be changed according to the characteristics of the subject (for example, either age or gender). That is, the criteria for distinguishing between macrovasculars and capillaries may be set by the examiner. The process of discriminating between macrovasculars and capillaries may be performed through the operation of the examiner, and the discriminant result may be obtained by the examiner designating the capillary region on the MC data. .. Of course, by designating a large blood vessel, a capillary region other than the large blood vessel may be specified as a result.

FIG. 6 is an example of the result of the discrimination process. It may be determined. In the above treatment, the capillary region may be extracted, and the macrovascular region does not necessarily have to be extracted.

<Measurement of capillaries with reduced effects of large blood vessels>
The CPU 71 may analyze the MC data to acquire the measurement result regarding the capillary region. For example, the CPU 71 may acquire the measurement result regarding the capillary region through a mitigation process for reducing the influence on the measurement result by a large blood vessel having a blood vessel diameter larger than that of the capillary.

The mitigation process may be, for example, an analysis process specific to a blood vessel region smaller than a certain blood vessel diameter. Further, the CPU 71 may perform a process of specifying the capillary region from the MC data, or may acquire a measurement result regarding the capillary region based on the specified capillary region. Further, the CPU 71 may discriminate between the macrovascular region and the capillary region and perform processing in the blood vessel region, and perform measurement processing on the region determined as the capillary region. That is, the above determination result may be used. In addition, a region surrounded by a blood vessel region may be specified, and measurement results regarding capillaries may be obtained based on the specified region. In addition, the capillaries may be identified by the difference between the MC data and the frontal image of the fundus. As the fundus front image, a front image acquired by a fundus camera or SLO may be used.

An example of mitigation processing is shown below. For example, the CPU 71 may perform measurement on the capillary region based on the result of the discrimination process. In this case, the CPU 71 may measure the blood vessel region of the capillaries based on the capillary region determined by the discrimination process. As the measurement result, for example, the blood vessel density of the capillaries and the blood vessel area of the capillaries may be used. As the blood vessel density of the capillaries, for example, the area of the capillaries (blood vessel volume) per unit area can be obtained by obtaining the ratio of the capillaries region in the entire blood vessel analysis region. The measurement result is not limited to this, and may be, for example, the total amount of capillaries, the degree of meandering of capillaries, the regularity of capillaries, and the like. When the blood vessel analysis region is divided into a plurality of sections, the CPU 71 may obtain the ratio and difference of the measurement results between the sections. Thereby, for example, the symmetry of capillaries can be obtained.

According to the above measurement process, for example, since the measurement specific to the capillaries is performed, the influence of the large blood vessels is reduced, and the capillaries can be measured more accurately. Therefore, for example, an eye disease related to capillaries can be evaluated more accurately. On the other hand, in the case of measurement including a large blood vessel, the large blood vessel occupies a certain specific gravity in the entire blood vessel region, and can be a factor for burying and fluctuating the measurement blood vessel related to the capillaries.

In the above description, the macrovascular region and the capillary region are discriminated, and the measurement result is acquired for the region discriminated as the capillary region, but the measurement result is not limited to this, and the CPU 71 is large by a weighting calculation or the like. The measurement process including the macrovascular region and the capillary region may be performed by using the arithmetic processing constructed so as to reduce the specific gravity of the blood vessel region. In this case, the CPU 71 may perform image processing for thinning the region determined as the macrovascular region to the same degree as the capillaries, and then perform measurement processing including the macrovascular region and the capillary region. The above treatment also provides measurement results in which the influence of large blood vessels is reduced. In the above description, an example of measuring the capillary region is shown, but the present invention is not limited to this, and the measurement may be performed on the macrovascular region extracted through the discrimination process.

<Two-dimensional measurement of capillaries, acquisition of blood vessel analysis map>
The CPU 71 may obtain the measurement result regarding the capillary region two-dimensionally or three-dimensionally. Further, the CPU 71 may output a color-coded color map according to the measurement result at each position with respect to the capillary region.

For example, the CPU 71 may perform measurement processing on each two-dimensional position of the capillary region. In this case, for example, the measurement process may be performed for each pixel forming the capillary region or for each pixel group composed of a plurality of pixels. The measurement range may be, for example, within a set range set as a part of the MC data, or may be the entire MC data. As the setting range set as a part of the MC data, the reference site of the fundus (for example, the fovea centralis, the center of the optic nerve head, the abnormal site) may be set as the center. The setting range may be a predetermined range set in advance, or may be a range arbitrarily set by the examiner. Further, the shape of the set range may be arbitrarily changed.

For example, the CPU 71 may divide the capillary region into a plurality of sections and perform measurement processing on each of the divided sections (see, for example, FIG. 7). In this case, for example, the CPU 71 may acquire the measurement result for each minute area. More specifically, the measurement result in each section may be obtained by dividing the 256 × 256 two-dimensional MC data into 8 × 8 two-dimensional MC data units. Further, the CPU 71 may acquire the measurement result for each relatively wide area (for example, two dimensions). More specifically, the measurement result in each section may be obtained by dividing the 256 × 256 two-dimensional MC data in the vertical and horizontal directions (for example, 3 divisions in each of the vertical and horizontal directions and 4 divisions in each of the vertical and horizontal directions). In addition, in the measurement process of capillaries, it is not always necessary to acquire it as a quantitative measurement value, and a method (for example, grading) in which the measurement result is obtained stepwise may be used.

The CPU 71 may acquire a blood vessel analysis map based on the measurement result regarding the capillary region (see, for example, FIG. 7). The blood vessel analysis map may be displayed on the screen of the display unit 75.

The blood vessel analysis map may be, for example, a map showing a two-dimensional distribution of measurement results relating to capillaries. The blood vessel analysis map may be, for example, a color map color-coded according to the measured values at each two-dimensional position. In this case, the CPU 71 may determine a display color according to the measured value at each two-dimensional position and display each position with the determined display color. The display color may be a preset display color according to the magnitude of the measurement result, or may be arbitrarily set by the examiner according to the measurement result. More specifically, the blood vessel analysis map may be, for example, a color-coded color map according to the measurement result in each section. The CPU 71 may determine a display color according to the measurement result for each section, and display each section with the determined display color.

In the blood vessel analysis map, the large blood vessels may not be color-coded. Further, the measurement result regarding the large blood vessel may be displayed in a color different from the color coding according to the measurement result of the capillaries. As a result, the examiner can accurately evaluate the capillaries because the capillaries and the large blood vessels are discriminated from each other. Further, the blood vessel analysis map may be displayed by superimposing it on the MC data. In this case, the color coding by the map is not superimposed and displayed on the large blood vessel of the MC data, or the color can be distinguished from the capillary region. Overlapping display may be performed.

The type of blood vessel analysis map may be, for example, at least one of a basic map, a comparison map, a difference map, and an examination date difference map. More specifically, the basic map may be a basic map (for example, a blood vessel density map) in which the magnitude of the measured value regarding the capillaries of the eye to be examined is two-dimensionally expressed. The comparison map may be a comparison map showing the comparison result between the measured value of the eye to be examined for the capillaries and the normal eye data for the capillaries stored in the normal eye database. The difference map may be a deviation map (difference map) showing the deviation between the measured value of the eye to be examined for the capillaries and the normal eye data for the capillaries stored in the blood vessel information database. The examination date difference map may be an examination date difference map showing the difference between different examination dates and the measured values of the eye to be inspected for capillaries.

When obtaining a blood vessel analysis map, for example, MC data may be divided into layers and a blood vessel analysis map may be obtained for at least one layer. In this case, a plurality of blood vessel analysis maps with different layer regions may be acquired. In addition, blood vessel analysis maps in a plurality of layer regions may be acquired. The layer-by-layer division processing may be performed by image processing (for example, segmentation) on the MC data, or the result of image processing (for example, segmentation) on the OCT data that is the basis of the MC data is applied to the MC data. You may.

When displaying the blood vessel analysis map, for example, the CPU 71 simultaneously displays the blood vessel analysis map and the MC data (for example, two-dimensional MC data) that is the basis of the blood vessel analysis map on the same screen of the display unit 75. It may be (see, for example, FIG. 8).

