JP7384656B2 - Ophthalmology information processing device, ophthalmology device, ophthalmology information processing method, and program - Google Patents

Description

The present invention relates to an ophthalmologic information processing device, an ophthalmologic device, an ophthalmologic information processing method, and a program.

In recent years, optical coherence tomography (OCT), which uses a light beam from a laser light source to measure or image the form of an object to be measured, has been attracting attention. Since OCT is not invasive to the human body unlike X-ray CT (Computed Tomography), it is expected to be applied particularly in certain fields such as medicine and biology. For example, in the field of ophthalmology, devices that form images of the fundus or anterior segment of the eye have been put into practical use. A device using such OCT (OCT device) can be applied to observation of various parts of the eye to be examined (fundus of the eye and anterior segment of the eye). Additionally, because it can obtain high-definition images, it is applied to the diagnosis of various ophthalmological diseases.

Examples of such ophthalmological diseases include uveitis. When uveitis occurs, various symptoms appear due to inflammatory cells infiltrating into the eye. Uveitis is comprehensively diagnosed based on the findings of an ophthalmological examination and the findings of a systemic examination. Ophthalmological examinations include examinations using a slit lamp and fluorescent ophthalmography examinations. Full body examinations include blood tests and chest X-rays. However, in many cases, a diagnosis of uveitis is not made because no characteristic findings are observed.

For example, Non-Patent Document 1 discloses a method of diagnosing and managing uveitis using OCT. Obtaining images depicting inflammatory cells by performing OCT is useful for assisting in the diagnosis of uveitis.

For ophthalmological diseases other than uveitis, if it is possible to objectively evaluate the image area at the cellular level depicted in images obtained using OCT, it will provide useful information to assist in the diagnosis of various ophthalmic diseases. There is a possibility that it can be done.

Caio V. Regatieri et al. , “Use of Optical Coherence Tomography in the Diagnosis and Management of Uveitis”, Int Ophthalmol Clin. , August 2014

In general OCT, the lateral resolution in the direction perpendicular to the A-scan is not as high as the depth resolution in the A-scan direction. Therefore, it is very difficult to identify image regions corresponding to cells or cell clusters depicted in images obtained using OCT. Therefore, conventionally, it has not been possible to objectively evaluate cells or cell clusters (image areas corresponding to cells, etc.) in OCT images.

The present invention has been made in view of these circumstances, and its purpose is to provide a new technique for evaluating image regions in OCT images regardless of the lateral resolution of OCT.

A first aspect of some embodiments includes a region identifying unit that identifies one or more image regions in an OCT image obtained by performing optical coherence tomography on a subject's eye, and a region identifying unit that identifies one or more image regions, and a length of the image region in the A-scan direction. The ophthalmologic information processing apparatus further includes a size specifying section that specifies the size of at least one of the one or more image regions.

In a second aspect of some embodiments, in the first aspect, the size specifying unit specifies the size of the image area from the length of the longest image area in the A-scan direction.

In a third aspect of some embodiments, in the first aspect, the size specifying unit specifies the size of the image area from the length of the area surrounding the image area in the A-scan direction.

In a fourth aspect of some embodiments, in any of the first to third aspects, the region specifying unit identifies the one or more image regions corresponding to cells or cell clusters depicted in the OCT image. Identify.

In a fifth aspect of some embodiments, in any of the first to fourth aspects, a classification unit that classifies the one or more image areas based on the size of the image area specified by the size identification unit is provided. include.

In a sixth aspect of some embodiments, in the fifth aspect, the OCT image is an image in which at least a portion of the vitreous body is depicted, and the classification section is configured to classify two or more inflammatory cells infiltrating into the vitreous body. The one or more image regions are classified into two or more classification types corresponding to.

In a seventh aspect of some embodiments, in the sixth aspect, the two or more classification types correspond to a first classification type corresponding to monocytes, a second classification type corresponding to neutrophils, and a lymphocyte. Includes the third classification type.

An eighth aspect of some embodiments is a distribution information generation unit that generates distribution information of the number or position of the one or more image regions classified by the classification unit in any of the fifth to seventh aspects. including.

A ninth aspect of some embodiments is that in any of the fifth to seventh aspects, the one or more image regions are included in the OCT image in a manner that can be identified for each classification type classified by the classification unit. It includes a distribution information generation unit that generates distribution information including the superimposed images.

A tenth aspect of some embodiments includes, in the eighth aspect or the ninth aspect, a display control unit that causes a display unit to display the distribution information.

In an eleventh aspect of some embodiments, in any of the first to tenth aspects, the OCT image is a three-dimensional image, and the size specifying section spans two or more adjacent B-scan images. Therefore, the size of the three-dimensional image area is specified from the length of the three-dimensional image area drawn in the A-scan direction.

In a twelfth aspect of some embodiments, in any of the first to eleventh aspects, the OCT image is formed based on OCT data obtained by performing optical coherence tomography on the eye to be examined. Includes an image forming section.

A thirteenth aspect of some embodiments is an ophthalmological apparatus including an optical system that acquires OCT data by performing optical coherence tomography on the eye to be examined, and the ophthalmological information processing apparatus according to the twelfth aspect. be.

A fourteenth aspect of some embodiments includes a region specifying step of specifying one or more image regions in an OCT image obtained by performing optical coherence tomography on a subject's eye, and a step of specifying a length of the image region in the A-scan direction. and a size specifying step of specifying the size of at least one of the one or more image regions.

In a fifteenth aspect of some embodiments, in the fourteenth aspect, the size specifying step specifies the size of the image area from the length of the longest image area in the A-scan direction.

In a sixteenth aspect of some embodiments, in the fourteenth aspect, the size identifying step identifies the size of the image area from the length of the area surrounding the image area in the A-scan direction.

In a seventeenth aspect of some embodiments, in any of the fourteenth to sixteenth aspects, the region specifying step includes identifying the one or more image regions corresponding to cells or cell clusters depicted in the OCT image. Identify.

In an eighteenth aspect of some embodiments, in any of the fourteenth to seventeenth aspects, a classification step of classifying the one or more image regions based on the size of the image region identified in the size identifying step is provided. include.

In a nineteenth aspect of some embodiments, in the eighteenth aspect, the OCT image is an image in which at least a portion of the vitreous body is depicted, and the classifying step includes determining whether two or more inflammatory cells infiltrate the vitreous body. The one or more image regions are classified into two or more classification types corresponding to.

In a twentieth aspect of some embodiments, in the nineteenth aspect, the two or more classification types correspond to a first classification type corresponding to monocytes, a second classification type corresponding to neutrophils, and a lymphocyte. Includes the third classification type.

A twenty-first aspect of some embodiments is a distribution information generation step of generating distribution information of the number or position of the one or more image regions classified in the classification step in any of the eighteenth to twentieth aspects. including.

A 22nd aspect of some embodiments is that in any of the 18th to 20th aspects, the one or more image regions are added to the OCT image in a manner that can be identified for each classification type classified in the classification step. The method includes a distribution information generation step of generating distribution information including the superimposed images.

A twenty-third aspect of some embodiments includes a display control step of displaying the distribution information on a display means in the twenty-first aspect or the twenty-second aspect.

In a twenty-fourth aspect of some embodiments, in any of the fourteenth to twenty-third aspects, the OCT image is a three-dimensional image, and the size specifying step spans two or more adjacent B-scan images. Therefore, the size of the three-dimensional image area is specified from the length of the three-dimensional image area drawn in the A-scan direction.

In a twenty-fifth aspect of some embodiments, in any of the fourteenth to twenty-fourth aspects, the OCT image is formed based on OCT data obtained by performing optical coherence tomography on the eye to be examined. including an image forming step.

A twenty-sixth aspect of some embodiments is a program that causes a computer to execute each step of the ophthalmological information processing method according to any one of the fourteenth to twenty-fifth aspects.

Note that it is possible to arbitrarily combine the configurations according to the plurality of aspects described above.

According to the present invention, it is possible to provide a new technique for evaluating an image region in an OCT image regardless of the lateral resolution of OCT.

It is a schematic diagram showing an example of composition of an ophthalmology system concerning an embodiment. FIG. 1 is a schematic diagram showing an example of the configuration of an ophthalmologic apparatus according to an embodiment. 1 is a schematic diagram showing an example of the configuration of an ophthalmological information processing device according to an embodiment. 1 is a schematic diagram showing an example of the configuration of an ophthalmological information processing device according to an embodiment. It is a schematic diagram for explaining operation of an ophthalmology information processing device concerning an embodiment. It is a schematic diagram for explaining operation of an ophthalmology information processing device concerning an embodiment. It is a schematic diagram for explaining operation of an ophthalmology information processing device concerning an embodiment. It is a schematic diagram for explaining operation of an ophthalmology information processing device concerning an embodiment. It is a schematic diagram for explaining operation of an ophthalmology information processing device concerning an embodiment. It is a schematic diagram for explaining operation of an ophthalmology information processing device concerning an embodiment. It is a schematic diagram for explaining operation of an ophthalmology information processing device concerning an embodiment. It is a schematic diagram showing an example of an operation flow of an ophthalmology information processing device concerning an embodiment. It is a schematic diagram showing an example of composition of an ophthalmologic device concerning a modification of an embodiment.

