Ophthalmological information processing device, ophthalmological device, ophthalmological information processing method, and program

Abstract

This ophthalmic information processing device includes an acquisition unit and a disease inference unit. The acquisition unit acquires a plurality of images, of an eye of a patient, that are different from each other in cross section direction. The disease inference unit, by using a plurality of trained models obtained by performing machine learning for the respective types of a plurality of images, outputs inference information for inferring whether or not an eye of a patient is a glaucomatous eye, from the plurality of images.

Description

[Technical Field]

[0001] The present invention relates to an ophthalmologic information processing device, an ophthalmologic apparatus, an ophthalmologic information processing method, and a program. [Background technology]

[0002] In recent years, rapid advances in machine learning techniques, such as deep learning, have led to the practical application of artificial intelligence technologies in a variety of fields. In particular, in the medical field, deep learning has improved the accuracy of detecting diseased areas or tissue conditions in diagnostic images, enabling rapid and accurate medical diagnoses.

[0003] For example, Patent Document 1 discloses a method for diagnosing glaucoma from a plurality of newly acquired time-series fundus images using machine learning of feature points in a plurality of past time-series fundus images as training data and using the obtained trained model.For example, Patent Document 2 discloses a method for classifying the pathology of glaucoma according to the shape of the optic disc using a neural network. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2018 / 211688 [Patent Document 2] Japanese Patent Application Publication No. 2019-5319 Summary of the Invention [Problem to be solved by the invention]

[0005] Glaucoma is a disease whose symptoms will progress unless appropriate treatment is administered. If glaucoma can be detected early through screening or other methods, it will be possible to suppress the progression of glaucoma by administering appropriate treatment. Therefore, there is a need for early detection of glaucoma with high accuracy and precision.

[0006] The present invention has been made in view of the above circumstances, and its purpose is to provide a new technique for detecting glaucoma with high accuracy and precision in screening and the like. [Means for solving the problem]

[0007] A first aspect of the embodiment is an ophthalmological information processing device that includes an acquisition unit that acquires multiple images of a test eye having different cross-sectional directions, and a disease estimation unit that uses multiple trained models obtained by machine learning for each type of the multiple images to output estimation information for estimating whether the test eye is a glaucomatous eye from the multiple images.

[0008] In a second aspect of the embodiment, in the first aspect, the disease estimation unit includes a plurality of estimators that use each of the plurality of trained models for each type of image to output feature values ​​or certainty information representing a certainty that the test eye is a glaucomatous eye, and a classifier that uses a classification model obtained by machine learning to output the estimation information from the plurality of feature values ​​or the plurality of certainty information output from the plurality of estimators.

[0009] In a third aspect of the embodiment, in the first or second aspect, the plurality of images include a tomographic image or a front image of the fundus of the subject's eye.

[0010] In a fourth aspect of the embodiment, in the third aspect, the plurality of images include a first B-scan image in a first cross-sectional direction passing through the optic disc, a second B-scan image in a second cross-sectional direction passing through the optic disc and intersecting the first cross-sectional direction, and a third B-scan image passing through the fovea.

[0011] In a fifth aspect of the embodiment, in the fourth aspect, at least one of the first B scan image, the second B scan image, and the third B scan image is a B scan image obtained by cropping a range from a first depth position shifted in the depth direction by a first number of pixels toward the vitreous body based on a predetermined layer region in the fundus of the test eye to a second depth position shifted in the depth direction by a second number of pixels toward the choroidal body based on the predetermined layer region.

[0012] In a sixth aspect of the embodiment, in the fifth aspect, the specified layer region is a layer region shifted in the depth direction by a third number of pixels from the average depth position of the internal limiting membrane identified based on three-dimensional OCT data of the test eye.

[0013] In a seventh aspect of the embodiment, in any of the third to sixth aspects, the front image includes at least one of an en-face image projecting a layer area from a layer area corresponding to the internal limiting membrane to a deep layer area, and a projection image.

[0014] An eighth aspect of the embodiment is any of the first to seventh aspects, further comprising a learning unit that generates the plurality of trained models by supervised machine learning for each type of the plurality of images.

[0015] A ninth aspect of the embodiment is any one of the first to eighth aspects, further comprising an image generating unit that generates at least one of the plurality of images based on three-dimensional OCT data of the subject's eye.

[0016] A tenth aspect of the embodiment is an ophthalmic device including an OCT unit that performs optical coherence tomography on the subject's eye, an image generation unit that generates at least one of the plurality of images based on the three-dimensional data acquired by the OCT unit, and an ophthalmic information processing device described in any of the first to eighth aspects.

[0017] An eleventh aspect of the embodiment is an ophthalmological information processing method including an acquisition step of acquiring multiple images of a test eye having different cross-sectional directions, and a disease estimation step of outputting estimation information for estimating whether the test eye has glaucoma from the multiple images using multiple trained models obtained by machine learning for each type of the multiple images.

[0018] In a twelfth aspect of the embodiment, in the eleventh aspect, the disease estimation step includes a plurality of estimation steps that use each of the plurality of trained models for each type of the plurality of images to output feature quantities or certainty information representing a certainty that the test eye is a glaucomatous eye, and a classification step that uses a classification model obtained by machine learning to output the estimation information from the plurality of feature quantities or the plurality of certainty information output in the plurality of estimation steps.

[0019] In a thirteenth aspect of the embodiment, in the eleventh or twelfth aspect, the plurality of images include a tomographic image or a front image of the fundus of the subject's eye.

[0020] In a fourteenth aspect of the embodiment, in the thirteenth aspect, the plurality of images include a first B-scan image in a first cross-sectional direction passing through the optic disc, a second B-scan image in a second cross-sectional direction passing through the optic disc and intersecting the first cross-sectional direction, and a third B-scan image passing through the fovea.

[0021] In a fifteenth aspect of the embodiment, in the fourteenth aspect, at least one of the first B scan image, the second B scan image, and the third B scan image is a B scan image obtained by cropping a range from a first depth position shifted in the depth direction by a first number of pixels toward the vitreous body based on a predetermined layer region in the fundus of the test eye to a second depth position shifted in the depth direction by a second number of pixels toward the choroidal body based on the predetermined layer region.

[0022] In a sixteenth aspect of the embodiment, in the fifteenth aspect, the predetermined layer region is a layer region shifted in the depth direction by a third number of pixels from the average depth position of the internal limiting membrane identified based on three-dimensional OCT data of the test eye.

[0023] In a seventeenth aspect of the embodiment, in any of the thirteenth to sixteenth aspects, the front image includes at least one of an en-face image projecting a layer area from a layer area corresponding to the internal limiting membrane to a deep layer area, and a projection image.

[0024] An eighteenth aspect of the embodiment is based on any one of the eleventh to seventeenth aspects and further includes a learning step of generating the plurality of trained models by supervised machine learning for each type of the plurality of images.

[0025] A nineteenth aspect of the embodiment, in any of the eleventh to eighteenth aspects, includes an image generating step of generating at least one of the plurality of images based on three-dimensional OCT data of the subject's eye.

[0026] A twentieth aspect of the embodiment is a program for causing a computer to execute each step of the ophthalmologic information processing method according to any one of the eleventh to nineteenth aspects.