For example, the CPU 71 may superimpose the obtained blood vessel analysis map on the MC data of the eye to be examined. As a result, the disappeared region of the blood vessel can be easily confirmed on the MC data. When superimposing the blood vessel analysis map, the CPU 71 may display the region beyond the normal range in the normal eye data to be emphasized as an abnormal region. For example, the CPU 71 may superimpose the obtained blood vessel analysis map on the front image of the eye to be inspected. The frontal image of the fundus may be an infrared frontal image, a color frontal image, or a frontal image based on OCT data.

The CPU 71 may change the blood vessel analysis map according to the measurement site. In this case, the output map may be preset according to the measurement site, or may be arbitrarily set by the examiner.

For example, the number of sections in the blood vessel analysis map may be changed depending on the measurement site. More specifically, for example, the number of sections to be divided may be set to be relatively large for the MC data of the macula site, and the number of sections to be divided may be set to be relatively small for the MC data of the papillary site. This is because detailed measurement results may be required for the macula, and overall measurement results may be required for the papilla. Further, the depth region of the MC data, which is the basis of the blood vessel analysis map, may be changed according to the measurement site. More specifically, for example, for the MC data of the macula site, a vascular analysis map for the region in front of the retina (eg, NFL to IPL) is set, and for the papillary site, the region of the entire retina (eg, NFL to RPE). ) May be set.

Further, the CPU 71 may change the blood vessel analysis map according to the analysis disease. In this case, the output map may be preset according to the measurement site, or may be arbitrarily set by the examiner.

For example, depending on the analysis disease, at least one of the number of sections in the blood vessel analysis map, the position of the sections, and the depth region of the MC data on which the blood vessel analysis map is based may be changed. For example, in the case of diabetic retinopathy, ischemia occurs in a relatively shallow region (front side) on the retina.

On the other hand, BRVO (Branch retinal vein occlusion: branch retinal vein occlusion), CRVO (Central retinal vein occlusion: central retinal vein occlusion), BRAO (Branch retinal artery occlusion: branch retinal vein occlusion), CRAO (Central retinal) In the case of artery occlusion), ischemia occurs throughout the retina. Therefore, the lesion can be evaluated suitably by changing the depth region of the MC data according to the lesion.

Regarding the number of sections in the blood vessel analysis map, for example, in the case of CRVO, the area around the papilla is considered to be the cause, so even if the sections are set uniformly over the entire fundus in the front direction (direction orthogonal to the depth direction). good. On the other hand, in the case of BRVO, since it is said that the onset occurs on the upper ear side, the number of sections may be large on the upper ear side and may be small on the other regions. That is, at least one of the number of sections and the arrangement position of the sections may be changed according to the lesion.

<Example of blood vessel density map>
Below, as an example of the blood vessel analysis map, a blood vessel density map showing a two-dimensional distribution of blood vessel density with respect to capillaries is shown (see FIGS. 7 and 9).

The map of FIG. 7 is an example of a color map color-coded according to the measured value (blood vessel density) in each section. For example, the CPU 71 may generate a color map according to a preset measurement value for each section and display the generated color map on the display unit 75. By changing the display color according to the measured value, the disappearance of blood vessels can be easily grasped.

Further, when the color map is displayed in section units as described above, at least one of the shape and the number of sections may be arbitrarily changed. As a result, the density may be displayed according to the disease.

The CPU 71 may compare the blood vessel density distribution data of the normal eye stored in the blood vessel information database with the blood vessel density distribution data of the eye to be inspected in the same range as the obtained blood vessel density map. The display allows the examiner to easily confirm the disappearance state of the blood vessels of the subject eye with respect to the normal eye. When displaying the comparison result, a difference map between the normal eye data and the eye data to be inspected is useful. Further, as a comparison result, the CPU 71 may simultaneously display the blood vessel density map based on the normal eye data and the blood vessel density map based on the eye test data on the display unit 75.

Further, the CPU 71 may compare the past blood vessel density distribution data of the test eye stored in the blood vessel information database with the current blood vessel density distribution data of the test eye in the same range as the obtained blood vessel density map. By displaying the comparison result, the examiner can easily confirm the change over time regarding the disappearance state of the blood vessel. When displaying the comparison result, a difference map between the past eye examination data and the current eye examination data is useful. Further, as a comparison result, the CPU 71 may simultaneously display the blood vessel density map based on the past eye examination data and the blood vessel density map based on the current eye examination data on the display unit 75.

As a method of displaying the blood vessel analysis map, for example, as shown in FIG. 9, the CPU 71 identifies a region surrounded by blood vessels based on the blood vessel region in the MC data, and as a result, measures the capillaries. The two-dimensional distribution of the result may be obtained.

In this case, the CPU 71 may, for example, determine the connectivity of each blood vessel by image processing and specify the region surrounded by blood vessels depending on whether or not the non-blood vessel region is surrounded by blood vessels. In this case, the non-blood vessel region does not necessarily have to be surrounded by 360 degrees, and if it is surrounded by a certain amount, it may be determined that the region is surrounded by blood vessels.

More specifically, the CPU 71 may calculate the area of the area surrounded by the blood vessels. The CPU 71 may perform color-coded display according to the calculated area in each area. At least two colors are used for color coding. For example, as shown in FIG. 9, a narrow area may be displayed in a first color (for example, red) and a wide area may be displayed in a second color (for example, green). As the capillaries disappear, the first color region decreases and the second color region increases. Therefore, the disappearance of the capillaries can be easily grasped by these color distributions. According to such a display method, the blood vessels themselves are not counted as an area, and as a result, the influence of the large blood vessels is reduced, and the disappearance state of the capillaries can be accurately grasped.

When determining the disappearance state of capillaries, the portion of the non-vascular region where the distance from the nearest blood vessel is equal to or greater than the threshold value may be color-coded.

In the above description, the blood vessel density map has been described as an example, but the present invention is not limited to this, and it goes without saying that the above display form can be applied to other blood vessel analysis maps.

<Blood vessel information database>
The blood vessel information database may be stored in the storage unit 74. The data stored in the blood vessel information database may be used, for example, for comparison with the acquired measurement result of the eye to be inspected, and the blood vessel measurement result (for example, blood vessel density, blood vessel area, capillaries) which is a measurement result related to blood vessels. At least one of the total amount of capillaries, the degree of tortuosity of capillaries, the regularity of capillaries, etc.) is stored as a database.

The blood vessel information database may be, for example, a normal eye database, and the blood vessel measurement results of the normal eye are stored. The normal eye database may be used, for example, for comparison with the actually measured measurement results of the eye to be inspected.

The normal eye database may be created by integrating a large number of normal eye blood vessel measurement results, and for example, statistical measurement values obtained from a large number of normal eye blood vessel measurement results may be stored. In this case, for example, a normal eye database may be constructed by acquiring blood vessel measurement results for a large number of eyes and integrating the blood vessel measurement results of normal eyes. The normal eye database as described above may be constructed for each race, gender, and eye characteristic (for example, axial length) and stored in the storage unit 74.

The blood vessel information database may be, for example, a follow-up database, and the past blood vessel measurement results of each eye to be examined are stored. The follow-up database may be used, for example, for comparison with newly acquired measurement results of the eye to be inspected, or for comparison with past measurement results acquired at different times. As the follow-up database, for example, the measurement results acquired in the follow-up observation may be stored for each subject together with the measurement time (for example, the date and time).

In the above-mentioned blood vessel information database, a database in which the influence of large blood vessels is reduced is constructed by acquiring measurement results in a state where the influence of large blood vessels is reduced and creating a database. By utilizing such a blood vessel information database, it is possible to more accurately analyze the capillaries of the eye to be inspected.

<Measurement of blood vessels considering the depth direction>
When performing blood vessel measurement, the CPU 71 may analyze MC data to acquire a measurement result relating to a blood vessel region in a specific depth region. Further, the CPU 71 may correct the measurement result based on the measurement range in the depth region. The blood vessel region in a specific depth region may be a predetermined layer (the entire retinal layer or the entire choroid), or data obtained by extracting a part of the depth region in the three-dimensional data. It may be.

For example, the CPU 71 may perform measurement on the blood vessel region in consideration of the measurement range in the depth direction. For example, the CPU 71 may correct the measurement result of the blood vessel region calculated based on the MC data based on the measurement range in the depth direction. For example, the two-dimensional measurement result regarding the blood vessel region calculated based on the front MC data may be corrected based on the measurement range in the depth direction.