Examples of ophthalmologic information processing devices, ophthalmologic devices, ophthalmologic information processing methods, and programs according to some embodiments of the present invention will be described in detail with reference to the drawings. Note that the matters described in the documents cited in this specification and any known technology can be incorporated into the embodiments.

The ophthalmology system according to the embodiment includes an ophthalmology apparatus and an ophthalmology information processing apparatus. The ophthalmological information processing method according to the embodiment is executed by an ophthalmological information processing apparatus. The program according to the embodiment causes a computer to execute each step of the ophthalmological information processing method according to the embodiment.

The ophthalmological information processing device according to the embodiment performs a predetermined analysis process and a predetermined display process on an image (data) of the eye to be examined obtained by performing optical coherence tomography on the eye to be examined by the ophthalmological apparatus. It is possible to apply The ophthalmological apparatus according to the embodiment includes an optical system for performing OCT on a subject's eye.

The ophthalmological apparatus according to some embodiments further has a function of acquiring a frontal image of the eye to be examined (anterior segment or fundus). The function of acquiring a frontal image of the eye to be examined is realized by an OCT device, a fundus camera, a scanning laser ophthalmoscope, a slit lamp microscope, a surgical microscope, and the like. The ophthalmological apparatus according to some embodiments further includes a function of measuring optical characteristics of an eye to be examined. The function of measuring the optical characteristics of the eye to be examined is achieved by a refractometer, keratometer, tonometer, wavefront analyzer, specular microscope, perimeter, etc. The ophthalmological device according to some embodiments further includes the functionality of a laser treatment device used for laser treatment.

[Ophthalmology system]
FIG. 1 shows a block diagram of a configuration example of an ophthalmologic system according to an embodiment. The ophthalmology system 1 according to the embodiment includes an ophthalmology apparatus 10, an ophthalmology information processing apparatus (an ophthalmology image processing apparatus, an ophthalmology analysis apparatus) 100, an operating device 180, and a display device 190.

The ophthalmologic apparatus 10 collects one-dimensional, two-dimensional, or three-dimensional OCT data by scanning a predetermined region of the subject's eye. The ophthalmologic apparatus 10 acquires a one-dimensional, two-dimensional, or three-dimensional image (OCT image) of the eye to be examined from the collected OCT data. The image of the eye to be examined includes a tomographic image and a frontal image. Tomographic images include A-scan images, B-scan images, and the like. The frontal image includes a C-scan image, a shadowgram, a projection image, and the like. In such an image of the eye to be examined, a region corresponding to at least one of the anterior segment and the posterior segment is depicted.

In particular, the ophthalmologic apparatus 10 according to the embodiment scans at least one of the anterior segment (cornea, crystalline lens) and the posterior segment (fundus of the eye), thereby providing a tomogram in which at least one of the anterior segment and the posterior segment is visualized. It is possible to obtain images or three-dimensional images. A region corresponding to at least a part of the anterior chamber of the eye or a region corresponding to at least a part of the vitreous body is depicted in the tomographic image or the three-dimensional image.

The ophthalmological apparatus 10 transmits the acquired image (data) of the eye to be examined to the ophthalmological information processing apparatus 100. In some embodiments, the ophthalmological device 10 and the ophthalmological information processing device 100 are connected via a data communication network. The ophthalmological information processing apparatus 100 according to some embodiments receives data from one of the plurality of ophthalmological apparatuses 10 selectively connected via a data communication network.

In some embodiments, the ophthalmological device 10 includes at least a portion of the ophthalmological information processing device 100 and is capable of realizing at least a portion of the functions of the ophthalmological information processing device 100. For example, the ophthalmological apparatus 10 may further include at least one of an operating device 180 and a display device 190, and can realize the functions of at least one of the operating device 180 and the display device 190.

The ophthalmological information processing device 100 identifies one or more image regions to be analyzed depicted in the image (tomographic image or three-dimensional image) acquired by the ophthalmological device 10, and from the length of the image region in the A-scan direction, A size of at least one of the identified one or more image regions is determined. The image region includes a region corresponding to a predetermined region within the eye (a characteristic region, a blood vessel, a lesion, a treatment scar), a region corresponding to cells or cell clusters within the eye, and the like. In some embodiments, the image region is a region corresponding to a granular material in a three-dimensional image or a region corresponding to a circular material in a tomographic image.

The ophthalmologic information processing apparatus 100 classifies a plurality of image regions depicted in the acquired image into a plurality of classification types based on the size of the identified image region. The ophthalmological information processing apparatus 100 can generate distribution information based on the classification results of a plurality of image regions. The distribution information includes distribution information (e.g., histogram) of the number of classified objects (number of image regions) for each classified classification type, distribution information of the position of each classified classification type (e.g., distribution image), etc. . In some embodiments, the distribution information includes an image in which a plurality of image regions are superimposed on an image of the eye to be examined in a manner that can be identified for each classification type. The ophthalmological information processing apparatus 100 can display the generated distribution information on the display device 190.

As a result, even if identification is difficult due to the lateral resolution of OCT, the size of each of one or more image regions depicted in the image of the subject's eye can be identified from the length in the A-scan direction, and the One or more image regions can now be evaluated using the determined size.

The operating device 180 and the display device 190 serve as user interface units and provide functions for exchanging information between the ophthalmological information processing device 100 and its user, such as displaying information, inputting information, and inputting operation instructions. The operating device 180 includes operating devices such as levers, buttons, keys, and pointing devices. The operating device 180 according to some embodiments includes a microphone for inputting information by sound. Display device 190 includes a display device such as a flat panel display. In some embodiments, the functions of the operating device 180 and the display device 190 are realized by a device that integrates a device with an input function and a device with a display function, such as a touch panel display. In some embodiments, operating device 180 and display device 190 include a graphical user interface (GUI) for inputting and outputting information.

Below, a case will be described in which the ophthalmological information processing apparatus 100 mainly generates distribution information of an image region corresponding to inflammatory cells or cell clusters infiltrated into the vitreous body in an OCT image. In this case, the ophthalmological apparatus 10 collects OCT data by performing an OCT scan on the fundus including at least a portion of the vitreous body. The ophthalmological information processing device 100 acquires an OCT image including at least a portion of the vitreous body from the collected OCT data, identifies an image region corresponding to inflammatory cells or cell clusters depicted in the acquired OCT image, Generate distribution information for the identified image area.

Further, the ophthalmological information processing apparatus 100 may be configured to generate distribution information of an image region corresponding to substances other than inflammatory cells or cell clusters. The ophthalmological apparatus 10 also performs an OCT scan on a site other than the vitreous body (for example, the anterior chamber of the eye), and the ophthalmological information processing apparatus 100 scans cells or cell clusters depicted in the image of the site other than the vitreous body. It may be configured to generate distribution information of an image area corresponding to .

[Ophthalmological equipment]
FIG. 2 shows a block diagram of a configuration example of the ophthalmologic apparatus 10 according to the embodiment.

The ophthalmological apparatus 10 is provided with an optical system for acquiring OCT data of the eye to be examined. Although the ophthalmologic apparatus 10 has the ability to perform swept source OCT, embodiments are not limited thereto. For example, the type of OCT is not limited to swept source OCT, but may be spectral domain OCT or the like. Swept source OCT splits light from a wavelength swept (wavelength scanning) light source into measurement light and reference light, and generates interference light by interfering with the reference light and the return light of the measurement light that has passed through the measured object. This interference light is then detected by a balanced photodiode or the like, and an image is formed by performing Fourier transform or the like on the detection data collected according to the wavelength sweep and measurement light scan. Spectral domain OCT splits light from a low-coherence light source into measurement light and reference light, makes the return light of the measurement light via the measured object interfere with the reference light, and generates interference light. This is a method in which the spectral distribution is detected with a spectrometer and the detected spectral distribution is subjected to Fourier transform or the like to form an image.

The ophthalmological apparatus 10 includes a control section 11, a data collection section 12, an image forming section 13, and a communication section 14.

The control unit 11 controls each part of the ophthalmological apparatus 10. In particular, the control section 11 controls the data collection section 12, the image forming section 13, and the communication section 14.