[0027] The configurations according to the above-described multiple aspects can be combined in any manner. [Effects of the Invention]

[0028] According to some embodiments of the present invention, it is possible to provide a new technique for detecting glaucoma with high accuracy and precision through screening or the like. [Brief explanation of the drawings]

[0029] [Figure 1] 1 is a schematic diagram illustrating an example of a configuration of an ophthalmologic system according to an embodiment. [Figure 2] 1 is a schematic diagram illustrating an example of a configuration of an ophthalmologic apparatus according to an embodiment. [Figure 3] 1 is a schematic diagram illustrating an example of a configuration of an ophthalmologic information processing apparatus according to an embodiment. [Figure 4] 1 is a schematic diagram illustrating an example of a configuration of an ophthalmologic information processing apparatus according to an embodiment. [Figure 5] 1 is a schematic diagram illustrating an example of a configuration of an ophthalmologic information processing apparatus according to an embodiment. [Figure 6] 1 is a schematic diagram illustrating an example of a configuration of an ophthalmologic information processing apparatus according to an embodiment. [Figure 7] 1 is a schematic diagram illustrating an example of a configuration of an ophthalmologic information processing apparatus according to an embodiment. [Figure 8] 1 is a schematic diagram illustrating an example of a configuration of an ophthalmologic information processing apparatus according to an embodiment. [Figure 9] FIG. 2 is a schematic diagram illustrating an example of an operation flow of the ophthalmologic information processing apparatus according to the embodiment. [Figure 10] FIG. 2 is a schematic diagram illustrating an example of an operation flow of the ophthalmologic information processing apparatus according to the embodiment. [Figure 11] FIG. 2 is a schematic diagram illustrating the operation of the ophthalmologic information processing apparatus according to the embodiment. [Figure 12] FIG. 10 is a schematic diagram illustrating an example of the configuration of an ophthalmologic apparatus according to a modified example of the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0030] Examples of an ophthalmological information processing device, an ophthalmological device, an ophthalmological information processing method, and a program according to some embodiments of the present invention will be described in detail with reference to the drawings. Note that matters described in documents cited in this specification and any known techniques can be incorporated into the embodiments.

[0031] An ophthalmic information processing device according to an embodiment is capable of performing predetermined analysis processing and predetermined display processing on three-dimensional optical coherence tomography (OCT) data of a subject's eye acquired by an ophthalmic device using OCT. An ophthalmic device according to some embodiments has not only the functionality of an OCT device for performing OCT, but also the functionality of at least one of a fundus camera, a scanning laser ophthalmoscope, a slit lamp microscope, and a surgical microscope. Furthermore, an ophthalmic device according to some embodiments has the functionality of measuring optical characteristics of a subject's eye. Examples of ophthalmic devices that have the functionality of measuring optical characteristics of a subject's eye include a refractometer, a keratometer, a tonometer, a wavefront analyzer, a specular microscope, and a perimeter. An ophthalmic device according to some embodiments has the functionality of a laser treatment device used for laser treatment.

[0032] The ophthalmologic information processing device generates multiple images (tomographic images or frontal images) with different cross-sectional directions from the acquired 3D OCT data of the test eye, and outputs estimation information (classification information) for estimating (classifying) whether the test eye is a glaucomatous eye (normal eye) from the multiple generated images.

[0033] Hereinafter, a case where the ophthalmological information processing device, ophthalmological apparatus, ophthalmological information processing method, and program according to the embodiments are applied to glaucoma diagnosis (classification) will be described. However, the application field of the ophthalmological information processing device, ophthalmological apparatus, ophthalmological information processing method, and program according to the embodiments is not limited to glaucoma diagnosis, and they can be applied to all diseases of the subject's eye. For example, diseases of the subject's eye include age-related macular degeneration, diabetic retinopathy, etc. in addition to glaucoma.

[0034] In this way, by providing a doctor or the like with estimated information indicating whether or not the subject's eye has glaucoma, it becomes possible to determine an effective treatment method for suppressing the progression of glaucoma at an early stage, thereby increasing the possibility of suppressing the progression of glaucoma.

[0035] For example, in a case where the ocular fundus disease is determined to be glaucoma, an appropriate treatment method can be selected from among drug therapy, laser treatment, surgery, etc. depending on the pathology of the disease.

[0036] The ophthalmologic information processing device according to the embodiment is capable of estimating whether or not a subject's eye has glaucoma by using a trained model obtained by machine learning for each of a plurality of images generated from 3D OCT data.

[0037] An ophthalmological system according to an embodiment includes an ophthalmological information processing device. An ophthalmological information processing method according to an embodiment is executed by the ophthalmological information processing device. A program according to an embodiment causes a computer (processor) to execute each step of the ophthalmological information processing method. A recording medium according to an embodiment is a non-transitory recording medium (storage medium) on which the program according to an embodiment is recorded.

[0038] Hereinafter, in this specification, a processor includes circuits such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), and a programmable logic device (e.g., an SPLD (Simple Programmable Logic Device), a CPLD (Complex Programmable Logic Device), and an FPGA (Field Programmable Gate Array)). The processor realizes the functions of the embodiment by, for example, reading and executing a program stored in a memory circuit or a storage device. The memory circuit or storage device may be included in the processor. Alternatively, the memory circuit or storage device may be provided external to the processor.

[0039] [Ophthalmology System] 1 shows a block diagram of an example of the configuration of an ophthalmologic system according to an embodiment. The ophthalmologic system 1 according to the embodiment includes an ophthalmologic apparatus 10, an ophthalmologic information processing device (ophthalmologic image processing device, ophthalmologic analysis device) 100, an operation device 180, and a display device 190.

[0040] The ophthalmic apparatus 10 performs OCT on the subject's eye and collects three-dimensional OCT data of the subject's eye. In this embodiment, the ophthalmic apparatus 10 collects fundus OCT data by OCT scanning the fundus of the subject's eye. The ophthalmic apparatus 10 can acquire multiple images of the fundus from the acquired fundus OCT data. The fundus images include tomographic images and frontal images of the fundus. The fundus tomographic images include B-scan images. The frontal images of the fundus include C-scan images, en-face images, shadowgrams, projection images, and the like. The ophthalmic apparatus 10 transmits the acquired OCT data of the subject's eye or data of the acquired images to the ophthalmic information processing apparatus 100.

[0041] In some embodiments, the ophthalmic apparatus 10 and the ophthalmic information-processing device 100 are connected via a data communication network. The ophthalmic information-processing device 100 according to some embodiments receives the above-described data from one of the plurality of ophthalmic apparatuses 10 selectively connected via the data communication network.

[0042] The ophthalmologic information processing device 100 generates first estimated information for estimating whether or not the subject's eye has glaucoma using an individual estimation model (trained model) obtained by machine learning for each of a plurality of images generated from the 3D OCT data. The ophthalmologic information processing device 100 generates second estimated information for estimating whether or not the subject's eye has glaucoma from the plurality of first estimated information generated for each of the plurality of images using a classification model (trained model) obtained by machine learning.

[0043] The following describes a case where five types of images are generated from three-dimensional OCT data. The five types of images are a horizontal B-scan image passing through the center of the optic disc (or its vicinity), a vertical B-scan image passing through the center of the optic disc (or its vicinity), a vertical B-scan image passing through the fovea (or its vicinity), a projection image, and an en-face image. However, the configuration according to the embodiment is not limited to the types and number of images generated from the three-dimensional OCT data. For example, a plurality of images including a B-scan image in a cross-sectional direction intersecting a line connecting the optic disc and the fovea may be generated from the three-dimensional OCT data, and the above-mentioned estimated information may be obtained based on the plurality of generated images.

[0044] In some embodiments, the ophthalmological information-processing device 100 constructs the above-mentioned trained model and outputs estimated information using the constructed trained model. In some embodiments, the ophthalmological information-processing device 100 outputs estimated information using an externally constructed trained model. In some embodiments, the ophthalmological information-processing device 100 constructs the above-mentioned trained model and outputs the constructed trained model to an external device.

[0045] The operation device 180 and the display device 190 function as a user interface unit, providing functions for exchanging information between the ophthalmologic information-processing device 100 and its user, such as displaying information, inputting information, and inputting operation instructions. The operation device 180 includes operation devices such as levers, buttons, keys, and pointing devices. In some embodiments, the operation device 180 includes a microphone for inputting information by sound. The display device 190 includes a display device such as a flat panel display. In some embodiments, the functions of the operation device 180 and the display device 190 are realized by a device that integrates a device having an input function and a device having a display function, such as a touch panel display. In some embodiments, the operation device 180 and the display device 190 include a graphical user interface (GUI) for inputting and outputting information.

[0046] [Ophthalmological equipment] FIG. 2 shows a block diagram of an example of the configuration of the ophthalmologic apparatus 10 according to the embodiment.