The measurement range in the depth direction may be, for example, the size of the measurement range D1 in the depth direction corresponding to the front MC data used for the blood vessel measurement, as shown in FIG. In this case, the CPU 71 may correct the measurement result of the blood vessel region calculated based on the front MC data according to the size of the measurement range D1.

The MC data relating to the specific depth region may be OCT frontal motion contrast data based on the three-dimensional motion contrast data in the specific depth region, and the CPU 71 specifies the MC data based on the measurement range of the depth region. The two-dimensional measurement result regarding the vascular region in the depth region of is may be corrected.

More specifically, when the front MC data is generated by selecting the 3D MC data relating to the partial region in the depth direction from the entire 3D MC data, the CPU 71 sets the depth measurement range of the partial region. The size in the depth direction may be obtained.

Here, when the measurement range D1 is larger than the size of the reference measurement range, the CPU 71 increases the measurement range in the depth direction with respect to the reference, so that the measurement result may be calculated lower according to the increment. .. On the other hand, when the measurement range D1 is smaller than the size of the reference measurement range, the CPU 71 reduces the measurement range in the depth direction with respect to the reference eye, so even if the measurement result is calculated higher according to the decrease. good.

According to the above correction, the fluctuation of the measurement result due to the difference in the size of the measurement range in the depth direction is corrected, and the blood vessel region can be measured more quantitatively. For example, in the evaluation of the disappearance state of blood vessels, when the measured value is obtained only from the front MC data, the information in the depth direction is not considered. Therefore, when the measured value for blood vessels is constant, the same result is output even if the size of the measurement range is different, so there is room for improvement in the evaluation of blood vessel density and the like. According to the above configuration, it is possible to perform measurement in consideration of the difference in measurement range, and it is possible to obtain blood vessel density and the like more accurately. The measurement range in the depth direction may be acquired at each two-dimensional position (for example, a section) from which the measurement result is acquired, and correction may be performed for each two-dimensional position.

The MC data relating to the specific depth region may be OCT frontal motion contrast data relating to the specific layer region, and the CPU 71 relates to the blood vessel region in the specific depth region based on the thickness data of the layer region. The dimensional measurement result may be corrected.

More specifically, when frontal MC data is acquired based on three-dimensional MC data in a given retinal layer (eg, optic nerve fiber layer: NFL), the CPU 71 will, for example, determine the predetermined retinal layer in the eye under test. The thickness data may be acquired and the measurement result may be corrected by the acquired thickness data.

Here, when the retina thickness of the eye to be inspected is large with respect to the reference thickness data (for example, the measured value of the predetermined retinal layer corresponding to the normal eye data of the blood vessel information database), the CPU 71 refers to the data in the reference eye. Since the volume of the predetermined retinal layer increases, the measurement result may be calculated lower according to the increase in thickness. On the other hand, when the net thickness of the eye to be inspected is smaller than the reference thickness data, the CPU 71 calculates the measurement result higher as the thickness increases because the volume of the predetermined retinal layer decreases with respect to the reference eye data. You may. The thickness data of the predetermined retinal layer may be acquired based on the OCT data which is the basis of the MC data, or may be acquired based on the distance between the vascular networks in the three-dimensional MC data.

According to the above correction, the fluctuation of the measurement result due to the difference in the thickness of the retinal layer is corrected, and the blood vessel region can be measured more quantitatively. For example, in the evaluation of the disappearance state of blood vessels, when the measured value is obtained only from the front MC data, the information in the depth direction is not considered. Therefore, when the measured value for blood vessels is constant, the same result is output even if the retina thickness is different, so there is room for improvement in the evaluation of blood vessel density and the like. According to the above configuration, it is possible to perform measurement in consideration of the difference in retina thickness, and it is possible to more accurately obtain the blood vessel density and the like.

For example, in the initial stage of a certain eye disease or the like, when the decrease in tissue is faster than the decrease in blood vessel density, the above correction tends to increase the blood vessel density result. On the contrary, for example, in the initial stage of a certain eye disease, when the decrease in blood vessel density is faster than the decrease in tissue, the above correction tends to decrease the blood vessel density result. In this case, follow-up observation using the corrected measurement result may lead to early detection of eye disease.

In the above description, the NFL is taken as an example as the predetermined retinal layer, but the CPU 71 applies the above embodiment to the layer region of the fundus (for example, another retinal layer or the choroidal layer). You may. The layer region may be a region composed of a single layer or a region composed of a plurality of layers.

In the above description, the measurement result is corrected in consideration of the measurement range in the depth direction, but the present invention is not limited to this. For example, the CPU 71 may perform three-dimensional measurement based on three-dimensional MC data.

More specifically, the CPU 71 may obtain the distribution of the blood vessel measurement result (for example, blood vessel density) three-dimensionally. In this case, the CPU 71 processes the three-dimensional MC data to three-dimensionally extract the blood vessel region in the predetermined depth region (for example, the predetermined retinal layer), and three-dimensionally obtains the measurement result of the extracted blood vessel region. You may ask for it. The CPU 71 may divide the extracted three-dimensional blood vessel region into a plurality of blocks and perform measurement processing on each of the divided blocks. In this case, for example, the CPU 71 may acquire the measurement result for each minute area. More specifically, the measurement result in each block may be obtained by dividing the 256 × 256 × 256 3D MC data into 8 × 8 × 8 3D MC data units (for example, FIG. 11). Further, the CPU 71 may acquire the measurement result for each relatively wide area. More specifically, the measurement result in each block may be obtained by dividing the three-dimensional MC data of 256 × 256 × 256 in the three-dimensional direction (for example, dividing into 3 to 10).

The result obtained three-dimensionally may be, for example, the volume of the blood vessel region and the three-dimensional blood vessel density distribution of the blood vessel region. When obtaining the three-dimensional blood vessel density distribution, the CPU 71 processes the three-dimensional MC data to obtain the volume of the blood vessel region in the predetermined depth region, and the volume of the three-dimensional MC data in the predetermined depth region is the blood vessel. By dividing by the volume of the region, the three-dimensional density of the vascular region can be obtained.

The CPU 71 may display the distribution of the measurement results obtained three-dimensionally as a color map. For example, the CPU 71 may display a color-coded three-dimensional image according to the measurement result in each block obtained as described above. Further, the CPU 71 may obtain a three-dimensional distribution of measurement results relating to capillaries by three-dimensionally measuring the region surrounded by blood vessels. In this case, the CPU 71 may, for example, determine the connectivity of each blood vessel by image processing and extract the region surrounded by blood vessels depending on whether or not the non-blood vessel region is surrounded by blood vessels. In this case, the non-vascular region does not necessarily have to be surrounded by 360 degrees in the three-dimensional direction, and if it is surrounded by a certain amount, it may be determined that the region is surrounded by blood vessels. More specifically, the CPU 71 may calculate the volume of the region surrounded by blood vessels. The CPU 71 may perform color-coded display according to the calculated volume in each area.

<Display of OCT blood vessel analysis map and OCT morphological analysis map>
The CPU 71 may simultaneously display a blood vessel analysis map based on MC data acquired by OCT and a morphological analysis map based on ophthalmic OCT data on a monitor.

For example, when displaying the blood vessel analysis map regarding the blood vessel measurement result of the eye to be inspected, the CPU 71 may display it at the same time as the morphological analysis map (for example, the map regarding the net film thickness) regarding the morphological measurement result of the eye to be inspected (for example). See FIG. 12). For example, the CPU 71 may display the blood vessel analysis map and the morphological analysis map in parallel.

For example, the CPU 71 may simultaneously display a blood vessel analysis map and a morphological analysis map with respect to a common layer region in the fundus. In this case, the correlation between the blood vessel information and the morphological information of the eye to be inspected can be easily obtained for a common region in the depth direction.

For example, in the morphological analysis map relating to the thickness of a predetermined retinal layer, when the thickness of the retinal layer is thin, by checking the blood vessel analysis map at the same time, whether the thickness decrease is due to the decrease in blood vessels or due to other factors. Whether or not it can be easily grasped. In this case, the CPU 71 may acquire the blood vessel analysis map and the morphological analysis map having different measurement times, and simultaneously display the blood vessel analysis map and the morphological analysis map in time series.