The data collection unit 12 collects data on the eye to be examined (one-dimensional, two-dimensional, or three-dimensional OCT data) by scanning the eye to be examined using OCT. The data collection unit 12 includes an interference optical system 12A and a scanning optical system 12B.

The interference optical system 12A splits the light from the light source (wavelength sweep type light source) into measurement light and reference light, and causes the return light of the measurement light that has passed through the eye to be examined to interfere with the reference light that has passed through the reference optical path to generate interference. Generate light and detect the generated interference light. The interference optical system 12A includes at least a fiber coupler and a light receiver such as a balanced photodiode. The fiber coupler splits the light from the light source into measurement light and reference light, and generates interference light by interfering with the return light of the measurement light that has passed through the eye to be examined and the reference light that has passed through the reference optical path. The optical receiver detects the interference light generated by the fiber coupler. The interference optical system 12A may include a light source.

The interference optical system 12A is capable of detecting interference light between the return light of the measurement light that has passed through the anterior segment or the fundus of the subject's eye and the reference light that has passed through the reference optical path. For example, by placing the focal point of the measurement light at or near the anterior segment of the eye to be examined, OCT scanning of the anterior segment becomes possible. For example, by placing the focal point of the measurement light at or near the fundus of the eye to be examined, OCT scanning of the fundus becomes possible. The focal position of the measurement light is controlled by at least one of controlling the insertion and removal of the front lens between the objective lens and the eye to be examined, and controlling movement of the focusing lens in the optical axis direction to change the focal position of the measurement light. It can be changed by

The scanning optical system 12B changes the incident position of the measurement light on the subject's eye by deflecting the measurement light generated by the interference optical system 12A under the control of the control unit 11. The scanning optical system 12B includes, for example, an optical scanner placed at a position that is optically substantially conjugate with the pupil of the eye to be examined. The optical scanner includes, for example, a galvano mirror that scans measurement light in the horizontal direction, a galvano mirror that scans the measurement light in the vertical direction, and a mechanism that independently drives these. Thereby, the measurement light can be scanned in any direction on a plane intersecting the optical axis.

The detection result (detection signal) of the interference light by the interference optical system 12A is an interference signal indicating the spectrum of the interference light.

The image forming unit 13 is controlled by the control unit 11 and generates image data of a tomographic image of the anterior segment or fundus of the eye to be examined based on the (three-dimensional) OCT data of the eye to be examined collected by the data collection unit 12. form. This processing includes processing such as noise removal (noise reduction), filter processing, and FFT (Fast Fourier Transform). The image data acquired in this manner is a data set including a group of image data formed by imaging reflection intensity profiles in a plurality of A-lines (paths of each measurement light within the subject's eye). To improve image quality, multiple data sets collected by scanning the same pattern multiple times can be overlapped (averaged).

The image forming unit 13 can form a B-scan image, a C-scan image, a projection image, a shadowgram, etc. by performing various types of rendering on three-dimensional OCT data. An image of an arbitrary cross section, such as a B-scan image or a C-scan image, is formed by selecting pixels (pixels, voxels) on a specified cross-section from three-dimensional OCT data. A projection image is formed by projecting three-dimensional OCT data in a predetermined direction (Z direction, depth direction, A scan direction). A shadowgram is formed by projecting a portion of three-dimensional OCT data (for example, partial data corresponding to a specific layer) in a predetermined direction.

The ophthalmological apparatus 10 according to some embodiments is provided with a data processing section that performs various data processing (image processing) and analysis processing on the image formed by the image forming section 13. For example, the data processing unit executes correction processing such as image brightness correction and dispersion correction. The data processing unit can form volume data (voxel data) of the eye to be examined by performing known image processing such as interpolation processing that interpolates pixels between tomographic images. When displaying an image based on volume data, the data processing unit performs rendering processing on the volume data to form a pseudo three-dimensional image when viewed from a specific viewing direction.

Each of the control section 11 and the image forming section 13 includes a processor. The processor may be, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), or a programmable logic device (for example, an SPLD). (Simple Programmable Logic Device), CPLD (Complex Programmable Logic Device), FPGA (Field Programmable Gate Array)). The functions of the image forming section 13 are realized by an image forming processor. In some embodiments, the functions of both the control unit 11 and the image forming unit 13 are implemented by one processor. In some embodiments, if the ophthalmological device 10 is provided with a data processing unit, the functionality of the data processing unit is also implemented by the processor.

The processor realizes the functions according to the embodiment by, for example, reading and executing a program stored in a memory circuit or a memory device. A memory circuit or device may be included in the processor. Further, a memory circuit or a memory device may be provided outside the processor.

A memory circuit or a memory device stores various data. The data stored in the storage circuit or storage device includes data acquired by the data collection unit 12 (measured data, photographed data, etc.), information regarding the subject and the subject's eye, and the like. The storage circuit or storage device may store various computer programs and data for operating each part of the ophthalmologic apparatus 10.

The communication unit 14 receives control from the control unit 11 and executes communication interface processing for transmitting or receiving information with the ophthalmological information processing apparatus 100.

The ophthalmological apparatus 10 according to some embodiments transmits image data of the eye to be examined formed by the image forming unit 13 to the ophthalmological information processing apparatus 100.

The ophthalmological apparatus 10 according to some embodiments is provided with a fundus camera, a scanning laser ophthalmoscope, and a slit lamp microscope for acquiring an image of the fundus of an eye to be examined. In some embodiments, the fundus image acquired by the fundus camera is a fluorescein angiography image or a fundus autofluorescence test image.

[Ophthalmology information processing device]
3 and 4 show block diagrams of exemplary configurations of the ophthalmological information processing apparatus 100 according to the embodiment. FIG. 3 represents a functional block diagram of the ophthalmological information processing apparatus 100. FIG. 4 represents a functional block diagram of the analysis section 200 of FIG. 3.

The ophthalmological information processing apparatus 100 includes a control section 110, an image forming section 120, a data processing section 130, and a communication section 140.

The control section 110 controls each section of the ophthalmological information processing apparatus 100. In particular, the control section 110 controls the image forming section 120, the data processing section 130, and the communication section 140.

The communication unit 140 receives control from the control unit 110 and executes communication interface processing for transmitting or receiving information with the communication unit 14 of the ophthalmological information processing device 100.

The control section 110 controls each section of the ophthalmological information processing apparatus 100. In particular, the control section 110 controls the image forming section 120, the data processing section 130, and the communication section 140. Control unit 110 includes a main control unit 111 and a storage unit 112. The main control section 111 includes a display control section 111A.

The display control unit 111A causes the display device 190 to display various information. For example, the display control unit 111A displays the image of the eye to be examined (tomographic image, OCT image) formed by the image forming unit 120 and the image of the data processing result (including the analysis processing result) by the data processing unit 130 on the display device 190. to be displayed. In particular, the display control unit 111A causes the display device 190 to display an image of the eye to be examined, and causes an image region corresponding to an inflammatory cell or a cell mass to be displayed in an identifiable manner in the image. The display control unit 111A according to some embodiments causes the display device 190 to display an image of the eye to be examined, and highlights a region corresponding to an inflammatory cell or the like in the image. For example, the display control unit 111A may display the area corresponding to the inflammatory cells or the background area thereof so that the brightness of the pixels in the area corresponding to the inflammatory cells or the background area is higher than the brightness of the pixels in the other areas. Display.

Furthermore, the display control unit 111A can cause the display device 190 to display in chronological order images representing regions corresponding to inflammatory cells and the like identified as described later from a plurality of OCT data having different examination dates.

The control unit 110 controls each unit of the ophthalmological system 1 based on an operation instruction signal corresponding to the user's operation on the operating device 180.

Each of the control section 110, the image forming section 120, and the data processing section 130 includes a processor. The functions of the image forming section 120 are realized by an image forming processor. The functions of the data processing section 130 are realized by a data processing processor. In some embodiments, the functions of at least two of the control unit 110, the image forming unit 120, and the data processing unit 130 are implemented by one processor.

The storage unit 112 stores various data. The data stored in the storage unit 112 includes data acquired by the ophthalmological apparatus 10 (measured data, photographed data, etc.), image data formed by the image forming unit 120, data processing results by the data processing unit 130, and the subject. There is information regarding the subject and the eye to be examined. The storage unit 112 may store various computer programs and data for operating each unit of the ophthalmological information processing device 100.

(Image forming section 120)
The image forming section 120 forms various OCT images from the OCT data acquired by the ophthalmologic apparatus 10 under the control of the control section 110 . For example, the image forming unit 120 forms an A-scan image from one-dimensional, two-dimensional, or three-dimensional OCT data acquired by the ophthalmological apparatus 10. For example, the image forming unit 120 forms a B-scan image from two-dimensional or three-dimensional OCT data acquired by the ophthalmologic apparatus 10. The image forming section 120 can form the above-mentioned image similarly to the image forming section 13. When the image forming section 13 of the ophthalmological apparatus 10 has the function of the image forming section 120 described above, the image forming section 120 does not need to be provided in the ophthalmological information processing apparatus 100.