[0047] The ophthalmic apparatus 10 is provided with an optical system for acquiring OCT data of the subject's eye. The ophthalmic apparatus 10 has a function for performing swept-source OCT, but the embodiment is not limited thereto. For example, the type of OCT is not limited to swept-source OCT and may be spectral-domain OCT or the like. Swept-source OCT is a technique in which light from a wavelength-swept (wavelength-scanning) light source is split into measurement light and reference light, and the return light of the measurement light that has passed through the object to be measured (the subject's eye) interferes with the reference light to generate interference light. The interference light is detected using a balanced photodiode or the like, and an image is formed by applying a Fourier transform or the like to the detection data collected in response to the wavelength sweep and the scanning of the measurement light. Spectral-domain OCT is a technique in which light from a low-coherence light source is split into measurement light and reference light, and the return light of the measurement light that has passed through the object to be measured interferes with the reference light to generate interference light. The spectral distribution of the interference light is detected using a spectroscope, and an image is formed by applying a Fourier transform or the like to the detected spectral distribution.

[0048] The ophthalmologic apparatus 10 includes a control unit (controller) 11, an OCT unit 12, an OCT data processing unit 13, and a communication unit 14.

[0049] The control unit 11 controls each unit of the ophthalmologic apparatus 10. In particular, the control unit 11 controls the OCT unit 12, the OCT data processing unit 13, and the communication unit .

[0050] The OCT unit 12 collects three-dimensional OCT data of the subject's eye by scanning the subject's eye using OCT. The OCT unit 12 includes an interference optical system 12A and a scanning optical system 12B.

[0051] The interference optical system 12A splits light from a light source (a swept-wavelength light source) into measurement light and reference light, generates interference light by causing return light of the measurement light that has passed through the eye to interfere with the reference light that has passed through the reference optical path, and detects the generated interference light. The interference optical system 12A includes at least a fiber coupler and a photoreceiver such as a balanced photodiode. The fiber coupler splits light from the light source into measurement light and reference light, and generates interference light by causing return light of the measurement light that has passed through the eye to interfere with the reference light that has passed through the reference optical path. The photoreceiver detects the interference light generated by the fiber coupler. The interference optical system 12A may include a light source.

[0052] The scanning optical system 12B, under the control of the control unit 11, deflects the measurement light generated by the interference optical system 12A to change the incident position of the measurement light on the fundus of the subject's eye. The scanning optical system 12B includes, for example, an optical scanner disposed at a position that is optically approximately conjugate with the pupil of the subject's eye. The optical scanner includes, for example, a first galvanometer mirror that deflects the measurement light horizontally, a second galvanometer mirror that deflects the measurement light vertically, and mechanisms for independently driving these. For example, the second galvanometer mirror is configured to further deflect the measurement light deflected by the first galvanometer mirror. This allows the measurement light to scan in any direction on the fundus plane.

[0053] 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.

[0054] The OCT data processing unit 13 is controlled by the control unit 11 and forms three-dimensional data (image data) of the fundus based on the data of the subject's eye collected by the OCT unit 12.

[0055] For example, the OCT data processing unit 13 forms a reflection intensity profile for each A line, and arranges the formed multiple reflection intensity profiles in the B scan direction (a direction intersecting the A scan direction (e.g., z direction), e.g., x direction) and a direction intersecting the A scan direction and B scan direction (e.g., y direction) to form three-dimensional OCT data (scan data).

[0056] Furthermore, for example, the OCT data processing unit 13 can image the reflection intensity profile in the A-line to form an A-scan image of the subject's eye E. The OCT data processing unit 13 can form a three-dimensional image by arranging a plurality of A-scan images formed for each A-line in the B-scan direction and in a direction intersecting the A-scan direction and the B-scan direction.

[0057] The processes executed by the OCT data processor 13 include noise removal (noise reduction), filtering, FFT (Fast Fourier Transform), etc. The image data acquired in this manner is a data set including a group of image data formed by imaging the reflection intensity profiles of multiple A-lines (paths of each measurement light in the subject's eye). To improve image quality, multiple data sets collected by repeating the same pattern scan multiple times can be superimposed (averaged).

[0058] In some embodiments, the OCT data processor 13 performs various data processing (image processing) and analysis processing on the image. For example, the OCT data processor 13 performs correction processing such as image brightness correction and dispersion correction. The OCT data processor 13 can generate volume data (voxel data) of the subject's eye by performing known image processing such as interpolation processing that interpolates pixels between tomographic images. When displaying an image based on the volume data, the OCT data processor 13 performs rendering processing on the volume data to generate a pseudo-three-dimensional image as viewed from a specific line of sight.

[0059] Each of the control unit 11 and the OCT data processing unit 13 includes a processor. The functions of the control unit 11 are realized by a control processor. The functions of the OCT data processing unit 13 are realized by a data processing processor. In some embodiments, the functions of both the control unit 11 and the OCT data processing unit 13 are realized by a single processor.

[0060] As described above, the processor realizes the functions of the embodiments by, for example, reading and executing a program stored in a memory circuit or a storage device. At least a part of the memory circuit or the storage device may be included in the processor. Alternatively, at least a part of the memory circuit or the storage device may be provided external to the processor.

[0061] The storage device etc. stores various types of data. The data stored in the storage device etc. includes data acquired by the OCT unit 12 (measurement data, photographed data, etc.), information on the subject and the subject's eye, etc. The storage device etc. may store various computer programs and data for operating each unit of the ophthalmologic apparatus 10.

[0062] The communication unit 14 is controlled by the control unit 11 and executes a communication interface process for transmitting or receiving information to or from the ophthalmologic information-processing device 100 .

[0063] The ophthalmologic apparatus 10 according to some embodiments transmits data of the subject's eye formed by the OCT data processing unit 13 to the ophthalmologic information-processing device 100.

[0064] The ophthalmic apparatus 10 according to some embodiments includes a fundus camera, a scanning laser ophthalmoscope, and a slit lamp microscope for acquiring an image of the fundus of the subject's eye. In some embodiments, the fundus image acquired by the fundus camera is a fluorescein angiography image or a fundus autofluorescence image.

[0065] [Ophthalmology information processing device] 3 to 6 show block diagrams of configuration examples of the ophthalmologic information-processing device 100 according to the embodiment. FIG. 3 shows a functional block diagram of the ophthalmologic information-processing device 100. FIG. 4 shows a functional block diagram of the analysis unit 200 in FIG. 3. FIG. 5 shows a functional block diagram of the estimation model construction unit 210 in FIG. 3. FIG. 6 shows a functional block diagram of the estimation unit 220 in FIG. 3.

[0066] The ophthalmologic information-processing device 100 includes a control unit (controller) 110, a data processing unit 130, and a communication unit 140. In some embodiments, the ophthalmologic information-processing device 100 includes an image forming unit having the same function as the OCT data processing unit 13 of the ophthalmologic apparatus 10.

[0067] The control unit 110 controls each unit of the ophthalmologic information-processing device 100. In particular, the control unit 110 controls the data processing unit 130 and the communication unit 140. The control unit 110 includes a main control unit 111 and a storage unit 112.

[0068] The control unit 110 controls each unit of the ophthalmologic system 1 based on an operation instruction signal corresponding to the operation content of the operation device 180 by the user.

[0069] Each of the control unit 110 and the data processing unit 130 includes a processor. The functions of the data processing unit 130 are realized by the data processor. In some embodiments, the functions of the control unit 110 and the data processing unit 130 are realized by a single processor.

[0070] The storage unit 112 stores various types of data. The data stored in the storage unit 112 includes data acquired by the ophthalmologic apparatus 10 (measurement data, photographed data, etc.), data processing results by the data processing unit 130, information on the subject and the subject's eye, etc. The storage unit 112 may store various computer programs and data for operating each unit of the ophthalmologic information-processing device 100.

[0071] The communication unit 140 is controlled by the control unit 110 and executes communication interface processing for transmitting or receiving information to or from the communication unit 14 of the ophthalmologic information-processing device 100 .