As the type of the blood vessel analysis map, for example, as described above, a basic map, a comparison map, a difference map, and an examination date difference map can be considered. Further, the morphological analysis map may be, for example, a map showing a two-dimensional distribution of morphological measurement results relating to the fundus. In this case, for example, a color map that is color-coded according to the measured value may be used. Examples of the analysis map include a thickness map (basic map) showing the layer thickness, a comparison map showing the comparison result between the layer thickness of the eye to be inspected and the layer thickness of the normal eye stored in the normal eye database, and the layer thickness of the eye to be inspected. Even if it is a difference map (deviation map) that shows the deviation from the layer thickness of the normal eye stored in the normal eye database by the standard deviation, and an examination date comparison thickness difference map that shows the difference in thickness from each examination date. good.

When determining the layer thickness, for example, the OCT data may be divided into layers by image processing (for example, segmentation processing) on the OCT data, and the thickness of each layer may be measured based on the interval between the layer boundaries. Of course, the morphological measurement result is not limited to the layer thickness. The analysis map is not limited to the layer thickness map, and may be, for example, a map showing the curvature distribution of the fundus.

Here, when the blood vessel analysis map is simultaneously displayed on the display unit 75 together with the morphological analysis map, by simultaneously displaying the maps with the same characteristics, the morphological information and the blood vessel information can be compared more accurately according to the purpose. Can be done. For example, the basic map of the blood vessel analysis map and the basic map of the morphological analysis map may be displayed at the same time. Similarly, each comparison map may be displayed at the same time, each difference map may be displayed at the same time, or each inspection date difference map may be displayed at the same time.

The morphological analysis map may be in a display format in which a color map (for example, a map related to layer thickness) is superimposed on the frontal image of the fundus. The fundus frontal image may be acquired by a fundus camera, SLO, or may be an OCT frontal image acquired based on OCT data. Further, a display format in which a color map (for example, a map related to layer thickness) is superimposed on the front MC image may be used.

When the blood vessel analysis map and the morphological analysis map are displayed at the same time, each map may be in a format in which a color map is superimposed on the front MC data. With such a display, the correlation between the blood vessel analysis result and the morphological analysis blood vessel can be easily obtained for the image of the MC data. In this case, the MC data, the blood vessel analysis map, and the morphological analysis map may be displayed at the same time. Further, each map may be in a format in which a color map is superimposed on the frontal image of the fundus. With such a display, the correlation between the blood vessel analysis result and the morphological analysis blood vessel can be easily obtained for the frontal image of the fundus. The frontal image of the fundus may be an infrared frontal image, a color frontal image, or a frontal image based on OCT data.

<Integration of blood vessel measurement results and morphological measurement results>
The CPU 71 may perform the measurement process in which the blood vessel measurement result of the eye to be inspected and the morphology measurement result of the eye to be inspected are integrated. When each measurement result is acquired as a two-dimensional distribution, for example, the CPU 71 may display the result of the integrated measurement process as a single color map or analysis chart (see, for example, FIG. 13). The CPU 71 may obtain an integrated value that integrates the blood vessel measurement result of the eye to be inspected and the morphology measurement result of the eye to be inspected.

When performing integrated measurement, the CPU 71 may obtain a representative value (for example, an average value or a total value) of each measured value of the blood vessel measurement result and the morphological measurement result, or a certain weighting is applied between each measured value. It may be a weighting operation to be performed. In this case, if the units of the measured values do not match (for example, density and thickness), a predetermined integrated parameter may be set, and an arbitrary coefficient may be set between the blood vessel measurement result and the morphological measurement result.

Further, similarly to the above, the CPU 71 may simultaneously display the result of the visual field measurement of the eye to be inspected and the result of the blood vessel measurement of the eye to be inspected on the same screen. Further, the CPU 71 may perform a measurement process in which the result of the visual field measurement of the eye to be inspected and the result of the blood vessel measurement of the eye to be inspected are integrated, and display the integrated measurement result.

Of course, the CPU 71 may simultaneously display the result of the morphological measurement of the eye to be inspected, the result of the visual field measurement of the eye to be inspected, and the result of the blood vessel measurement of the eye to be inspected on the same screen. Further, the CPU 71 may perform measurement processing in which the result of morphological measurement of the eye to be inspected, the result of visual field measurement of the eye to be inspected, and the result of blood vessel measurement of the eye to be inspected are integrated, and display the integrated measurement result. ..

<Blood vessel analysis chart>
According to the above description, the measurement result is displayed as a blood vessel analysis map, but the present invention is not limited to this, and the measurement result may be output as a blood vessel analysis chart.

For example, the blood vessel analysis chart may be a blood vessel analysis chart for measuring the basic statistic of the blood vessel measurement result in a preset section, or the basic statistic in the section may be measured. The sections forming the blood vessel analysis chart may be one region or a plurality of sections. In the case of a plurality of sections, the basic statistic may be measured for each divided section. The basic statistic may be a representative value (mean value, median value, mode value, maximum value, minimum value, etc.), dispersal degree (variance, standard deviation, coefficient of variation), or the like.

For example, the blood vessel analysis chart may be a chart for obtaining the average for each region for the two-dimensional distribution of the blood vessel measurement results. The blood vessel analysis chart may be provided with a numerical display area for displaying the blood vessel measurement result in a predetermined area numerically.

<Follow-up>
The CPU 71 may acquire the blood vessel measurement result based on the MC data from the storage unit 74 and display the acquired blood vessel measurement result over time (see, for example, FIG. 14). For example, the CPU 71 may display a graph showing the time course of the blood vessel measurement result, or may arrange a plurality of blood vessel analysis maps having different acquisition times in chronological order. A plurality of blood vessel analysis maps with different acquisition times may be displayed as time-lapse images. The CPU 71 may obtain the difference between the first acquisition time and the second acquisition time with respect to the blood vessel measurement result. The CPU 71 may display a difference map of the two-dimensional distribution by obtaining the difference between the first acquisition time and the second acquisition time in the two-dimensional distribution of the blood vessel measurement result.

In the above explanation, the point that the influence of the large blood vessel is reduced and the measurement is performed has been described, but <blood vessel analysis map>, <blood vessel density map>, <blood vessel information database>, <blood vessel considering the depth direction Items such as <Measurement>, <Display of blood vessel analysis map and thickness analysis map>, <Integration of blood vessel measurement result and morphological measurement result>, <Vessel analysis chart>, <Follow-up>, or items explained below, or , Regarding the technical contents shown in other parts, it is possible to implement a method that can be applied to measurement including large blood vessels. Of course, blood vessel measurement specific to a large blood vessel may be performed. Similarly, other items may be implemented in parallel or independently.

In the above description, the MC data acquired by OCT has been described as an example, but it is acquired by a fundus imaging device (for example, a fundus camera, a scanning laser ophthalmoscope (SLO)) that captures a frontal image of the fundus of the eye to be inspected. The above embodiment can also be applied to the measurement of the fundus blood vessels included in the fundus anterior image. In this case, the frontal image of the fundus may be at least one of frontal image data (for example, a color fundus image) due to reflected light from the eye to be inspected and frontal image data due to fluorescence from the eye to be inspected. Moreover, the analysis which combined these with the OCT motion contrast data may be performed.

When automatically discriminating between the blood vessel region and the non-blood vessel region, the CPU 71 applies a binarization process (for example, a discriminant analysis method) from the brightness value of the MC data 402 to vascularize / non-blood vessel. The area may be defined. When the blood vessel analysis region is divided into a plurality of sections, a threshold value may be set for each region corresponding to each section, or a threshold value may be set for the entire blood vessel analysis region.

<Development of blood vessel information into MC data>
The CPU 71 may acquire blood vessel information regarding the blood vessel region included in the MC data. Further, the CPU 71 may add the acquired blood vessel information to the blood vessel region included in the MC data. In this case, it is sufficient that the blood vessel information is acquired for at least one blood vessel included in the blood vessel region, the blood vessel information may be acquired for one blood vessel included in the MC data, and the blood vessel information may be given, or the MC data. Blood vessel information may be acquired for each of the plurality of blood vessels included in the blood vessel, and the blood vessel information may be given to each. Further, the blood vessel information may include blood vessel information for each blood vessel, and the position information and blood vessel information of each blood vessel may be acquired as a set.