(Data processing unit 130)
The data processing unit 130 performs various data processing (image processing) and analysis processing on the image formed by the image forming unit 120. For example, the data processing unit 130 executes correction processing such as image brightness correction and dispersion correction. The data processing unit 130 (or the image forming unit 120) forms an A-scan image, a B-scan image, a C-scan image, a projection image, a shadowgram, etc. from the three-dimensional OCT data acquired by the ophthalmologic apparatus 10. When the ophthalmologic apparatus 10 has the function of the data processing section 130, the data processing section 130 may not be provided in the ophthalmologic information processing apparatus 100.

The data processing section 130 includes a three-dimensional image generation section 131 and an analysis section 200.

(3D image generation unit 131)
The three-dimensional image generation unit 131 can form volume data (voxel data) of the eye to be examined by performing known image processing such as interpolation processing that interpolates pixels between B-scan images (tomographic images). can. When displaying an image based on volume data, the data processing unit 130 performs rendering processing on the volume data to form a pseudo three-dimensional image when viewed from a specific viewing direction.

In some embodiments, the three-dimensional image generation unit 131 performs the above image processing on a plurality of B-scan images in which image regions corresponding to inflammatory cells or cell clusters have been identified by the analysis unit 200 described below. . Thereby, the data processing unit 130 can form volume data in which image regions corresponding to inflammatory cells and the like can be identified.

In some embodiments, the analysis unit 200 described below specifies an image region corresponding to inflammatory cells or cell clusters by analyzing volume data generated by the three-dimensional image generation unit 131. By associating the image area specified by the analysis unit 200 with the volume data, the data processing unit 130 can form volume data in which image areas corresponding to inflammatory cells and the like can be specified.

(Analysis section 200)
The analysis unit 200 performs predetermined analysis processing on the image data of the eye to be examined formed by the image forming unit 120 (or the image data of the eye to be examined acquired by the ophthalmological apparatus 10). The analysis processing according to some embodiments includes identification of an image region corresponding to inflammatory cells or cell clusters, identification of the size of the identified image region, and classification into a predetermined classification type based on the identified size. This includes processing, distribution information generation processing, etc.

The analysis section 200 includes a region specifying section 210, a region connecting section 220, a size specifying section 230, a classification section 240, and a distribution information generating section 250. In this embodiment, it is assumed that the region specifying unit 210 specifies an image region corresponding to inflammatory cells or the like in each of the plurality of B-scan images. It is assumed that the three-dimensional image generation unit 131 forms volume data from a plurality of B-scan images in which image regions corresponding to inflammatory cells and the like are identified by the area identification unit 210. It is assumed that at least the region connecting section 220 performs the processing described below on the volume data formed by the three-dimensional image generating section 131.

In some embodiments, the functionality of analysis unit 200 is implemented by one or more processors. In some embodiments, the functions of each part of the analysis unit 200 are implemented by one or more processors.

(Area identification unit 210)
The region identifying unit 210 identifies one or more image regions corresponding to inflammatory cells or cell clusters from a B-scan image (OCT image) of the fundus (or anterior segment) of the eye to be examined. The region specifying section 210 includes an analysis range specifying section 211, a noise removing section 212, and a binarization processing section 213.

(Analysis range identification unit 211)
The analysis range specifying unit 211 specifies a predetermined layer region depicted in the B-scan image through segmentation processing, and specifies the analysis range of the image region based on the specified layer region. Specifically, the analysis range specifying unit 211 specifies a plurality of layer regions in the A-scan direction based on the OCT data or B-scan image of the eye to be examined acquired by the ophthalmological apparatus 10, and extracts a plurality of layer regions from the specified layer regions. Identify the predetermined layer area.

The analysis range specifying unit 211 according to some embodiments specifies a plurality of partial data sets corresponding to a plurality of tissues of the eye to be examined by analyzing three-dimensional OCT data. For example, the analysis range identifying unit 211 determines the gradient of pixel values (luminance values) in each A-scan image included in the OCT data, and identifies a position where the gradient is large as a tissue boundary. Note that the A-scan image is one-dimensional image data extending in the depth direction of the fundus. The depth direction of the fundus is defined as, for example, the Z direction, the direction of incidence of measurement light, the direction of the measurement optical axis, the direction of the optical axis of the interference optical system, and the like.

In a typical example, the analysis range specifying unit 211 specifies a plurality of partial data sets corresponding to a plurality of layer tissues of the fundus by analyzing OCT data representing the fundus (retina, choroid, etc.) and the vitreous body. . Each partial data set is defined by the boundaries of the layer organization. An example of a layered tissue specified as a partial data set is a layered tissue that constitutes a retina. The layered tissues constituting the retina include the inner limiting membrane, the nerve fiber layer, the ganglion cell layer, the inner plexiform layer, the inner nuclear layer, the outer plexiform layer, the outer nuclear layer, the outer limiting membrane, the photoreceptor layer, and the RPE. The analysis range identification unit 211 can identify partial data sets corresponding to Bruch's membrane, choroid, sclera, vitreous body, and the like. The analysis range specifying unit 211 according to some embodiments specifies a partial data set corresponding to a lesion. Examples of lesions include avulsions, edema, hemorrhages, tumors, and drusen.

The analysis range specifying unit 211 according to some embodiments specifies a layer structure corresponding to a predetermined number of pixels on the sclera side with respect to the RPE as Bruch's membrane, and sets a partial data set corresponding to the layer structure as Bruch's membrane. Obtain as a partial dataset.

The analysis range specifying unit 211 specifies a shallow layer area as an analysis range from a position shifted by a predetermined number of pixels in the shallow layer direction (progressing direction of the return light of the measurement light) from the specified predetermined layer area.

FIG. 5 shows an explanatory diagram of the operation of the analysis range specifying section 211. FIG. 5 shows an example of a B-scan image of the fundus of the eye to be examined.

For example, the analysis range identifying unit 211 analyzes the B-scan image IMG0 to identify the inner limiting membrane (ILM) LR. The analysis range specifying unit 211 specifies, as the analysis range AR, a region of the shallow layer whose boundary is a position shifted in the shallow layer direction by a predetermined number of pixels pn from the specified inner limiting membrane LR. The number of pixels pn may range from 5 to 30, for example.

The analysis range specifying unit 211 corrects the B-scan image IMG0 so that pixel values in regions deeper than the analysis range AR become predetermined values, and generates an analysis range specifying image. Examples of the predetermined value include a minimum pixel value (for example, "0"), a maximum pixel value, and a value indicating that the pixel value is outside the analysis range. Here, the analysis range specifying unit 211 corrects the pixel value of a region deeper than the analysis range AR to "0", and performs an analysis in which an image region corresponding to inflammatory cells or cell clusters is depicted within the analysis range. Generate a range specific image. This makes it possible to exclude the vicinity of the internal limiting membrane LR from the analysis range, and it becomes possible to improve the accuracy of identifying the image region corresponding to inflammatory cells or cell clusters infiltrating the vitreous body.

(Noise removal unit 212)
The noise removal unit 212 removes noise components in the analysis range specified by the analysis range identification unit 211. Specifically, the noise removal unit 212 performs a predetermined filtering process on the analysis range specifying image generated as a result of the analysis range specifying process by the analysis range specifying unit 211 to remove noise components. Generate a denoised image.

The predetermined filtering process includes a filtering process using a smoothing filter such as a Gaussian filter, a filtering process using an edge-preserving smoothing filter such as a bilateral filter, and the like. In particular, by applying an edge-preserving smoothing filter to the analysis range specific image, a noise-removed image is created in which background noise components are removed while leaving the edges of the image area corresponding to small inflammatory cells or cell clusters. It becomes possible to obtain.

(Binarization processing unit 213)
The binarization processing unit 213 performs binarization processing on the noise-removed image from which noise components have been removed by the noise removal unit 212. Specifically, the binarization processing unit 213 binarizes the noise-removed image based on the threshold value. In the binarization process, for example, the pixel value of the image area corresponding to inflammatory cells or cell clusters becomes the maximum value, and the pixel value of other image areas becomes the minimum value (for example, the pixel value of the image area corresponding to the inflammatory cells or cell clusters becomes the minimum value (for example, the pixel value of the image area corresponding to the value). The threshold value may be a predetermined value or a value determined according to the noise-removed image.