[0072] The data processing unit 130 can generate an image of any specified cross-sectional direction by performing various rendering (or image generation processing) on ​​the 3D OCT data from the ophthalmologic apparatus 10. In this embodiment, the data processing unit 130 generates multiple images with different cross-sectional directions. The multiple images include B-scan images, C-scan images, en-face images, projection images, shadowgrams, etc. Images of any cross-section, such as B-scan images and C-scan images, are formed by selecting pixels (voxels) on a specified cross-section from the 3D OCT data (image data). En-face images are formed by flattening a portion of the 3D OCT data (image data). Projection images are formed by projecting the 3D OCT data in a specified direction (z direction, depth direction, A-scan direction). Shadowgrams are formed by projecting a portion of the 3D OCT data (e.g., partial data corresponding to a specific layer) in a specified direction.

[0073] In some embodiments, at least one of the cross-sectional position and the cross-sectional direction is specified by the user operating the operation device 180. The user can specify at least one of the cross-sectional position and the cross-sectional direction using the operation device 180 while referring to a frontal image of the fundus generated from three-dimensional OCT data or a fundus image acquired using a fundus camera (not shown).

[0074] In some embodiments, the data processing unit 130 identifies at least one of the cross-sectional position and the cross-sectional direction by analyzing the OCT data. In this case, the data processing unit 130 can identify a characteristic region by a known analysis process and identify at least one of the cross-sectional position and the cross-sectional direction so as to pass through the identified characteristic region. Alternatively, the data processing unit 130 can identify a characteristic region by a known analysis process and identify at least one of the cross-sectional position and the cross-sectional direction so as to avoid the identified characteristic region.

[0075] The data processing unit 130 performs predetermined data processing on the formed image of the subject's eye. Like the OCT data processing unit 13, the data processing unit 130 can perform various data processing (image processing) and analysis processing on the formed image. For example, the data processing unit 130 performs correction processing such as image brightness correction and dispersion correction. The data processing unit 130 can form volume data (voxel data) of the subject's eye by performing known image processing such as interpolation processing that interpolates pixels between tomographic images. When an image based on the volume data is to be displayed, the data processing unit 130 performs rendering processing on the volume data to form a pseudo-three-dimensional image as viewed from a specific line of sight.

[0076] The OCT data processing unit 13 in the ophthalmologic apparatus 10 may generate the above-mentioned group of images instead of the data processing unit 130. In this case, the ophthalmologic information-processing device 100 acquires the above-mentioned group of images from the ophthalmologic apparatus 10.

[0077] Furthermore, the data processing unit 130 generates a glaucoma estimation model for estimating whether or not the subject's eye has glaucoma from a plurality of images generated from the three-dimensional OCT data.

[0078] The data processing unit 130 uses the above-described glaucoma estimation model to output estimation information for estimating whether or not the subject's eye has glaucoma based on a plurality of images of the subject's eye.

[0079] The data processing unit 130 includes an analysis unit 200, an estimation model construction unit 210, and an estimation unit 220.

[0080] The analysis unit 200 performs a predetermined analysis process on the fundus image data (or fundus image data acquired by the ophthalmologic apparatus 10). The analysis process includes a process for generating a tomographic image in a desired cross-sectional direction at a desired cross-sectional position. The analysis process according to some embodiments includes a process for identifying a predetermined site such as the optic disc or the fovea centralis.

[0081] 4, the analysis unit 200 includes an image generation unit 201. The image generation unit 201 generates a plurality of images having different cross-sectional directions from the three-dimensional OCT data acquired by the ophthalmologic apparatus 10. The plurality of images include a B-scan image, a C-scan image, an en-face image, a projection image, a shadowgram, and the like.

[0082] The image generating unit 201 can generate at least one image, from the three-dimensional OCT data, for which a cross-sectional position and a cross-sectional direction are specified by the user. The image generating unit 201 can also generate at least one image, for which a cross-sectional position and a cross-sectional direction are specified so as to pass through a predetermined region identified in the analysis processing in the analysis unit 200. The image generating unit 201 can also generate at least one image, for which a cross-sectional position and a cross-sectional direction are specified so as to avoid the predetermined region identified in the analysis processing in the analysis unit 200.

[0083] In this embodiment, the image generating unit 201 generates five types of images with different cross-sectional directions from three-dimensional OCT data of the subject's eye. The five types of images include a horizontal B-scan image (first B-scan image) passing through the center of the optic disc (or its vicinity), a vertical B-scan image (second B-scan image) passing through the center of the optic disc (or its vicinity), a vertical B-scan image (third B-scan image) passing through the fovea (or its vicinity), a projection image, and an en-face image.

[0084] Here, the three B-scan images are B-scan images (cropped images) obtained by cropping a range from a first depth position shifted by a first number of pixels (e.g., 256 pixels) toward the vitreous body in the depth direction (z direction, the direction of measurement light propagation, the optical axis direction of the interference optical system) based on a predetermined layer region in the fundus of the subject's eye to a second depth position shifted by a second number of pixels (e.g., 256 pixels) toward the choroidal body based on the predetermined layer region. For example, the predetermined layer region is a layer region shifted by a third number of pixels (e.g., 50 pixels) toward the choroidal body from the average depth position of the internal limiting membrane identified based on three-dimensional OCT data of the subject's eye. At least one of the three B-scan images may be the cropped image. The cropped image may be generated, for example, by the image generating unit 201.

[0085] A projection image is an integrated image of all layers in the depth direction. An en-face image is an integrated image of a predetermined depth (e.g., 50 micrometers) from the top layer of the retina (e.g., the internal limiting membrane). That is, an en-face image is, for example, an image projected from a layer region corresponding to the internal limiting membrane to a deeper layer region.

[0086] As described above, the estimation model construction unit 210 constructs a glaucoma estimation model (the above estimation model and classification model) for estimating whether or not the subject's eye is a glaucomatous eye.

[0087] 5, the estimation model construction unit 210 includes a glaucoma estimation learning unit 211. The estimation unit 220 includes a glaucoma estimation unit 221 as shown in FIG.

[0088] Fig. 7 is a schematic diagram illustrating the operation of the ophthalmologic information-processing device 100 according to the embodiment. Fig. 7 schematically illustrates the relationship between the estimation model construction unit 210 and the estimation unit 220. Fig. 7 also illustrates the relationship between the glaucoma estimation unit 221 and the glaucoma estimation learning unit 211. In Fig. 7, the same parts as those in Fig. 5 or 6 are denoted by the same reference numerals, and descriptions thereof will be omitted as appropriate.

[0089] The glaucoma estimation learning unit 211 generates a plurality of individual estimation models for estimating whether or not the subject's eye has glaucoma for each of a plurality of images generated from the 3D OCT data. The glaucoma estimation learning unit 211 generates the plurality of individual estimation models by performing supervised machine learning for each of the plurality of images. In some embodiments, the glaucoma estimation learning unit 211 generates the plurality of individual estimation models by transfer learning.

[0090] When five types of images are generated from the three-dimensional OCT data as described above, the glaucoma estimation learning unit 211 generates an individual estimation model for each type of image.

[0091] For example, for a horizontal B-scan image IMG1 that passes through the center of the optic disc (or its vicinity), the glaucoma estimation learning unit 211 generates an individual estimation model for the B-scan image IMG1 by performing supervised machine learning using B-scan images IMG1 of multiple test eyes other than the test eye that is the estimation target as training data TR1 and labels representing the determination results of a doctor or the like as to whether or not each B-scan image has glaucoma as training data SD1. The function of the individual estimation model for the B-scan image IMG1 is realized by a first estimator 301 shown in FIG. 7.

[0092] For example, for a vertical B-scan image IMG2 that passes through the center of the optic disc (or its vicinity), the glaucoma estimation learning unit 211 generates an individual estimation model for the B-scan image IMG2 by performing supervised machine learning using B-scan images IMG2 of multiple test eyes other than the test eye that is the estimation target as training data TR1 and labels representing the determination results of a doctor or the like as to whether or not each B-scan image has glaucoma as training data SD1. The function of the individual estimation model for the B-scan image IMG2 is realized by the second estimator 302 shown in FIG. 7.