Regarding the blood vessel information, for example, the CPU 71 may acquire the arteriovenous information regarding the blood vessel region included in the MC data as the blood vessel information. Further, the CPU 71 may add arteriovenous information to the blood vessel region included in the MC data (see, for example, FIG. 15). That is, the blood vessel information may be information on the function of blood vessels.

Further, for example, the CPU 71 may acquire bleeding information regarding the blood vessel region included in the MC data as blood vessel information. Further, the CPU 71 may add bleeding information to the blood vessel region included in the MC data.

When obtaining the blood vessel information, the CPU 71 may acquire the blood vessel information from data different from the MC data. As a result, for example, blood vessel information that is difficult to detect only with the MC data is added to the MC data, and the blood vessel evaluation by the examiner is performed better. In the case of different data, the acquisition area may be common for at least a part of the MC data. Further, in different data, it may be distribution data related to blood vessel information, or may be registered with respect to MC data.

For example, the CPU 71 may acquire blood vessel information from OCT data or Doppler OCT data acquired by OCT. The OCT data or Doppler OCT data may be data acquired by an OCT device that acquires MC data, or may be data acquired by an OCT device that is different from the OCT device that acquires MC data. The CPU 71 may acquire blood vessel information from the MC data itself, and for example, a Doppler OCT may be used as an OCT device for acquiring MC data.

Further, the CPU 71 may be acquired from image data captured by a modality (imaging unit) different from that of OCT. The different modality may be a fundus camera, SLO, LSFG (laser speckle flowgraphy), as long as blood vessel information can be acquired.

Blood vessel information is not limited to arteriovenous information and bleeding information, but includes blood flow velocity information, layer position information, color information, polarization characteristic information, traveling direction information, hardness information, ratio information of inner and outer walls of blood vessels, and the like. May be obtained. That is, the blood vessel information may be, for example, information on the characteristics of the blood vessel region included in the MC data. Blood flow velocity information may be acquired, for example, by Doppler OCT. The layer position information may be acquired by, for example, OCT data or MC data. Color information may be acquired, for example, by a fundus camera or spectroscopic OCT. Polarization characteristic information may be acquired by, for example, PS-OCT.

When outputting the MC data, for example, the CPU 71 may perform image processing on the MC data based on the acquired blood vessel information and display the MC data including the blood vessel information on the display unit 75. The display is not limited to the display on the display unit 75, and MC data including blood vessel information may be printed or output to an external server. The MC data may be, for example, front MC data, three-dimensional MC data, or two-dimensional MC data. In this case, the MC data including the blood vessel information may be displayed on the display unit 75 based on the blood vessel information given in advance to the MC data. In addition, a display regarding the acquired blood vessel information may be added to the MC data.

When analyzing the MC data and acquiring the measurement result regarding the blood vessel region, for example, the CPU 71 may acquire the measurement result regarding the blood vessel region by using the acquired blood vessel information. In this case, the CPU 71 may obtain the measurement result two-dimensionally or three-dimensionally. The measurement result may be displayed as, for example, an analysis map or an analysis chart.

The CPU 71 may acquire the measurement result by using the blood vessel information given in advance to the MC. After acquiring the measurement result regarding the blood vessel region in advance, the blood vessel information may be added to the acquired measurement result.

When the arteriovenous information is acquired as the blood vessel information, the CPU 71 may acquire at least one of the measurement result regarding the arterial region and the measurement result regarding the vein by using the arteriovenous information. For example, when the bleeding information is acquired as the blood vessel information, the CPU 71 may acquire the measurement result regarding the bleeding blood vessel region by using the bleeding information. That is, the CPU 71 may perform measurement processing for each artery / vein by using the arteriovenous information of each blood vessel.

The blood vessel information regarding the blood vessel region included in the MC data may be stored in the storage unit 74 together with the MC data. In this case, for example, the position information in each blood vessel in the MC data and the blood vessel information may be stored in association with each other. In this case, the specific method is not limited as long as the blood vessel information of at least one blood vessel included in the MC data can be referred to in at least one of the analysis / measurement processing and the display processing later.

As for the addition of blood vessel information, for example, each blood vessel in the MC data and the blood vessel information may be associated with each other by registering the blood vessel information including the position information of each blood vessel in the MC data. In addition, a table showing the correspondence between each blood vessel and blood vessel information in the MC data may be set. Further, blood vessel information may be added to the MC data of each blood vessel, and the CPU 71 may refer to the blood vessel information corresponding to the MC data of each blood vessel.

<Acquisition and grant of arteriovenous information>
The following is an example of acquisition and addition of arteriovenous information. For example, the CPU 71 may acquire discrimination information for discriminating whether at least one blood vessel included in the MC data is an artery or a vein as arteriovenous information. The arteriovenous information may be arteriovenous information in which whether or not each blood vessel is an artery or a vein is specified. The arteriovenous information may be information on any of arteries and veins, and may be, for example, information on arteries only or information on veins only. The arteriovenous information may be vascular distribution information relating to at least one of arteries and veins.

For example, the CPU 71 may perform image processing on the MC data based on the arteriovenous information and display the MC data reflecting the arteriovenous information on the display unit 75. In this case, the CPU 71 may display each blood vessel region in the MC data in a different color (for example, arteries are red and veins are blue) depending on whether or not they are arteries or veins. In this case, the color may be superimposed on the blood vessel region of the MC data imaged in black and white, or the MC data itself may be colored. Further, for example, the CPU 71 may display MC data in which only arteries or veins are imaged by using the arteriovenous information. In this case, the CPU 71 may image one of the arterial region and the venous region by extracting one of the arterial region and the venous region by using the arteriovenous information. Further, the CPU 71 may switch display or parallel display the MC data relating to the arterial region and the MC data relating to the venous region. In this case, the arteries and veins may be displayed in different colors. In addition, arteriovenous information may be displayed when a specific blood vessel is designated.

By doing the above, by identifying whether the blood vessels included in the MC data are arteries or veins, it becomes possible to evaluate the state of the blood vessels of the eye to be inspected, including the functional aspect, and clinically. I think it is useful for.

Further, by adding arteriovenous information to the MC data, it is possible to display and measure the MC data reflecting the information. For example, when the CPU 71 analyzes the MC data and acquires the measurement result regarding the blood vessel region, the CPU 71 may acquire at least one of the measurement result regarding the arterial region and the measurement result regarding the vein by using the arteriovenous information. .. As the measurement result, at least one of blood vessel density, blood vessel area, total amount of blood vessels, meandering degree of blood vessels, regularity of capillaries, and blood vessel diameter may be calculated for arteries or veins.

The CPU 71 may acquire an integrated measurement result in which the measurement result regarding the arterial region and the measurement result regarding the vein of the MC data are integrated. For example, the CPU 71 may acquire the ratio or difference between the measurement result regarding the arterial region and the measurement result regarding the vein. More specifically, the CPU 71 may determine the arteriovenous ratio (A / V ratio), which is the ratio of the blood vessel diameter in the arterial region to the blood vessel diameter in the venous region.

The CPU 71 may obtain the measurement result two-dimensionally or three-dimensionally with respect to at least one of the measurement result regarding the arterial region and the measurement result regarding the vein. Further, the CPU 71 may display the obtained measurement result on the display unit 75 as a blood vessel analysis map. Further, the CPU 71 may switch or display the blood vessel analysis map for the arterial region and the blood vessel analysis map for the venous region in parallel. Of course, the measurement result is not limited to the blood vessel analysis map, and the obtained measurement result may be displayed as a blood vessel analysis chart.

The CPU 71 may add arteriovenous information to a part or the whole of the blood vessel region included in the MC data, and may further give arteriovenous information to a part of at least one blood vessel, or at least a part of the blood vessel. Arteriovenous information may be given to the entire blood vessel. Of course, arteriovenous information may be given to each of a plurality of blood vessel regions. Further, the CPU 71 may classify each blood vessel region with respect to the blood vessel diameter and add arteriovenous information with respect to a part of the blood vessel diameter. For example, arteriovenous information may be given to either the capillary region or the macrovascular region.