For example, the binarization processing unit 213 identifies the distribution of pixel values of the noise-removed image by analyzing the noise-removed image, and determines a threshold value for the binarization process based on the identified pixel value distribution. , binarize the noise-removed image based on the determined threshold. Specifically, the binarization processing unit 213 calculates one or more statistical values from the distribution of pixel values of the noise-removed image, and determines a threshold value from the calculated one or more statistical values. Statistical values include the mean, median, mode, standard deviation, and coefficient of variation.

At this time, the binarization processing unit 213 applies the above-mentioned method to the distribution of pixel values in a predetermined range so that pixel values outside the analysis range corrected by the analysis range identification unit 211 are not used for calculating statistical values. It is possible to calculate statistical values. For example, the binarization processing unit 213 calculates the mode for the distribution of pixel values in a range excluding the minimum value "0" of pixel values in the area outside the analysis range. For example, the binarization processing unit 213 calculates the standard deviation for the distribution of pixel values in the range of standard deviation from 1 to a predetermined value. The predetermined value may be a value corresponding to the most frequent value.

Such a binarization processing unit 213 can, for example, calculate a threshold value from a polynomial whose variables are two or more calculated statistical values (for example, the mode and the standard deviation). The binarization processing unit 213 generates a binarized image by binarizing the noise-removed image based on a threshold value.

In some embodiments, the binarization processing unit 213 removes image regions whose size is clearly smaller than the inflammatory cells and the like to be analyzed in the binarized image. Specifically, the binarization processing unit 213 removes from the binarized image an area where pixels having the same pixel value are consecutive in the A-scan direction and are equal to or less than a predetermined number of pixels (for example, 3 pixels). For example, the binarization processing unit 213 sets the pixel value of an area where the pixel having the maximum pixel value is three or less consecutive pixels in the A-scan direction to the minimum value (for example, "0"). This makes it possible to exclude image regions whose size is clearly smaller than the inflammatory cells and the like to be analyzed, thereby improving the accuracy of identifying image regions corresponding to inflammatory cells or cell clusters.

In the following, it is assumed that in the image after binarization processing, the pixel value of pixels in the image area corresponding to inflammatory cells or cell clusters is the maximum value, and the pixel value of pixels in other image areas is the minimum value. explain.

The three-dimensional image generation unit 131 forms volume data by performing the above image processing on the plurality of binarized images generated by the binarization processing unit 213. The area connection unit 220, the size identification unit 230, the classification unit 240, and the distribution information generation unit 250 each perform their own processing using the formed volume data.

(Area connection section 220)
The region connection unit 220 three-dimensionally searches for connectivity of pixels with the same pixel value in the volume data generated by the three-dimensional image generation unit 131, and determines that pixels with the same pixel value are three-dimensionally continuous. Identify image regions. That is, the region connecting unit 220 can specify a three-dimensional image region drawn across two or more B-scan images adjacent in the B-scan direction.

For example, the region connection unit 220 performs three-dimensional connected component extraction processing (Connected-Component Labeling: CCL) processing on volume data. For example, the region connecting unit 220 searches for pixels having the same pixel value as a predetermined pixel in the A-scan direction and in directions orthogonal to (crossing) the A-scan direction (i.e., six directions), and searches for pixels having the same pixel value as a predetermined pixel. Identify the area where is continuous. The region connection unit 220 performs unique labeling on the specified region and connects the labeled regions. Note that the region connecting unit 220 may identify the image region using a clustering method such as a known contour tracking process, a snake method, or a mean shift method.

(Size identification unit 230)
The size specifying unit 230 specifies the size of each of the one or more image regions specified by the area connecting unit 220. The size specifying unit 230 can specify the size of at least one of the one or more image areas specified by the area connecting unit 220 from the length of the image area in the A-scan direction. This makes it possible to identify the size of the image area from the length of the image area in the A-scan direction, even if the image area is difficult to identify due to the lateral resolution of OCT.

FIGS. 6 and 7 are diagrams illustrating the operation of the size specifying unit 230. FIG. 6 schematically shows a first processing example of the size identification process performed by the size identification unit 230. FIG. 7 schematically shows a second processing example of the size identification process performed by the size identification unit 230.

For example, the size specifying unit 230 specifies the contour for each of the one or more image areas specified by the area connecting unit 220, and calculates the length of the longest part in the A-scan direction of the specified contour in the image area. Specify as the size of. Specifically, as shown in FIG. 6, the size specifying unit 230 specifies the length SZ1 of the longest part in the A-scan direction in the image area PC1 specified by the area connecting unit 220 as the size of the image area PC1. .

For example, the size specifying unit 230 specifies, for each of the one or more image areas specified by the area connecting unit 220, a rectangular area (bounding box) surrounding the image area, and Specify the length of as the size of the image area. The short side or long side of the rectangle is a side parallel to the A-scan direction. Specifically, as shown in FIG. 7, the size specifying unit 230 specifies an area BX surrounding the image area PC2 specified by the area connecting unit 220, and calculates the length SZ2 of the specified area BX in the A-scan direction. is specified as the size of the image area PC2.

As described above, the size specifying unit 230 specifies the size of each of the one or more image regions specified by the area connecting unit 220.

(Classification unit 240)
The classification unit 240 classifies one or more image regions specified by the size identification unit 230 into a predetermined classification type based on the specified size. For example, the classification unit classifies one or more image regions into two or more classification types corresponding to two or more inflammatory cells. The two or more classification types include the first classification type corresponding to inflammatory cells or cell clusters with a diameter of 20 micrometers or more, the second classification type corresponding to inflammatory cells or cell clusters with a diameter of about 15 micrometers, and the second classification type corresponding to inflammatory cells or cell clusters with a diameter of about 15 micrometers. Includes the third classification type corresponding to inflammatory cells or cell clusters of 10 micrometers. Specifically, the first classification type is a classification type corresponding to monocytes. The second classification type is a classification type corresponding to neutrophils. The third classification type is a classification type corresponding to lymphocytes.

FIG. 8 shows an explanatory diagram of the operation of the classification section 240. In FIG. 8, the vertical direction schematically represents a group of consecutive pixels with the maximum pixel value in the A-scan direction, and the horizontal direction represents the number of consecutive pixels with the maximum pixel value in the A-scan direction. Expressed schematically as the number of consecutive pixels. Here, the number of pixels corresponds to the size specified by the size specifying section 230.

In this embodiment, when the number of consecutive pixels is 1 to 3, the classification unit 240 excludes from the classification target a pixel group in which pixels px having the maximum pixel value are consecutive in the A-scan direction. Alternatively, as described above, the binarization processing unit 213 sets the pixel value of an area where the pixel having the maximum pixel value is 3 or less consecutive pixels in the A-scan direction to the minimum value, so that the classification unit 240 excluded from processing.

When the number of consecutive pixels is 4 or 5, the classification unit 240 classifies a pixel group in which pixels px having the maximum pixel value are consecutive in the A-scan direction into the third classification type CL3. For example, when the number of consecutive pixels is 4 or 5, it is assumed that the size of the image area specified by the size specifying unit 230 corresponds to 10 to 13 micrometers. At this time, the classification unit 240 determines that the size specified by the size identification unit 230 corresponds to lymphocytes, and classifies the image area into the third classification type CL3.

When the number of consecutive pixels is 6 or 7, the classification unit 240 classifies the pixel group in which pixels px having the maximum pixel value are consecutive in the A-scan direction into the second classification type CL2. For example, when the number of consecutive pixels is 6 or 7, it is assumed that the size of the image area specified by the size specifying unit 230 corresponds to 14 to 16 micrometers. At this time, the classification unit 240 determines that the size specified by the size identification unit 230 corresponds to a neutrophil, and classifies the image area into the second classification type CL2.

When the number of consecutive pixels is 8 or more, the classification unit 240 classifies a pixel group in which pixels px having the maximum pixel value are consecutive in the A-scan direction into the first classification type CL1. For example, when the number of consecutive pixels is 8 or more, it is assumed that the size of the image area specified by the size specifying unit 230 corresponds to 20 micrometers or more. At this time, the classification unit 240 determines that the size specified by the size identification unit 230 corresponds to a monocyte, and classifies the image area into the first classification type CL1.

(Distribution information generation unit 250)
The distribution information generation unit 250 generates distribution information using the classification result of the image area by the classification unit 240. The distribution information includes a histogram representing the distribution of the number of classified image regions, a distribution image representing the distribution of the positions of the classified image regions, and the like.

Such a distribution information generation section 250 includes a histogram generation section 251 and a distribution image generation section 252.