[0093] For example, for a B-scan image IMG3 taken in the vertical direction passing through the fovea (or its vicinity), the glaucoma estimation learning unit 211 generates an individual estimation model for the B-scan image IMG3 by performing supervised machine learning using B-scan images IMG3 of multiple test eyes other than the test eye that is the estimation target as training data TR1 and labels representing the judgment results of a doctor or the like as to whether or not each B-scan image has glaucoma as training data SD1. The function of the individual estimation model for the B-scan image IMG3 is realized by a third estimator 303 shown in FIG. 7.

[0094] For example, for the projection image IMG4, the glaucoma estimation learning unit 211 generates an individual estimation model for the projection image IMG4 by performing supervised machine learning using the projection images IMG4 of multiple test eyes other than the test eye that is the estimation target as training data TR1 and labels representing the judgment results of a doctor or the like as to whether or not each projection image has glaucoma as training data SD1. The function of the individual estimation model for the projection image IMG4 is realized by the fourth estimator 304 shown in FIG. 7.

[0095] For example, for en-face image IMG5, the glaucoma estimation learning unit 211 generates an individual estimation model for en-face image IMG5 by performing supervised machine learning using en-face images IMG5 of multiple test eyes other than the test eye that is the estimation target as training data TR1 and labels representing the judgment results of a doctor or the like as to whether or not each en-face image has glaucoma as training data SD1. The function of the individual estimation model for en-face image IMG5 is realized by a fifth estimator 305 shown in FIG. 7.

[0096] Furthermore, the glaucoma estimation learning unit 211 generates a classification model for estimating whether or not the subject's eye has glaucoma from a plurality of pieces of estimation information output from the plurality of generated individual estimation models. The glaucoma estimation learning unit 211 generates the classification model by performing unsupervised machine learning or supervised machine learning.

[0097] For example, the glaucoma estimation learning unit 211 generates a classification model by performing unsupervised machine learning or supervised machine learning using a group of images IMG1 to IMG5 of multiple test eyes other than the test eye to be estimated as training data TR1. When performing supervised machine learning, a label indicating whether the test eye for training is a glaucomatous eye or not is used as training data SR1. The function of this classification model is realized by a classifier 306 shown in FIG. 7.

[0098] A glaucoma estimation model is configured by the multiple individual estimation models and classification models for images IMG1 to IMG5. That is, the estimation unit 220 uses the glaucoma estimation model generated by the glaucoma estimation learning unit 211 to output estimation information for estimating whether the subject eye is a glaucoma eye (whether the subject eye is a glaucoma eye or a normal eye) from multiple images of the subject eye (multiple images generated from OCT data of the subject eye). In some embodiments, the estimation information includes information indicating whether the subject eye is a glaucoma eye. In some embodiments, the estimation information includes information (certainty information) indicating a certainty that the subject eye is a glaucoma eye (e.g., a probability that it is estimated to have glaucoma).

[0099] Each of the above multiple individual estimation models may have a similar configuration.

[0100] 8 shows a block diagram of an example configuration of the first estimator 301 according to the embodiment. Each of the second estimator 302 to the fifth estimator 305 may have the same configuration as that shown in FIG.

[0101] The function of the first estimator 301 is realized by, for example, a convolutional neural network (CNN). That is, in accordance with instructions from an individual estimation model (trained model) stored in a computer's memory, the computer performs calculations based on trained weighting coefficients and response functions in the convolutional neural network on pixel values ​​of image IMG1 input to a convolutional layer 311 of a feature extractor 310, which is an input layer, and outputs a determination result from a classifier 320, which is an output layer. The first estimator 301 having such a configuration can extract local correlation patterns while gradually reducing the resolution of the image, and output a determination result based on the extracted correlation patterns.

[0102] The first estimator 301 includes a feature extractor 310 and a classifier 320. The feature extractor 310 extracts features of the determination image by repeatedly extracting features and downsampling (filtering) for each predetermined image region of the input image IMG1. The classifier 320 outputs estimation information (e.g., a confidence level) for estimating whether the subject's eye has glaucoma or not, based on the features extracted by the feature extractor 310.

[0103] The feature extractor 310 includes a plurality of units, each of which includes a convolution layer and a pooling layer, connected in multiple stages. In each unit, the output of the convolution layer is connected to the input of the pooling layer. The pixel value of the corresponding pixel in the image IMG1 is input to the input of the first convolution layer. The input of the subsequent convolution layer is connected to the output of the previous pooling layer.

[0104] 8, feature extractor 310 includes two units connected in two stages. That is, in feature extractor 310, a unit including convolutional layer 311 and pooling layer 312 is connected to a unit including convolutional layer 313 and pooling layer 314 at the rear stage. The output of pooling layer 312 is connected to the input of convolutional layer 313.

[0105] The classifier 320 includes fully connected layers 321 and 322, and the output of the fully connected layer 321 is connected to the input of the fully connected layer 322.

[0106] In the feature extractor 310 and the classifier 320, trained weighting coefficients are assigned between the neurons in the two connected layers. Each neuron performs a calculation using a response function on the calculation result that takes into account the weighting coefficients from one or more input neurons, and outputs the obtained calculation result to the neuron in the next stage.

[0107] The weighting coefficients are updated by performing known machine learning using two or more previously acquired images IMG1 of the subject's eye (horizontal B-scan images passing through the center of the optic disc (or its vicinity)) as training data and labels assigned to each image by a doctor or the like as teacher data. Existing weighting coefficients are updated by machine learning using two or more previously acquired images as training data. In some embodiments, the weighting coefficients are updated by transfer learning.

[0108] The first estimator 301 may have a known layer structure such as VGG16, VGG19, InceptionV3, ResNet18, ResNet50, or Xception. The classifier 320 may have a known configuration such as Random Forest or Support Vector Machine (SVM).

[0109] The classifier 306 in the glaucoma estimation unit 221 is generated by performing known machine learning using, for example, two or more previously acquired images IMG1 to IMG5 of the subject's eye as training data and labels assigned to each image by a doctor or the like as teacher data. The classifier 306 outputs information OUT indicating whether the subject's eye is a glaucomatous eye or certainty information OUT indicating the certainty that the subject's eye is a glaucomatous eye based on a plurality of pieces of estimated information from the first estimator 301 to the fifth estimator 305. In some embodiments, the classifier 306 outputs information OUT indicating whether the subject's eye is a glaucomatous eye or certainty information OUT indicating the certainty that the subject's eye is a glaucomatous eye based on a plurality of feature amounts extracted from predetermined layers of each of the first estimator 301 to the fifth estimator 305. The classifier 306 may have a known configuration such as a random forest or a support vector machine, similar to the classifier 320 shown in FIG. 8.

[0110] The ophthalmic device 10 or the communication unit 140 that performs reception processing of OCT data from the ophthalmic device 10 is an example of an "acquisition unit" according to the embodiment. The five types of images, i.e., a horizontal B-scan image passing through the center of the optic disc (or its vicinity), a vertical B-scan image passing through the center of the optic disc (or its vicinity), a vertical B-scan image passing through the fovea (or its vicinity), a projection image, and an en-face image, are examples of "multiple images" according to the embodiment. The glaucoma estimation unit 221 is an example of a "disease estimation unit" according to the embodiment.

[0111] A horizontal B-scan image passing through the center of the optic disc (or its vicinity) is an example of a "first B-scan image" according to an embodiment. A vertical B-scan image passing through the center of the optic disc (or its vicinity) is an example of a "second B-scan image" according to an embodiment. A vertical B-scan image passing through the fovea (or its vicinity) is an example of a "third B-scan image" according to an embodiment.

[0112] The individual estimation model for B-scan image IMG1, the individual estimation model for B-scan image IMG2, the individual estimation model for B-scan image IMG3, the individual estimation model for projection image IMG4, and the individual estimation model for en-face image IMG5 are examples of "plurality of trained models" according to the embodiment. The B-scan image is an example of a "tomographic image" according to the embodiment. The projection image or en-face image is an example of a "front image" according to the embodiment. The glaucoma estimation learning unit 211 is an example of a "learning unit" according to the embodiment. The horizontal direction is an example of a "first cross-sectional direction" according to the embodiment. The vertical direction is an example of a "second cross-sectional direction" according to the embodiment.

[0113] [Example of operation] An example of the operation of the ophthalmologic information-processing apparatus 100 according to the embodiment will be described.