The arteriovenous discrimination method is a method of discriminating the arteriovenous using OCT, a method of discriminating the arteriovenous by applying the principle of the pulse oximeter, or a method of discriminating the arterial vein from the color information of the fundus color image and the SLO image. There are ways to do this. A specific example will be shown below.

<Arteriovenous discrimination based on OCT data>
When acquiring the arteriovenous information, for example, the CPU 71 may acquire the arteriovenous information based on the OCT data of the fundus or the Doppler OCT data acquired by the OCT. The OCT data may be, for example, OCT data acquired for a region common to the MC data (for example, two-dimensional OCT data), or OCT data on which the MC data is based. In this case, the OCT data is distinguished from the MC data in that it is the reflectance data of the fundus. The OCT data may be, for example, tomographic image data including morphological information of the fundus.

When the arteriovenous information is acquired from the OCT data, for example, it may be determined whether the blood vessel of the eye to be inspected is an artery or a vein based on the brightness value of the OCT data. More specifically, as shown in FIG. 16, in the OCT data, the arteries appear bright and the veins appear darker than the arteries.

When detecting the blood vessel region in the OCT data, for example, the brightness value in the region on the NFL side of the RPE may be obtained for each A scan data, and the region having a relatively low brightness value may be set as the blood vessel region candidate. The CPU 71 may detect a region having a predetermined width or more in the scanning direction as a blood vessel region among the blood vessel candidate regions. When detecting the blood vessel region in the OCT data, the CPU 71 may detect the blood vessel region in the OCT data by using the blood vessel information included in the MC data. In this case, when the OCT data which is the basis of the MC data is used as the OCT data, registration between the data is easy and the blood vessel region can be easily specified.

Next, the CPU 71 may discriminate between arteries and veins based on the brightness value of the blood vessel region on the OCT data. For example, the CPU 71 may determine arteries and veins by using the brightness average of the blood vessel region in each region of the fundus region TSNIT as shown in FIG. The CPU 71 may calculate the brightness average from ILM to IPL / INL of the blood vessel region in each region of TSNIT, and determine the threshold value at which the interclass variance is maximized by a discriminant analysis method. The CPU 71 may use a blood vessel having a brightness higher than this threshold value as an artery and a blood vessel having a brightness lower than this threshold value as a vein.

The method for discriminating between an artery and a vein using OCT data is not limited to this, and the difference in the brightness of the blood vessel wall between the artery and the vein may be used. Further, the difference in the brightness of the RPE under the blood vessel between the artery and the vein may be utilized. That is, the method is not particularly limited as long as it is a method for discriminating between an artery and a vein by utilizing the difference (for example, brightness, shape) between the artery and the vein imaged on the OCT data. That is, the present inventors have found a discrimination method utilizing the fact that the drawing state differs between the artery and the vein in the blood vessel included in the OCT data.

When the arteriovenous information based on the OCT data is acquired as described above, the CPU 71 may add the arteriovenous information to the corresponding blood vessel region in the MC data relating to the region common to the OCT data. In the case of 3D MC data, the CPU 71 acquires arteriovenous information based on each 2D OCT data forming the 3D OCT data, and the acquired arteriovenous information is used as each 2D MC data of the 3D MC data. May be applied to the corresponding blood vessels of. Of course, the CPU 71 may acquire the three-dimensional distribution information regarding the artery or vein in the dimensional OCT data, and add the arteriovenous information based on the acquired three-dimensional distribution information to the blood vessel region of the three-dimensional MC data. In this case, the CPU 71 may acquire the arteriovenous information using the frontal OCT data and add the acquired arteriovenous information to the blood vessel region of the frontal OCT data.

In the above description, the arteriovenous information is acquired based on the OCT data, but the present invention is not limited to this, and the arteriovenous information may be acquired using the positive or negative of the phase change amount in the Doppler OCT data. For example, the CPU 71 may obtain the traveling direction of the blood flow based on the amount of phase change, or may acquire the arteriovenous information based on the traveling direction of the blood flow. In this case, for example, if the destination following the traveling direction of the blood flow is the papilla, it may be determined as a vein, and if it is the opposite, it may be determined as an artery.

<Applying the principle of pulse oximeter>
A pulse oximeter is a hemoglobin that is not bound to hemoglobin that is bound to oxygen, and measures the oxygen saturation in blood by utilizing the property that the absorption of red light and infrared light changes. Applying this, the OCT light source performs the same measurement as the pulse oximeter. The one with high oxygen saturation is judged as an artery, and the one with low oxygen saturation is judged as a vein.

That is, the CPU 71 may acquire arteriovenous information based on the oxygen saturation data of the eye to be examined. In this case, the arteriovenous information may be given by registering the obtained arteriovenous information of each blood vessel and the MC data.

<Acquisition of arteriovenous information from other devices>
The CPU 71 may acquire arteriovenous information based on image data acquired by an imaging means (modality) different from OCT. As another modality, for example, image data acquired by a fundus camera, SLO, or LSFG (laser speckle flowgraphy) may be used. For example, arteriovenous information may be acquired based on the difference in color tone between arteries and veins in the fundus image acquired by the fundus camera and SLO. In addition, arteries and veins may be determined by utilizing the property that arteries have a lighter blood vessel color than veins.

Further, the arteriovenous information may be acquired based on the difference in the blood flow direction due to the time-series change of the fluorescence contrast image acquired by the SLO or the fundus camera. Further, arteriovenous information may be acquired based on the relative difference in blood flow velocity and blood flow direction acquired by LSFG.

When acquiring arteriovenous information as described above, a modality for obtaining images that are the source of the arteriovenous information is arranged together with the OCT optical system, and these images may be acquired at the same time as the motion contrast data is acquired. .. Further, the modality is not limited to this, and the modality may be arranged separately from the OCT device. Further, it is not always necessary for the CPU 71 to execute the discrimination process as described above, and the CPU 71 may acquire the arteriovenous information acquired in advance from the outside or the storage unit 74.

<Measurement of blood vessel direction>
The CPU 71 may calculate the edge strength in each direction in the three-dimensional direction with respect to the three-dimensional MC data, and acquire the traveling direction information regarding the blood vessel region based on the edge strength in each direction. Further, the CPU 71 may add the acquired traveling direction information to the blood vessel region included in the three-dimensional MC data. As a result, the direction of each blood vessel included in the MC data can be detected, so that the degree of meandering and the like can be easily measured.

More specifically, as shown in FIG. 18, the CPU 71 may perform edge detection on the volume data of the three-dimensional MC data in the XYZ directions, and acquire the edge detection results in the XYZ directions as volume data. In this case, the volume data of the edge detection result for each direction is acquired. Here, the traveling direction of each blood vessel can be easily detected based on the edge detection result in each volume data corresponding to each point of the three-dimensional MC data. For example, when only a blood vessel extending straight in the X direction exists in the three-dimensional MC data, the edge is detected in the YZ direction and the edge is not detected in the X direction, so that the traveling direction is detected based on this. Using such a relationship, it is possible to detect the traveling direction of each blood vessel. Although a simple example is shown for convenience of explanation, the above method can be applied even when a plurality of blood vessels travel in different directions.

The CPU 71 may perform image processing on the three-dimensional motion contrast data based on the acquired traveling direction information, and display the three-dimensional motion contrast data reflecting the traveling direction information on the display unit (for example, FIG. 19). In this case, an arrow may be displayed on each blood vessel, or a color corresponding to the traveling direction may be given.

When the CPU 71 analyzes the three-dimensional motion contrast data and acquires the measurement result regarding the blood vessel region, the CPU 71 may acquire the meandering degree of the blood vessel by using the traveling direction information.

The detection of the traveling direction is not limited to the detection of the three-dimensional direction, and may be applied to the detection of the two-dimensional direction. For example, in the front MC data, edge detection may be performed in each of the XY directions, and a two-dimensional traveling direction may be detected based on the edge detection result.