(Histogram generation unit 251)
The histogram generation unit 251 generates a histogram as distribution information representing the distribution of the number of one or more image regions classified by the classification unit 240. The histogram generation unit 251 generates histogram information for displaying the above-mentioned histogram. For example, the histogram generation unit 251 counts the number of image areas for each classification type using the classification result of the image area by the classification unit 240, and generates histogram information representing the distribution of the number for each classification type. The display control unit 111A can display a histogram on a display unit (for example, the display device 190), not shown, based on the histogram information.

FIG. 9 shows an explanatory diagram of the operation of the histogram generation section 251. FIG. 9 shows an example of a histogram generated by the histogram generation unit 251. In FIG. 9, the vertical axis represents the number of objects for each classification type, and the horizontal axis represents the classification type. In FIG. 9, the first classification type is monocytes, the second classification type is neutrophils, and the third classification type is lymphocytes.

By displaying a histogram such as the one shown in FIG. 9 on the display unit, the distribution of the number of inflammatory cells can be easily understood. For example, when it is determined that the number of cells of the third classification type is large, it means that there are many lymphocytes infiltrating into the eye, and the possibility of uveitis derived from lymphoma is considered. In addition, if the number of Type 1 cells is determined to be large, it means that there are many monocytes infiltrating into the eye, and considering that macrophage inflammation is strong, there is a possibility of infectious uveitis. Conceivable. In addition, if the number of neutrophils in the second classification type is judged to be large, it means that there are many neutrophils infiltrating into the eye, and it is possible that the uveitis is caused by something other than lymphoma-derived uveitis or infectious uveitis. Possible gender.

(Distribution image generation unit 252)
The distribution image generation unit 252 generates a distribution image as distribution information representing the distribution of positions of one or more image areas classified by the classification unit 240. Specifically, the distribution image generation unit 252 generates a distribution image including an image in which one or more classified image regions are superimposed on the OCT image to be analyzed. For example, the distribution image generation unit 252 acquires position information in the volume data of the image area specified by the area identification unit 210 or the image area connected by the area connection unit 220, and uses the acquired position information to generate an OCT image. A distribution image is generated by specifying a position in the center and arranging an image area at the specified position.

In some embodiments, the distribution image generation unit 252 generates a distribution image in an identifiable manner for each classification type classified by the classification unit 240. For example, the distribution image generation unit 252 generates a distribution image in which the color, brightness, or display mode of the image area is different for each classification type. For example, the distribution image generation unit 252 generates a displayable distribution image with different blinking states for each classification type.

In some embodiments, the distribution image generation unit 252 generates a distribution image that includes an image region with a size corresponding to the size specified by the size identification unit 230. As a result, each type of inflammatory cells infiltrating into the eye is depicted in a distribution image with a different size, making it easier to understand the distribution of multiple types of inflammatory cells.

In some embodiments, the distribution image generation unit 252 generates a distribution image that includes an image region that can be identified at a predetermined size, regardless of the size specified by the size identification unit 230. This makes it possible to understand the distribution of multiple types of inflammatory cells, regardless of the size of the inflammatory cells, even if the inflammatory cells infiltrating into the eye are small and difficult to identify in a distribution image.

The display control unit 111A can display the distribution image generated as described above on a display unit (not shown, for example, the display device 190).

FIGS. 10 and 11 are diagrams illustrating the operation of the distribution image generation section 252. FIG. 10 shows an example of a three-dimensional distribution image of the fundus. FIG. 11 shows an example of a two-dimensional distribution image of the fundus.

As shown in FIG. 10, the distribution image generation unit 252 generates a distribution image IMG1 by arranging image regions pc1 to pc3 of the first to third classification types in volume data. The display control unit 111A displays the generated distribution image IMG1 on the display unit.

Further, as shown in FIG. 11, the distribution image generation unit 252 generates a distribution image IMG2 by arranging image regions pc1 to pc3 of the first to third classification types in a two-dimensional tomographic image. The display control unit 111A displays the generated distribution image IMG2 on the display unit.

In some embodiments, the display control unit 111A selectively displays image regions pc1 to pc3 of the first to third classification types on the display unit. For example, the display control unit 111A can cause the display unit to display an image area of a classification type specified by the user using the operating device 180. For example, in the distribution images IMG1 and IMG2 shown in FIG. 10 or FIG. It is possible to display it.

Further, the display control unit 111A can display the histogram shown in FIG. 9 on the display unit so as to be superimposed on the distribution images IMG1 and IMG2 shown in FIG. 10 or 11. For example, the display control unit 111A can cause the display unit to identifiably display the number of image areas of the same classification type as the image area specified by the user using the operating device 180 in the distribution images IMG1 and IMG2. It is.

In some embodiments, the histogram generation unit 251 generates distribution information representing the distribution of the number of image regions corresponding to inflammatory cells or cell clusters within a region specified using the operating device 180 in the OCT image. generate.

In some embodiments, the analysis unit 200 performs analysis processing on one or more image regions classified by the classification unit 240. The analysis process includes a process of calculating the density (number of image areas per unit area) for each classification type, a process of identifying an area corresponding to the calculated density, and the like. This makes it possible to recognize regions with a high density of desired inflammatory cells and regions with a low density, thereby contributing to the diagnosis of uveitis.

[Operation example]
Operation examples of the ophthalmological information processing apparatus 100 according to some embodiments will be described.

FIG. 12 shows an example of the operation of the ophthalmological information processing apparatus 100 according to the embodiment. FIG. 12 shows a flow diagram of an example of the operation of the ophthalmological information processing apparatus 100. A computer program for implementing the process shown in FIG. 12 is stored in the storage unit 112. The main control unit 111 executes the processing shown in FIG. 12 by operating according to this computer program.

In the following, it is assumed that OCT measurement is performed by the ophthalmological apparatus 10 and OCT data or an OCT image of the eye to be examined has already been acquired.

(S1: Obtain OCT image)
First, the main control unit 111 acquires an OCT image of the eye to be examined.

Specifically, the main control unit 111 controls the communication unit 140 to obtain OCT data or an OCT image obtained by the ophthalmologic apparatus 10 . When OCT data is acquired from the ophthalmological apparatus 10, the main control unit 111 can form an OCT image by controlling the image forming unit 120.

(S2: Identify the analysis range)
Next, the main control unit 111 controls the analysis range specifying unit 211 to specify the analysis range in the OCT image (tomographic image) acquired in step S1 as described above.

Specifically, the main control unit 111 controls the analysis range specifying unit 211 to specify a region corresponding to the internal limiting membrane in the OCT image acquired in step S1 through segmentation processing. As described above, the analysis range specifying unit 211 generates an analysis range specifying image whose analysis range is a region of the shallow layer whose boundary is a position shifted toward the shallow layer by a predetermined number of pixels pn from the specified internal limiting membrane LR. generate.

(S3: Noise removal)
Next, the main control unit 111 controls the noise removal unit 212 to perform noise removal processing on the analysis range image generated in step S2 as described above.

The noise removal unit 212 generates a noise removed image as described above.

(S4: Binarization processing)
Next, the main control unit 111 controls the binarization processing unit 213 to perform binarization processing on the noise-removed image generated in step S3.

The binarization processing unit 213 generates a binarized image as described above.

(S5: Connect areas)
Next, the main control unit 111 controls the area connection unit 220 to connect pixels having pixel values corresponding to inflammatory cells or cell clusters, thereby specifying an image area corresponding to inflammatory cells or the like.

Specifically, the main controller 111 controls the three-dimensional image generator 131 to form volume data. The main control unit 111 controls the area connection unit 220 to specify image areas corresponding to inflammatory cells and the like in the formed volume data.

Note that when identifying an image region corresponding to inflammatory cells etc. in a tomographic image, the main control unit 111 controls the region connecting unit 220 to identify inflammatory cells in the binarized image generated in step S4. etc., to specify the image area corresponding to the image area.

(S6: Specify size)
Next, the main control unit 111 controls the size specifying unit 230 to specify the size of the one or more image areas specified in step S5.

The size specifying unit 230 specifies the size of the image area based on the size of the image area in the A-scan direction as described above.

(S7: Classification)
Next, the main control unit 111 controls the classification unit 240 to classify the one or more image regions whose sizes were specified in step S6.

The classification unit 240 classifies the image area specified in step S5 into first to third classification types based on the size specified in step S6. Note that the classification unit 240 can exclude from the separation target image regions whose size is clearly smaller than the inflammatory cells and the like to be analyzed.

(S8: Generate distribution information)
Next, the main control unit 111 controls the distribution information generation unit 250 to generate distribution information of the image area specified in step S5 based on the classification result in step S7.

The distribution information generation unit 250 generates a histogram as shown in FIG. 9 and a distribution image as shown in FIG. 10 or 11.

This completes the series of processing (end).