[0114] 9 and 10 show flow diagrams of an example of operation of the ophthalmological information processing device 100 according to the embodiment. FIG. 9 shows a flow diagram of an example of a generation process of a trained model for glaucoma estimation. FIG. 10 shows a flow diagram of an example of a glaucoma estimation process using a trained model generated by the generation process shown in FIG. 9. The storage unit 112 stores a computer program for realizing the processes shown in FIGS. 9 and 10. The control unit 110 (main control unit 111) operates in accordance with this computer program, thereby being able to execute the processes shown in FIGS. 9 and 10.

[0115] In FIG. 9, it is assumed that OCT has been performed in advance on the fundus of a plurality of subjects' eyes in the ophthalmologic apparatus 10, and a plurality of pieces of three-dimensional OCT data have been acquired.

[0116] (S1: Creating a training data set from 3D OCT data) First, the main control unit 111 controls the communication unit 140 to acquire a plurality of pieces of three-dimensional OCT data for a plurality of eyes to be examined, which are acquired by the ophthalmologic apparatus 10.

[0117] Next, the main controller 111 controls the image generator 201 to generate the above five types of images as a training data group from each of the multiple 3D OCT data. That is, the image generator 201 generates, from the 3D OCT data, a horizontal B-scan image passing through the center of the optic disc (or its vicinity), a vertical B-scan image passing through the center of the optic disc (or its vicinity), a vertical B-scan image passing through the fovea (or its vicinity), a projection image, and an en-face image. Furthermore, the image generator 201 performs a cropping process on each of the generated five types of images to generate cropped images. A doctor or the like interprets the generated cropped images (or the B-scan images before the cropping process) and attaches a label indicating whether the subject's eye has glaucoma. The main controller 111 associates the cropped images with the labels and stores them in the storage unit 112.

[0118] (S2: Generate a glaucoma estimation model) Next, the main control unit 111 controls the glaucoma estimation learning unit 211 to generate an individual estimation model (disease estimation model) for one of the above five types of images. The glaucoma estimation learning unit 211 generates the individual estimation model by performing supervised machine learning as described above.

[0119] (S3:Next?) Next, the main control unit 111 determines whether or not to perform the process of generating an individual estimation model for the next image. For example, the main control unit 111 counts the number of predetermined image types or the number of image types generated in step S1 to determine whether or not to perform the process of generating an individual estimation model for the next image.

[0120] When it is determined that the process of generating an individual estimation model is to be performed for the next image (step S3: Y), the operation of the ophthalmologic information-processing device 100 proceeds to step S2.

[0121] When it is not determined that the process of generating an individual estimation model is to be performed for the next image (step S3: N), the operation of the ophthalmologic information-processing device 100 ends (END).

[0122] As shown in FIG. 10, the ophthalmologic information-processing device 100 generates estimation information for estimating whether or not the subject's eye has glaucoma, using the trained model generated according to the flow shown in FIG.

[0123] In FIG. 10, it is assumed that OCT has been performed in advance on the fundus of the subject's eye, which is the estimation target, in the ophthalmologic apparatus 10, and three-dimensional OCT data has been acquired.

[0124] (S11: Creating a group of images from 3D OCT data) First, the main control unit 111 controls the communication unit 140 to acquire three-dimensional OCT data of the subject's eye, which is the estimation target, acquired by the ophthalmologic apparatus 10.

[0125] Next, the main controller 111 controls the image generator 201 to generate the above five types of images as an image group from the 3D OCT data. That is, the image generator 201 generates, from the 3D OCT data, a horizontal B-scan image passing through the center of the optic disc (or its vicinity), a vertical B-scan image passing through the center of the optic disc (or its vicinity), a vertical B-scan image passing through the fovea centralis (or its vicinity), a projection image, and an en-face image. Furthermore, the image generator 201 performs cropping processing on each of three B-scan images of the generated five types of images to generate cropped images.

[0126] (S12: Estimated glaucoma) Next, the main control unit 111 controls the glaucoma estimation unit 221 to output estimation information for estimating whether the subject eye has glaucoma or not, using a glaucoma estimation model (individual estimation model for each image) generated by repeating steps S2 to S3 of Figure 9 for the five types of image groups generated in step S11.

[0127] This is the end of the operation of the ophthalmologic information-processing device 100 (END).

[0128] Here, to evaluate the classification accuracy (determination accuracy) of glaucoma by the ophthalmologic information processing device 100 according to the embodiment, the ophthalmologic information processing device according to the comparative example of the embodiment will be compared. The ophthalmologic information processing device according to the comparative example of the embodiment includes a convolutional neural network that uses any one of the five types of images similar to those of the embodiment as single input data, and is configured to determine whether the subject eye has glaucoma or not for each of the five types of images of the fundus of the subject eye by performing known supervised machine learning.

[0129] FIG. 11 shows an example of the area under the receiver operating characteristic curve (AUROC) obtained by the ophthalmologic information processing apparatus 100 according to the embodiment.

[0130] In Figure 11, the results of evaluation using 143 cases of OCT data from normal eyes and 672 cases of OCT data from glaucoma eyes through 5-fold cross-validation are shown as AUROC.

[0131] Specifically, when a convolutional neural network is used to classify whether a subject's eye is glaucomatous or normal using only horizontal B-scan images passing through the center of the optic disc (or its vicinity) (Disc H AUC), the AUROC is 0.92. When a convolutional neural network is used to classify whether a subject's eye is glaucomatous or normal using only vertical B-scan images passing through the center of the optic disc (or its vicinity) (Disc V AUC), the AUROC is 0.97. When a convolutional neural network is used to classify whether a subject's eye is glaucomatous or normal using only vertical B-scan images passing through the fovea (or its vicinity) (Fovea V AUC), the AUROC is 0.96. When a convolutional neural network is used to classify whether a subject's eye is glaucomatous or normal using only projection images (Projection AUC), the AUROC is 0.89. When classifying a subject's eye as glaucomatous or normal using a convolutional neural network based only on en-face images (Enface AUC), the AUROC is 0.94.

[0132] In contrast, when the ophthalmologic information-processing device 100 according to the embodiment classifies the subject's eye as either a glaucomatous eye or a normal eye using the above five types of images as input data (Combination AUC), the AUROC is 0.98.

[0133] As described above, the configuration according to the embodiment makes it possible to improve the determination accuracy (classification accuracy) compared to when simply using a convolutional neural network to determine whether or not an eye has glaucoma.

[0134] <Modification> The configuration according to the embodiment is not limited to the above configuration.

[0135] An ophthalmologic apparatus according to some embodiments has at least one of the functions of the ophthalmologic information-processing device 100, the function of the operation device 180, and the function of the display device 190, in addition to the functions of the ophthalmologic apparatus 10.

[0136] Hereinafter, ophthalmologic apparatuses according to some modified embodiments will be described, focusing on the differences from the ophthalmologic apparatus according to the above-described embodiment.

[0137] A block diagram of a configuration example of an ophthalmologic apparatus 10a according to a modified example of the embodiment is shown in Fig. 12. In Fig. 12, the same components as those in Fig. 2 are denoted by the same reference numerals, and the description thereof will be omitted where appropriate.

[0138] The configuration of the ophthalmic apparatus 10a according to this modification differs from the configuration of the ophthalmic apparatus 10 according to the above embodiment in that the ophthalmic apparatus 10a includes the functions of an ophthalmic information processing device 100, an operation device 180, and a display device 190. The ophthalmic apparatus 10a includes a control unit 11a, an OCT unit 12, an OCT data processing unit 13, an ophthalmic information processing unit 15a, an operation unit 16a, and a display unit 17a.

[0139] The control unit 11a controls each unit of the ophthalmologic apparatus 10a, particularly the OCT unit 12, the OCT data processing unit 13, the ophthalmologic information processing unit 15a, the operation unit 16a, and the display unit 17a.

[0140] The ophthalmological information processing unit 15a has the same configuration as the ophthalmological information processing device 100 and has the same functions as the ophthalmological information processing device 100. The operation unit 16a has the same configuration as the operation device 180 and has the same functions as the operation 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.