Not limited to the above method, even if the traveling direction is detected by performing the thinning process on each blood vessel of the MC data and obtaining the connectivity of each pixel of the skeleton generated by the thinning process. good. For example, in the case of two dimensions, the two-dimensional traveling direction may be detected by obtaining the connectivity with the peripheral 8 pixels. Similarly, in the case of three dimensions, the three-dimensional traveling direction may be detected by determining the connectivity of the peripheral pixels in three dimensions.

<Running state of blood vessels with respect to the fundus layer>
The CPU 71 may acquire layer traveling information indicating the traveling state of the blood vessel region included in the MC data with respect to the layer region formed on the eye to be inspected (see, for example, FIG. 20). Further, the CPU 71 may add layer running information to the blood vessel region included in the MC data. Thereby, for example, the running state of the blood vessel with respect to the layer region can be acquired, and the normality / abnormality of the eye to be inspected can be determined.

The layer traveling information may be, for example, information that can be discriminated with respect to the layer region in which at least one blood vessel included in the MC data is traveling. The layer traveling information may be, for example, information on whether or not at least one blood vessel included in the MC data travels in a specific layer region. Further, it may be information on whether or not at least one blood vessel included in the MC data travels from which layer region to which layer region.

The MC data may be MC data relating to a plurality of layers formed on the fundus of the eye to be inspected, and the CPU 71 performs a process of identifying a layer in which at least one blood vessel runs among the plurality of layers. Layer running information may be acquired.

More specifically, the CPU 71 may acquire layer direction information based on the running position of the blood vessel in the MC data and the layer position acquired by the segmentation process on the MC data or the OCT data.

For example, the CPU 71 may compare the three-dimensional position information of a specific blood vessel included in the MC data with the three-dimensional position information of each layer of the fundus to specify the fundus layer on which the blood vessel K travels. In this case, the CPU 71 may specify the layer region in which the blood vessel K runs by determining whether or not the blood vessel K exists in the specific layer. For example, it is detected that the blood vessel K in which the choroidal layer is present reaches the RPE layer.

In this case, whether or not the CPU 71 reaches (extends) a second layer (for example, RPE layer) different from the first layer with respect to the blood vessels distributed in the first layer (for example, choroidal layer). You may determine whether or not. Further, the CPU 71 may determine from the first layer to which layer the blood vessel region reaches in the MC data. According to the above method, it is possible to easily determine whether or not the blood vessels existing in the choroidal layer have reached the RPE layer, so that it is possible to easily evaluate diabetic retinopathy and the like.

By acquiring the layer region information regarding each blood vessel as described above, the layer region information can be given to each blood vessel included in the MC data. The CPU 71 may perform image processing on the three-dimensional motion contrast data based on the added layer area information, and display the three-dimensional motion contrast data reflecting the layer area information on the display unit. In this case, a color corresponding to the layer area information may be added.

The CPU 71 may acquire the measurement result regarding the blood vessel based on the given layer area information. For example, with respect to the blood vessels existing in the first layer, the CPU 71 may acquire the two-dimensional distribution of the blood vessels that have reached the second layer as a measurement result, and for example, acquire the density distribution specified for the blood vessels. You may. Further, the CPU 71 may acquire the measurement result regarding the blood vessel crossing the plurality of layers in the MC data.

The CPU 71 may obtain the blood vessel measurement result based on the layer area information two-dimensionally or three-dimensionally. Further, the CPU 71 may display the obtained measurement result on the display unit 75 as a blood vessel analysis map. Of course, the measurement result is not limited to the blood vessel analysis map, and the obtained measurement result may be displayed as a blood vessel analysis chart.

<Separation of blood vessel layers using motion contrast data>
The CPU 71 may acquire blood vessel distribution information regarding the two-dimensional abundance of blood vessels in each depth region of the three-dimensional MC data. Further, the CPU 71 may separate the blood vessels included in the three-dimensional MC data into layers based on the acquired blood vessel distribution information. That is, the vascular layer may be separated using MC data.

This allows the vascular layer to be reliably separated. In the conventional technique, the blood vessel layer of MC data is separated by using the layer boundary detection result of OCT data. For example, the NFL vascular layer is defined by a fixed value, such as a predetermined range of NFL / GCL layer boundaries. Therefore, some patients cannot separate the vascular layer well. Also, if OCT layer boundary detection fails, vascular layer separation may also fail. Blood vessels in the retina are clustered in specific areas. Therefore, from the results of OCT Angiography, it is considered that the blood vessel layer can be accurately separated by separating each group of blood vessels.

More specifically, for example, as the two-dimensional abundance of blood vessels, the number of pixels in which blood vessels are detected in the front MC data of each depth region may be measured, and the number of blood vessel pixels in each depth region may be measured. You may obtain a histogram showing the distribution of (see FIG. 21). Further, the CPU 71 may obtain the density distribution or the blood vessel area of the blood vessels in the front MC data of each depth region as the two-dimensional abundance of the blood vessels. That is, the blood vessel distribution information may be, for example, information indicating the distribution of the two-dimensional abundance of blood vessels in each depth region of the three-dimensional MC data in the depth direction.

The CPU 71 may measure the number of pixels in which the blood vessel is detected as the two-dimensional abundance of the blood vessel, or may acquire a histogram showing the distribution of the number of blood vessel pixels in each depth region. When the three-dimensional MC data is divided in the depth direction, it may be divided in units of one pixel or in units of two or more pixels.

The blood vessels of the fundus are generally divided into each layer of the fundus to form a blood vessel layer. Therefore, the acquired blood vessel distribution information includes mountains corresponding to each blood vessel layer. Therefore, the CPU 71 may separate the blood vessels for each layer by separating each mountain in the blood vessel distribution information. When separating each mountain included in the blood vessel distribution, for example, the CPU 71 may detect the mountain depending on whether the region exceeding a certain abundance has a predetermined width in the depth direction.

The three-dimensional MC data may be divided into a plurality of parts in the front direction (direction orthogonal to the depth direction), and blood vessel distribution information may be acquired in each of the divided regions. In this case, the blood vessels may be separated into layers in each of the divided regions. For example, the blood vessel layer may be separated in units of blocks B1, B2, and B3 in FIG. 21.

The CPU 71 may provide layer information that can be discriminated from blood vessels existing in other layers to each blood vessel separated for each layer.

<Acquisition and grant of bleeding information>
The CPU 71 may acquire bleeding information regarding the blood vessel region included in the MC data. Further, the CPU 71 may add the acquired bleeding information to the blood vessel region included in the MC data.

The bleeding information may be the position information of the bleeding area in the blood vessel area. Further, the bleeding information may be bleeding information for determining the presence or absence of bleeding related to at least one blood vessel. Further, the bleeding information may be bleeding information in which the presence or absence of bleeding is specified for each blood vessel. Further, the bleeding information may be information indicating the distribution of the bleeding region.

Bleeding information may be acquired, for example, based on frontal OCT data. The front OCT data may be front OCT data relating to a part of the depth direction in the three-dimensional OCT data, or may be front OCT data relating to the entire depth direction in the three-dimensional OCT data. Of course, bleeding information may be acquired based on the two-dimensional OCT data. In this case, the position information of the bleeding information may be acquired.

Regarding the bleeding area where there was bleeding from the blood vessel in the OCT data, the brightness value is attenuated from the bleeding area to the back side, and the layer structure is not drawn. In the example of FIG. 22, bleeding occurs in the left region of the image. Therefore, the position information of the bleeding region can be determined by detecting the bleeding region by utilizing these characteristics.

Here, information regarding the bleeding region acquired by the OCT data may be added to the MC data. In this case, the CPU 71 may display the MC data to which the bleeding region is assigned based on the bleeding region information assigned to the MC data (see, for example, FIG. 23). This makes it possible to confirm the bleeding vascular region in the MC data. In this case, the position of the bleeding area is registered on the MC data by associating the position information of the bleeding area with the position information of the MC data.

In this case, as the MC data to which the bleeding region is added, for example, the MC data to which the graphic corresponding to the bleeding region is added may be displayed on the MC data, or the blood vessel region with respect to the bleeding region can be discriminated and displayed. The bleeding MC data may be displayed. The bleeding information may be acquired from an image acquired by another imaging means such as a fundus camera or SLO.