As described above, according to the embodiment, it is desired to identify regions corresponding to inflammatory cells or cell clusters by analyzing OCT images, so inflammatory cells and other cells useful for assisting diagnosis of uveitis etc. can be identified. It becomes possible to quantitatively evaluate the

In particular, by using OCT images, it becomes possible to non-invasively identify the distribution of inflammatory cells, etc., and it becomes possible to quantitatively evaluate the subsidence and relapse of inflammation in uveitis during treatment. become.

In addition, we used the length in the A-scan direction of OCT to identify the size of the image area corresponding to inflammatory cells, etc. in the OCT image. It becomes possible to quantitatively evaluate cells, etc.

<Modified example>
Configurations according to some embodiments are not limited to the above configurations.

In addition to the functions of the ophthalmologic apparatus 10, the ophthalmologic apparatus according to some embodiments includes at least one of the functions of the ophthalmologic information processing apparatus 100, the operation device 180, and the display device 190.

Hereinafter, ophthalmologic apparatuses according to some modified examples of the embodiments will be described, focusing on the differences from the ophthalmologic apparatuses according to the above-described embodiments.

FIG. 13 shows a block diagram of a configuration example of an ophthalmologic apparatus 10a according to a modification of the embodiment. In FIG. 13, parts similar to those in FIG. 2 are denoted by the same reference numerals, and description thereof will be omitted as appropriate.

The configuration of the ophthalmologic apparatus 10a according to this modification differs from the configuration of the ophthalmologic apparatus 10 according to the above-described embodiments in that the ophthalmologic apparatus 10a has the functions of the ophthalmologic information processing apparatus 100, the functions of the operating device 180, and the display device. It has 190 functions. The ophthalmologic apparatus 10a includes a control section 11a, a data collection section 12, an image forming section 13, an ophthalmology information processing section 15a, an operation section 16a, and a display section 17a.

The control unit 11a controls each part of the ophthalmologic apparatus 10a. In particular, the control section 11a controls the data collection section 12, the image forming section 13, the ophthalmological information processing section 15a, the operation section 16a, and the display section 17a.

The ophthalmological information processing unit 15a has the same configuration as the ophthalmological information processing apparatus 100 and has the same functions as the ophthalmological information processing apparatus 100. The operating unit 16a has the same configuration as the operating device 180 and has the same functions as the operating device 180. The display unit 17a has the same configuration as the display device 190 and has the same functions as the display device 190.

According to this modification, it is possible to provide an ophthalmologic apparatus that has a compact configuration and is capable of quantitatively evaluating the distribution of an image area corresponding to inflammatory cells or cell clusters.

<Effect>
Hereinafter, effects of the ophthalmologic information processing apparatus, ophthalmologic apparatus, ophthalmologic information processing method, and program according to some embodiments will be described.

The ophthalmological information processing device (100) according to some embodiments includes a region specifying section (252) and a size specifying section (230). The region identifying unit identifies one or more image regions in the OCT image obtained by performing optical coherence tomography on the eye to be examined. The size specifying unit specifies at least one size of the one or more image areas from the length of the image area in the A-scan direction.

According to such a configuration, at least one size of one or more image regions in the OCT image of the eye to be examined is specified from the length of the image region in the A-scan direction. Thereby, it becomes possible to specify the size of an image region that cannot be identified due to the lateral resolution of OCT, and it becomes possible to evaluate with high precision the size of a small image region depicted in an OCT image.

In some embodiments, the size identifying unit identifies the size of the image area based on the length of the longest image area in the A-scan direction.

According to this configuration, the size of the image area is specified from the length of the longest part in the A-scan direction, so the size of the small image area depicted in the OCT image can be determined with high precision using simple processing. It becomes possible to evaluate

In some embodiments, the size identifying unit identifies the size of the image area from the length of the area surrounding the image area in the A-scan direction.

According to this configuration, the size of the image area is specified from the length of the area surrounding the image area in the A-scan direction, so the size of the small image area depicted in the OCT image can be determined with simple processing. It becomes possible to evaluate with high accuracy.

In some embodiments, the region identifying unit identifies one or more image regions corresponding to cells or cell clusters depicted in the OCT image.

According to such a configuration, it becomes possible to evaluate with high precision the size of an image area corresponding to a cell or a cell mass depicted in an OCT image.

Some embodiments include a classifier (240) that classifies one or more image regions based on the size of the image region identified by the size determiner.

According to such a configuration, it is possible to classify the size of a small image area depicted in an OCT image into a plurality of classification types, and to evaluate the image area for each classification type.

In some embodiments, the OCT image is an image in which at least a portion of the vitreous body is visualized, and the classification unit includes one or more classification types corresponding to two or more inflammatory cells infiltrating the vitreous body. Classify image regions.

According to such a configuration, it is possible to highly accurately evaluate two or more inflammatory cells infiltrating the vitreous body depicted in an OCT image.

In some embodiments, the two or more classification types include a first classification type corresponding to monocytes, a second classification type corresponding to neutrophils, and a third classification type corresponding to lymphocytes.

According to such a configuration, it is possible to distinguish and evaluate single cells, neutrophils, and lymphocytes that infiltrate the vitreous body, thereby making it possible to provide information useful for assisting in the diagnosis of uveitis.

Some embodiments include a distribution information generation unit (250) that generates distribution information of the number or position of one or more image regions classified by the classification unit.

According to such a configuration, it becomes possible to easily grasp the number or position distribution of small image regions depicted in an OCT image.

Some embodiments include a distribution information generation unit (250, distribution image generating section 252).

According to such a configuration, it becomes possible to easily understand small image regions depicted in an OCT image for each classification type.

Some embodiments include a display control unit (111A) that causes a display unit (display device 190) to display distribution information.

According to such a configuration, it is possible to provide an ophthalmological information processing apparatus that can easily understand small image areas depicted in an OCT image for each classification type.

In some embodiments, the OCT image is a three-dimensional image, and the size identification section determines the length in the A-scan direction of a three-dimensional image region drawn across two or more adjacent B-scan images. The size of the three-dimensional image area is specified from

According to such a configuration, it is possible to highly accurately evaluate the size of a small three-dimensional image region depicted in an OCT image.

Some embodiments include an image forming unit (120) that forms an OCT image based on OCT data obtained by performing optical coherence tomography on a subject's eye.

According to such a configuration, by acquiring OCT data, it is possible to provide an ophthalmologic information processing apparatus that can highly accurately evaluate the size of a small image area depicted in an OCT image.

The ophthalmological apparatus (10) according to some embodiments includes an optical system (interference optical system 12A and scan optical system 12B) that acquires OCT data by performing optical coherence tomography on a subject's eye, and the above-described optical system. and an ophthalmological information processing device.

According to such a configuration, it is possible to provide an ophthalmologic apparatus that can evaluate the size of a small image area with high precision using OCT.

An ophthalmological information processing method according to some embodiments includes a region specifying step of specifying one or more image regions in an OCT image obtained by performing optical coherence tomography on a subject's eye, and an A-scan direction of the image region. a size specifying step of specifying the size of at least one of the one or more image regions from the length of the image area.

According to such a method, at least one size of one or more image regions in an OCT image of the eye to be examined is specified from the length of the image region in the A-scan direction. Thereby, it becomes possible to specify the size of an image region that cannot be identified due to the lateral resolution of OCT, and it becomes possible to evaluate with high precision the size of a small image region depicted in an OCT image.

In some embodiments, the step of determining the size determines the size of the image region from the length of the longest image region in the A-scan direction.

According to this method, the size of the image area is specified from the length of the longest part in the A-scan direction, so the size of the small image area depicted in the OCT image can be determined with high precision using simple processing. It becomes possible to evaluate

In some embodiments, the size identifying step identifies the size of the image area from the length of the area surrounding the image area in the A-scan direction.

According to this method, the size of the image area is specified from the length of the area surrounding the image area in the A-scan direction, so the size of the small image area depicted in the OCT image can be determined with simple processing. It becomes possible to evaluate with high accuracy.

In some embodiments, the region identifying step identifies one or more image regions that correspond to cells or cell clusters depicted in the OCT image.

According to such a method, it becomes possible to evaluate with high precision the size of an image area corresponding to a cell or a cell mass depicted in an OCT image.

Some embodiments include a classification step that classifies one or more image regions based on the size of the image region identified in the size determination step.

According to such a method, it is possible to classify the size of a small image region depicted in an OCT image into a plurality of classification types, and to evaluate the image region for each classification type.

In some embodiments, the OCT image is an image depicting at least a portion of the vitreous, and the classification step includes classifying one or more of the two or more classification types corresponding to the two or more inflammatory cells infiltrating the vitreous. Classify image regions.

According to such a method, it is possible to highly accurately evaluate two or more inflammatory cells infiltrating the vitreous body depicted in an OCT image.