[0141] According to this modification, it is possible to obtain, with a compact configuration, highly accurate estimation information for estimating whether a subject's eye has glaucoma (a diseased eye) through machine learning using a smaller amount of training data, thereby providing a new technology for providing appropriate treatment for glaucoma at an early stage.

[0142] <effect> Hereinafter, an ophthalmological information processing apparatus, an ophthalmological apparatus, an ophthalmological information processing method, and a program according to embodiments will be described.

[0143] An ophthalmological information processing device (ophthalmological information processing device 100, ophthalmological information processing unit 15a) according to some embodiments includes an acquisition unit (the ophthalmological device 10 or a communication unit 140 that performs processing for receiving OCT data from the ophthalmological device 10) and a disease estimation unit (glaucoma estimation unit 221). The acquisition unit acquires multiple images of the subject's eye, each having a different cross-sectional direction. The glaucoma estimation unit uses multiple trained models obtained by machine learning for each type of image to output estimation information for estimating whether the subject's eye has glaucoma from the multiple images.

[0144] According to this configuration, for each of the images in a plurality of different cross-sectional directions, a trained model obtained by machine learning is used to obtain inferred information for estimating whether the subject's eye has glaucoma, thereby enabling highly accurate and early detection of glaucoma, thereby enabling appropriate treatment for glaucoma to be administered early.

[0145] In some embodiments, the disease estimation unit includes a plurality of estimators (first estimator 301 to fifth estimator 305) that use a plurality of trained models for each of a plurality of image types to output feature quantities or confidence information representing the confidence that the test eye is a glaucomatous eye, and a classifier (306) that uses a classification model obtained by machine learning to output estimation information from the plurality of feature quantities or the plurality of confidence information output from the plurality of estimators.

[0146] According to this configuration, a trained model obtained by machine learning is used to output feature values ​​or confidence information for each of a plurality of image types, and a classification model obtained by machine learning is used to output estimated information from the plurality of feature values ​​or plurality of confidence information, thereby enabling highly accurate and early detection of glaucoma, thereby enabling appropriate treatment for glaucoma to be administered early.

[0147] In some embodiments, the plurality of images includes a tomographic image or an en face image of the fundus of the subject's eye.

[0148] With this configuration, it is possible to easily obtain an image for detecting glaucoma, which makes it possible to easily provide appropriate treatment for glaucoma at an early stage.

[0149] In some embodiments, the plurality of images includes a first B-scan image in a first cross-sectional direction (horizontal) passing through the optic disc, a second B-scan image in a second cross-sectional direction (vertical) passing through the optic disc and intersecting the first cross-sectional direction, and a third B-scan image passing through the fovea.

[0150] With this configuration, glaucoma, which affects the cross-sectional shape of the optic disc or fovea, can be detected with high accuracy and at an early stage.

[0151] In some embodiments, at least one of the first B scan image, the second B scan image, and the third B scan image is a B scan image (cropped image) obtained by cropping a range from a first depth position shifted in the depth direction by a first number of pixels toward the vitreous body based on a predetermined layer region in the fundus of the test eye to a second depth position shifted in the depth direction by a second number of pixels toward the choroidal body based on the predetermined layer region.

[0152] According to this configuration, by increasing the information density contained in the B-scan image, it is possible to further improve the accuracy of classifying whether or not the subject's eye has glaucoma.

[0153] In some embodiments, the specified layer region is a layer region shifted in the depth direction by a third number of pixels from the average depth position of the internal limiting membrane identified based on three-dimensional OCT data of the test eye.

[0154] According to this configuration, the eye to be examined is classified as having glaucoma or not by focusing on the target area where morphological changes due to glaucoma are thought to be prominent, thereby enabling glaucoma to be detected more accurately and at an earlier stage.

[0155] In some embodiments, the front image includes at least one of an en-face image projecting a layer region from a layer region corresponding to the internal limiting membrane to a layer region of a deeper layer, and a projection image.

[0156] With this configuration, glaucoma, which affects the frontal shape of the subject's eye, can be detected with high accuracy and at an early stage.

[0157] Some embodiments include a learning unit (glaucoma estimation learning unit 211) that generates multiple trained models by supervised machine learning for each of multiple types of images.

[0158] With this configuration, it is possible to provide an ophthalmologic information processing device that can generate a trained model for detecting glaucoma in the subject's eye with high accuracy and early stage.

[0159] Some embodiments include an image generating unit (201) that generates at least one of a plurality of images based on three-dimensional OCT data of the subject's eye.

[0160] According to this configuration, it is possible to provide an ophthalmologic information processing device that can detect glaucoma early and with high accuracy by acquiring three-dimensional OCT data of the subject's eye.

[0161] An ophthalmic device (10, 10a) according to some embodiments includes an OCT unit (12) that performs optical coherence tomography on the subject's eye, an image generation unit (201) that generates at least one of a plurality of images based on three-dimensional data acquired by the OCT unit, and an ophthalmic information processing device (100, ophthalmic information processing unit 15a) described in any one of the above.

[0162] According to this configuration, it is possible to provide an ophthalmologic apparatus that can perform optical coherence tomography on the subject's eye and detect glaucoma early and with high accuracy from the obtained OCT data.

[0163] An ophthalmological information processing method according to some embodiments includes an acquisition step of acquiring multiple images of a test eye having different cross-sectional directions, and a disease estimation step of outputting estimation information for estimating whether the test eye has glaucomatous eye from the multiple images using multiple trained models obtained by machine learning for each type of the multiple images.

[0164] According to this method, for each of images in a plurality of different cross-sectional directions, a trained model obtained by machine learning is used to obtain estimated information for estimating whether the subject's eye has glaucoma, thereby enabling highly accurate and early detection of glaucoma, thereby enabling appropriate treatment for glaucoma to be administered early.

[0165] In some embodiments, the disease estimation step includes a plurality of estimation steps that use a plurality of trained models for each of a plurality of image types to output feature quantities or confidence information representing the confidence that the test eye is a glaucomatous eye, and a classification step that uses a classification model obtained by machine learning to output estimation information from the plurality of feature quantities or the plurality of confidence information output in the plurality of estimation steps.

[0166] According to this method, a trained model obtained by machine learning is used to output feature values ​​or confidence information for each of a plurality of image types, and a classification model obtained by machine learning is used to output estimated information from the plurality of feature values ​​or plurality of confidence information, thereby enabling highly accurate and early detection of glaucoma, thereby enabling appropriate treatment for glaucoma to be administered early.

[0167] In some embodiments, the plurality of images includes tomographic or en face images of the fundus of the subject's eye, and the disease is an ocular fundus disease.

[0168] According to this method, it is possible to easily obtain an image for detecting glaucoma, which makes it possible to easily provide appropriate treatment for glaucoma at an early stage.

[0169] In some embodiments, the plurality of images includes a first B-scan image in a first cross-sectional direction (horizontal) passing through the optic disc, a second B-scan image in a second cross-sectional direction (vertical) passing through the optic disc and intersecting the first cross-sectional direction, and a third B-scan image passing through the fovea.

[0170] According to this method, glaucoma, which affects the cross-sectional shape of the optic disc or fovea, can be detected with high accuracy and at an early stage.

[0171] In some embodiments, at least one of the first B scan image, the second B scan image, and the third B scan image is a B scan image (cropped image) obtained by cropping a range from a first depth position shifted in the depth direction by a first number of pixels toward the vitreous body based on a predetermined layer region in the fundus of the test eye to a second depth position shifted in the depth direction by a second number of pixels toward the choroidal body based on the predetermined layer region.

[0172] According to this method, by increasing the information density contained in the B-scan image, it becomes possible to further improve the accuracy of classifying whether or not the subject's eye has glaucoma.

[0173] In some embodiments, the specified layer region is a layer region shifted in the depth direction by a third number of pixels from the average depth position of the internal limiting membrane identified based on three-dimensional OCT data of the test eye.

[0174] According to this method, the eye to be examined is classified as having glaucoma or not by focusing on a target area where morphological changes due to glaucoma are thought to be prominent, thereby enabling glaucoma to be detected more accurately and at an earlier stage.