<Labeling>
The CPU 71 may assign a label indicating the analysis result to each voxel of the three-dimensional data in the analysis / display of the three-dimensional data. The types of labels include vascular area, avascular area, blood flow, blood vessel wall, direction of blood vessel, layer and depth where blood vessel exists, blood flow volume, speed of blood flow, oxygen saturation of blood flow, and blood vessel. It may be at least one of connection, normal blood vessel, abnormal blood vessel, bleeding and the like.

The label may be indicated numerically, or may be indicated by a color or a graph. Further, the label may be displayed alone, or may be displayed superimposed on the OCT data (tomographic image), the fundus anterior image, and the OCTMC data (OCT Angiography image). Alternatively, a plurality of labels may be displayed in combination. Further, as described above, the CPU 71 may discriminate the arteries and veins from other devices, OCT data, and OCTMC data, and record the discriminant result as a label in the voxel.

Further, the CPU 71 may determine the direction of the blood vessel and record the determination result as a label in the voxel. More specifically, the MC data may be edge-detected in the XYZ directions, the direction of the blood vessel may be detected from the size of the edge component in each direction, and recorded as a label. The meandering degree of the blood vessel may be calculated from the orientation label of the blood vessel, and this meandering degree may also be recorded as a label. As a method of calculating the meandering degree, a method of accumulating the difference between the direction labels of the blood vessels of the immediately preceding voxel and the current voxel while tracing the blood vessels to obtain the meandering degree can be considered.

Further, the CPU 71 may determine whether it is a normal blood vessel or an abnormal blood vessel by observing the connection of blood vessels. For example, if the blood vessel that emerges from the choroid extends in the NFL direction, it is judged to be abnormal. By labeling normal blood vessels and abnormal blood vessels, it is possible to display only one of them or display them in different colors.

Further, the CPU 71 may record the layer information of the blood vessel layer as a label in the voxel. That is, by attaching the result of separating the blood vessel layer to the voxel as a label using MC data, it is possible to display the blood flow for each layer and use it for determining the normality or abnormality of the blood vessel shown below.

In measuring the blood vessel diameter, the CPU 71 may delineate the labeled region of the blood vessel region and record the delineated region on the voxel as the label of the skeleton of the blood vessel (see FIG. 24). Further, the CPU 71 may measure the amount of change in the blood vessel before and after thinning as the blood vessel diameter and record the blood vessel diameter as a label in the voxel.

More specifically, the blood vessel region detected by the MC data may be thinned, and the original blood vessel diameter may be measured from the thinned line. The result of thinning and the diameter of the blood vessel may be recorded on the voxel as a label. The skeleton of a blood vessel may be displayed by displaying only the voxels labeled as thin lines. Further, by using the blood vessel diameter label, only blood vessels having a certain blood vessel diameter or more or less, which changes the color of the blood vessel depending on the blood vessel diameter, may be displayed. In addition, a normal eye database of blood vessel diameter may be constructed.

Further, the CPU 71 may determine the bleeding part from the OCT data and the OCTMC data and record it in a voxel as a label. According to OCT data, the bleeding site has a relatively high reflex. In addition, the signal is attenuated under the bleeding part. By combining this information with OCTMC data, a location having the above-mentioned characteristics of a bleeding portion may be detected as a bleeding portion and recorded as a label at a location where a blood vessel is located.

The label display may be displayed in chronological order for follow-up (follow-up). In addition, the label display may be displayed in combination with the inspection results of other devices.

FIG. 25 is an example when the OCT label is selected as the display label, and data such as a 3D map is displayed. Here, various analysis results may be given to each voxel, and a single label or a plurality of labels desired by the examiner may be superimposed and displayed on the three-dimensional data.

Here, when the blood vessel label is selected, only the blood vessels of the retina are displayed, and the structure of the blood vessels can be easily confirmed. Further, the blood vessel label and the layer label may be combined and displayed in different colors for each layer.

The CPU 71 may combine the blood vessel label and the arteriovenous label to color-code the arteries and veins. Moreover, only one of an artery and a vein may be displayed. Further, the blood vessel label, the arteriovenous label, and the layer boundary label may be combined to display blood vessels of only the desired retinal layer.

By combining a plurality of labels in this way, various data can be easily confirmed. In addition, the examiner can freely set the combination of labels. In addition, the results of other devices may be displayed together. For example, the correlation between the visual field defect and the blood vessel defect can be confirmed by combining the result of the perimeter and the result of OCT Angiography.

According to the label display as described above, diagnosis can be supported by analyzing various data including OCT data, MC data, etc. as three-dimensional data and presenting the data to the user.

In the above description, the fundus of the eye to be inspected has been described as an example, but the present invention is not limited to this, and the application is also applicable to the anterior segment of the eye to be inspected. Furthermore, it is applicable not only to the eye to be inspected but also to other motion contrast data acquired by OCT (for example, motion contrast data in tissues other than the eye).

It is a block diagram which shows the outline of this Example. It is a figure which shows an example of the optical system of an OCT device. It is a figure for demonstrating acquisition of motion contrast. It is a figure which shows an example of the display screen. It is a figure which shows an example of the setting screen. This is an example of the result of the discrimination process. This is an example of a case where the capillary region is divided into a plurality of sections for measurement. This is a display example of a blood vessel analysis map and MC data. This is an example of a blood vessel density map. It is a figure which shows an example of the case of performing the blood vessel measurement in consideration of the depth direction. It is a figure which shows an example of the case where the distribution of the blood vessel measurement result is obtained three-dimensionally. This is a display example of a blood vessel analysis map and a morphological analysis map. This is an integrated example of blood vessel measurement results and morphological measurement results. This is an example of displaying the blood vessel measurement result over time. This is an example of giving arteriovenous information to the blood vessel region. It is an example of an arterial image and a vein image in OCT data. This is an example of discriminating arteries and veins based on the brightness value of the blood vessel region on the OCT data. It is a figure which shows an example of the case of acquiring the traveling direction information. This is an example of displaying the three-dimensional motion contrast data reflecting the traveling direction information on the display unit. This is an example of determining the running state of a blood vessel with respect to a layered region. This is an example of separating the blood vessel layer using motion contrast data. This is an example showing a bleeding area in OCT data. This is an example of displaying MC data with a bleeding area. It is a figure which shows an example of the thinning process. This is an example when the OCT label is selected as the display label.

1 OCT motion contrast data analysis device 10 OCT device 70 Control unit 75 Display unit

Claims (4)

An ophthalmic analyzer for analyzing OCT motion contrast data including the blood vessel region of the eye to be inspected.
A analysis means for analyzing the OCT motion contrast data, as arteriovenous information on the blood vessel region included in the OCT motion contrast data, at least one blood vessel included in the OCT motion contrast data is arterial Discrimination information for discriminating whether it is a blood vessel or a blood vessel, which is data different from the OCT motion contrast data, acquired by OCT data acquired by an optical interference tomography or by an imaging means different from the optical interference tomometer. An analysis processing means for acquiring discrimination information based on the obtained image data and assigning whether the blood vessel region of the OCT motion contrast data is an artery or a vein based on the acquired discrimination information.
Based on the acquired discrimination information, the OCT motion contrast data is subjected to image processing, and the OCT motion contrast data including whether it is an artery or a vein with respect to the blood vessel region is displayed on the display unit. Control means and
Ophthalmic analysis apparatus, characterized in that it comprises a. When the analysis processing means analyzes the OCT motion contrast data and acquires the measurement result regarding the blood vessel region, the analysis processing means acquires at least one of the measurement result regarding the arterial region and the measurement result regarding the venous region by using the discrimination information. The ophthalmic analysis apparatus according to claim 1 , wherein the ophthalmic analysis apparatus is characterized in that. The analysis processing means calculates the edge strength in each direction of the three-dimensional direction with respect to the three-dimensional OCT motion contrast data, and based on the edge strength in each direction, the blood vessel region included in the three-dimensional OCT motion contrast data. Get the driving direction information about
Alternatively, the three-dimensional OCT motion contrast data is thinned in each direction in the three-dimensional direction, and the traveling direction information is acquired from the skeleton of the thinning process.
The ophthalmic analysis apparatus according to any one of claims 1 and 2 , wherein the traveling direction information is given to the blood vessel region. An ophthalmic analysis program for operating a computer as various processing means of the ophthalmic analysis apparatus according to any one of claims 1 to 3.

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