In some embodiments, the two or more classification types include a first classification type corresponding to monocytes, a second classification type corresponding to neutrophils, and a third classification type corresponding to lymphocytes.

According to such a method, it is possible to distinguish and evaluate single cells, neutrophils, and lymphocytes that infiltrate the vitreous, and thus it becomes possible to provide information useful for assisting in the diagnosis of uveitis.

Some embodiments include a distribution information generation step that generates distribution information of the number or position of one or more image regions classified in the classification step.

According to such a method, it becomes possible to easily understand the number or position distribution of small image regions depicted in an OCT image.

Some embodiments include a distribution information generation step of generating distribution information including an image in which one or more image regions are superimposed on the OCT image in an identifiable manner for each classification type classified in the classification step.

According to such a method, it becomes possible to easily understand small image areas depicted in an OCT image for each classification type.

Some embodiments include a display control step of displaying the distribution information on the display means.

According to such a method, it is possible to provide an ophthalmological information processing method that allows easy understanding of small image regions depicted in OCT images for each classification type.

In some embodiments, the OCT image is a three-dimensional image, and the size determination step includes determining the length in the A-scan direction of a three-dimensional image region drawn across two or more adjacent B-scan images. The size of the three-dimensional image area is specified from

According to such a method, it is possible to highly accurately evaluate the size of a small three-dimensional image region depicted in an OCT image.

Some embodiments include an image forming step of forming an OCT image based on OCT data obtained by performing optical coherence tomography on the subject's eye.

According to such a method, by acquiring OCT data, it is possible to provide an ophthalmological information processing method that allows highly accurate evaluation of the size of a small image region depicted in an OCT image.

Some embodiments are programs that cause a computer to execute each step of any of the ophthalmological information processing methods described above.

According to such a program, a computer can specify at least one size of one or more image regions in an OCT image of an eye to be examined from the length of the image region in the A-scan direction. Thereby, it becomes possible to specify the size of an image region that cannot be identified due to the lateral resolution of OCT, and it becomes possible to evaluate with high precision the size of a small image region depicted in an OCT image.

A program for realizing the ophthalmological information processing method according to some embodiments can be stored in any computer-readable recording medium (for example, a non-transitory recording medium). The recording medium may be an electronic medium using magnetism, light, magneto-optical, semiconductor, or the like. Typically, the recording medium is a magnetic tape, magnetic disk, optical disk, magneto-optical disk, flash memory, solid state drive, or the like.

It is also possible to send and receive computer programs through networks such as the Internet and LAN.

The embodiments described above are merely examples for implementing the invention. Those who wish to carry out this invention can make any modifications (omissions, substitutions, additions, etc.) within the scope of the gist of this invention.

1 Ophthalmology system 10, 10a Ophthalmology apparatus 11, 11a, 110 Control section 12 Data collection section 12A Interference optical system 12B Scanning optical system 13, 120 Image forming section 14, 140 Communication section 15 Ophthalmology information processing section 16a Operation section 17a Display section 100 Ophthalmological information processing device 111 Main control section 111A Display control section 112 Storage section 130 Data processing section 131 Three-dimensional image generation section 180 Operating device 190 Display device 200 Analysis section 210 Area specification section 211 Analysis range specification section 212 Noise removal section 213 Binary Converting processing unit 220 Area connection unit 230 Size identification unit 240 Classification unit 250 Distribution information generation unit 251 Histogram generation unit 252 Distribution image generation unit

Claims (22)

a region identifying unit that identifies one or more image regions in an OCT image obtained by performing optical coherence tomography on the eye to be examined;
a size specifying unit that specifies at least one size of the one or more image areas from the length of the image area in the A-scan direction;
including;
The region specifying unit is an ophthalmological information processing device that specifies the one or more image regions corresponding to cells or cell clusters depicted in the OCT image. a region identifying unit that identifies one or more image regions in an OCT image obtained by performing optical coherence tomography on the eye to be examined;
a size specifying unit that specifies at least one size of the one or more image areas from the length of the image area in the A-scan direction;
a classification unit that classifies the one or more image areas based on the size of the image area specified by the size identification unit;
including;
The OCT image is an image in which at least a part of the vitreous body is depicted,
The classification unit is an ophthalmological information processing device that classifies the one or more image regions into two or more classification types corresponding to two or more inflammatory cells infiltrating the vitreous body. a region identifying unit that identifies one or more image regions in an OCT image obtained by performing optical coherence tomography on the eye to be examined;
a size specifying unit that specifies at least one size of the one or more image areas from the length of the image area in the A-scan direction;
including;
The OCT image is a three-dimensional image,
The size specifying unit specifies the size of the three-dimensional image area based on the length in the A-scan direction of the three-dimensional image area drawn across two or more adjacent B-scan images. Device. The ophthalmological information processing device according to any one of claims 1 to 3, wherein the size specifying unit specifies the size of the image area based on the length of the longest image area in the A-scan direction. . The ophthalmological information processing according to any one of claims 1 to 3, wherein the size specifying unit specifies the size of the image area from the length of the area surrounding the image area in the A-scan direction. Device. Claim 2, wherein the two or more classification types include a first classification type corresponding to monocytes, a second classification type corresponding to neutrophils, and a third classification type corresponding to lymphocytes. The ophthalmological information processing device described. The ophthalmologic information processing apparatus according to claim 2 or 6, further comprising a distribution information generation section that generates distribution information on the number or position of the one or more image regions classified by the classification section. A claim further comprising: a distribution information generation unit that generates distribution information including an image in which the one or more image regions are superimposed on the OCT image in a manner that can be identified for each classification type classified by the classification unit. The ophthalmological information processing device according to claim 2 or claim 6 . The ophthalmologic information processing apparatus according to claim 7 or 8, further comprising a display control section that displays the distribution information on a display means. The eye according to any one of claims 1 to 9, further comprising an image forming unit that forms the OCT image based on OCT data obtained by performing optical coherence tomography on the eye to be examined. The ophthalmological information processing device described. an optical system that acquires OCT data by performing optical coherence tomography on the eye to be examined;
The ophthalmological information processing device according to claim 10 ;
ophthalmological equipment including; a region identifying step of identifying one or more image regions in the OCT image obtained by performing optical coherence tomography on the eye to be examined;
a size specifying step of specifying at least one size of the one or more image areas from the length of the image area in the A-scan direction;
including;
In the ophthalmological information processing method, the region specifying step specifies the one or more image regions corresponding to cells or cell clusters depicted in the OCT image . a region identifying step of identifying one or more image regions in the OCT image obtained by performing optical coherence tomography on the eye to be examined;
a size specifying step of specifying at least one size of the one or more image areas from the length of the image area in the A-scan direction;
a classification step of classifying the one or more image regions based on the size of the image region specified in the size identification step;
including;
The OCT image is an image in which at least a part of the vitreous body is depicted,
In the ophthalmological information processing method, the classification step classifies the one or more image regions into two or more classification types corresponding to two or more inflammatory cells infiltrating the vitreous body. a region identifying step of identifying one or more image regions in the OCT image obtained by performing optical coherence tomography on the eye to be examined;
a size specifying step of specifying at least one size of the one or more image areas from the length of the image area in the A-scan direction;
including;
The OCT image is a three-dimensional image,
The size specifying step includes ophthalmological information processing in which the size of the three-dimensional image area is specified from the length in the A-scan direction of the three-dimensional image area drawn across two or more adjacent B-scan images. Method. The ophthalmological information processing method according to any one of claims 12 to 14, wherein the size specifying step specifies the size of the image area from the length of the longest image area in the A-scan direction. . The ophthalmological information processing according to any one of claims 12 to 14 , wherein the size specifying step specifies the size of the image area from the length of the area surrounding the image area in the A-scan direction. Method. 14. The two or more classification types include a first classification type corresponding to monocytes, a second classification type corresponding to neutrophils, and a third classification type corresponding to lymphocytes. The ophthalmological information processing method described. The ophthalmologic information processing method according to claim 13 or 17, further comprising a distribution information generation step of generating distribution information of the number or position of the one or more image regions classified in the classification step. A claim characterized by comprising a distribution information generation step of generating distribution information including an image in which the one or more image regions are superimposed on the OCT image in an identifiable manner for each classification type classified in the classification step. The ophthalmological information processing method according to claim 13 or claim 17 . The ophthalmologic information processing method according to claim 18 or 19, further comprising a display control step of displaying the distribution information on a display means. The method according to any one of claims 12 to 20, further comprising an image forming step of forming the OCT image based on OCT data obtained by performing optical coherence tomography on the eye to be examined. The ophthalmological information processing method described. A program for causing a computer to execute each step of the ophthalmological information processing method according to any one of claims 12 to 21 .

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