[0175] In some embodiments, the front image includes at least one of an en-face image projecting a layer region from a layer region corresponding to the internal limiting membrane to a layer region of a deeper layer, and a projection image.

[0176] According to this method, glaucoma, which affects the frontal shape of the subject's eye, can be detected with high accuracy and at an early stage.

[0177] Some embodiments include a training step that generates multiple trained models using supervised machine learning for each of multiple image types.

[0178] According to this method, it is possible to provide an ophthalmological information processing method that can generate a trained model for detecting glaucoma in the subject's eye with high accuracy and early stage.

[0179] Some embodiments include an image generating step of generating at least one of a plurality of images based on three-dimensional OCT data of the subject's eye.

[0180] According to this method, by acquiring three-dimensional OCT data of the subject's eye, it is possible to provide an ophthalmologic information processing method that can detect glaucoma with high accuracy and at an early stage.

[0181] A program according to some embodiments causes a computer to execute each step of any of the ophthalmologic information processing methods described above.

[0182] According to this program, for each of a plurality of images taken in different cross-sectional directions, a trained model obtained by machine learning is used to obtain inferred information for estimating whether or not the subject's eye has glaucoma, enabling highly accurate and early detection of the disease, thereby enabling appropriate treatment for glaucoma to be administered early.

[0183] A program for implementing the ophthalmologic information processing method according to some embodiments can be stored in any non-transitory computer-readable recording medium. The recording medium may be an electronic medium using magnetic, optical, magneto-optical, semiconductor, or the like. Typically, the recording medium is a magnetic tape, a magnetic disk, an optical disk, a magneto-optical disk, a flash memory, a solid-state drive, or the like.

[0184] It is also possible to send and receive computer programs via a network such as the Internet or a LAN.

[0185] The above-described embodiments are merely examples for carrying out the present invention, and those who wish to carry out the present invention may make any modifications (omissions, substitutions, additions, etc.) within the scope of the gist of the present invention. [Explanation of symbols]

[0186] 1. Ophthalmology System 10, 10a ophthalmological equipment 11, 11a, 110 control section 12 OCT section 12A Interferometric Optical System 12B Scanning Optical System 14, 140 Communications Department 15a Ophthalmology Information Processing Department 16a Control section 17a Display section 100 Ophthalmology information processing device 111 Main control unit 112 Storage section 130 Data Processing Unit 180 Operating device 190 Display device 200 Analysis Department 201 Image Generation Unit 210 Estimation Model Building Department 211 Glaucoma Estimation Learning Unit 220 Estimation Department 221 Glaucoma Estimation Department

Claims

1. an acquisition unit that acquires a plurality of images of the subject's eye having cross-sectional directions different from each other; a disease estimation unit that outputs estimation information for estimating whether the subject's eye is a glaucoma eye from the plurality of images using a plurality of trained models obtained by machine learning for each type of the plurality of images; Including, the plurality of images include a tomographic image of a fundus of the subject's eye, An ophthalmologic information processing device, wherein the plurality of images include a first B-scan image in a first cross-sectional direction passing through the optic disc, a second B-scan image in a second cross-sectional direction passing through the optic disc and intersecting the first cross-sectional direction, and a third B-scan image passing through the fovea.

2. At least one of the first B-scan image, the second B-scan image, and the third B-scan image is a B-scan image obtained by cropping a range from a first depth position shifted in the depth direction by a first number of pixels toward the vitreous body based on a predetermined layer region in the fundus of the test eye to a second depth position shifted in the depth direction by a second number of pixels toward the choroidal body based on the predetermined layer region.

2. The ophthalmological information processing apparatus according to claim 1.

3. The predetermined layer region is a layer region shifted in the depth direction by a third number of pixels from the average depth position of the inner limiting membrane identified based on three-dimensional OCT data of the test eye.

3. The ophthalmologic information processing apparatus according to claim 2.

4. An acquisition unit that acquires multiple images of a subject's eye having cross-sectional directions different from each other; a disease estimation unit that outputs estimation information for estimating whether the subject's eye is a glaucoma eye from the plurality of images using a plurality of trained models obtained by machine learning for each type of the plurality of images; Including, the plurality of images include a front image of a fundus of the subject's eye, The front image includes at least one of an en-face image projected from a layer region corresponding to the internal limiting membrane to a layer region of a deep layer, and a projection image.

5. The disease prediction unit a plurality of estimators that use the plurality of trained models for each type of the plurality of images to output feature amounts or certainty information that indicates a certainty that the subject's eye is a glaucoma eye; a classifier that uses a classification model obtained by machine learning to output the estimation information from the plurality of feature quantities or the plurality of pieces of confidence information output from the plurality of estimators; Contains 5. The ophthalmologic information processing apparatus according to claim 1, wherein the ophthalmologic information processing apparatus is a computer.

6. a learning unit that generates the plurality of trained models by supervised machine learning for each of the plurality of image types; 6. The ophthalmologic information processing apparatus according to claim 1, wherein the ophthalmologic information processing apparatus is a computer.

7. an image generating unit that generates at least one of the plurality of images based on three-dimensional OCT data of the subject's eye; 7. The ophthalmologic information processing apparatus according to claim 1, wherein the ophthalmologic information processing apparatus is a computer.

8. an OCT unit that performs optical coherence tomography on the subject's eye; an image generating unit that generates at least one of the plurality of images based on the three-dimensional data acquired by the OCT unit; An ophthalmological information processing device according to any one of claims 1 to 7; 1. An ophthalmic device comprising:

9. an acquiring step of acquiring a plurality of images of the subject's eye having cross-sectional directions different from each other; a disease inference step of outputting inferred information for inferring whether the subject's eye is a glaucoma eye from the plurality of images using a plurality of trained models obtained by machine learning for each type of the plurality of images; Including, the plurality of images include a tomographic image of a fundus of the subject's eye, An ophthalmologic information processing method, wherein the plurality of images include a first B-scan image in a first cross-sectional direction passing through the optic disc, a second B-scan image in a second cross-sectional direction passing through the optic disc and intersecting the first cross-sectional direction, and a third B-scan image passing through the fovea.

10. The disease estimation step includes: a plurality of estimation steps of outputting feature amounts or certainty information representing a certainty that the subject's eye is a glaucoma eye using each of the plurality of trained models for each type of the plurality of images; a classification step of outputting the estimation information from the plurality of feature quantities or the plurality of pieces of confidence information output in the plurality of estimation steps, using a classification model obtained by machine learning; Contains 10. The ophthalmologic information processing method according to claim 9.

11. At least one of the first B-scan image, the second B-scan image, and the third B-scan image is a B-scan image obtained by cropping a range from a first depth position shifted in the depth direction by a first number of pixels toward the vitreous body side with reference to a predetermined layer region in the fundus of the subject's eye to a second depth position shifted in the depth direction by a second number of pixels toward the choroid side with reference to the predetermined layer region.

11. The ophthalmologic information processing method according to claim 9 or 10.

12. The predetermined layer region is a layer region shifted in the depth direction by a third number of pixels from the average depth position of the internal limiting membrane identified based on the three-dimensional OCT data of the subject's eye. The ophthalmologic information processing method according to claim 11 .

13. An acquisition step of acquiring a plurality of images of the subject's eye having cross-sectional directions different from each other; a disease inference step of outputting inferred information for inferring whether the subject's eye is a glaucoma eye from the plurality of images using a plurality of trained models obtained by machine learning for each type of the plurality of images; Including, the plurality of images include a front image of a fundus of the subject's eye, The ophthalmologic information processing method, wherein the front image includes at least one of an en-face image projected from a layer region corresponding to the internal limiting membrane to a layer region of a deep layer, and a projection image.

14. A learning step of generating the plurality of trained models by supervised machine learning for each of the plurality of image types. The ophthalmologic information processing method according to any one of claims 9 to 13.

15. an image generating step of generating at least one of the plurality of images based on three-dimensional OCT data of the subject's eye. The ophthalmologic information processing method according to any one of claims 9 to 14.

16. A program causing a computer to execute each step of the ophthalmologic information processing method according to any one of claims 9 to 15.

Images