Information processing device, information processing method, and program

Abstract

To recognize a degree of deviation of a shape of a subject eye from a spherical shape.SOLUTION: An information processing device of the present disclosure includes a display control part that displays in a display part a comparison result of comparing a shape of a subject eye and a virtual spherical shape corresponding to the shape of the subject eye obtained by using a tomographic image of the subject eye imaged by an ophthalmologic device and information on the ophthalmologic device.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The disclosed technology relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] Ophthalmic devices that have been put into practical use include devices (fundus cameras) for obtaining two-dimensional images of the fundus of the subject's eye, and devices (OCT devices) for obtaining tomographic images of the subject's eye using optical coherence tomography (OCT) with low-coherence light.

[0003] By analyzing the shape of the retina using tomographic images of the retina captured by an OCT device, it is possible to quantitatively diagnose the progression of pathological myopia and the degree of recovery after treatment. Pathological myopia is a condition in which the shape of the optic disc and its surroundings of the test eye are significantly elongated or deformed due to axial elongation or the formation of posterior staphyloma. Non-Patent Document 1 describes classifying the shape of the test eye into 10 types based on fundus camera images. Patent Document 1 also discloses a technology for calculating the local curvature of the test eye using tomographic images and comparing the calculated curvature with the curvature in a statistical database to determine whether the test eye has symptoms of posterior staphyloma. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-153884 [Non-patent literature]

[0005] [Non-Patent Document 1] Brain J. Curtin, “The Posterior Staphyloma of Pathologic Myopia”, Transaction of American Ophthalmology Society, Vol. LXXXV, pp. 67-86, (1997). Summary of the Invention [Problem to be solved by the invention]

[0006] Here, in a method for calculating the local curvature of the subject's eye using a tomographic image, for example, it is difficult to grasp the overall shape of the subject's eye, making it difficult to grasp how much the shape of the subject's eye deviates from the shape of a sphere. Therefore, the method for calculating the local curvature of the subject's eye may not properly diagnose pathological myopia.

[0007] Therefore, an object of the present disclosure is to understand how much the shape of the subject's eye deviates from the shape of a sphere. [Means for solving the problem]

[0008] The information processing device disclosed herein includes a display control unit that displays on a display unit a comparison result obtained by comparing the shape of the test eye obtained using a tomographic image of the test eye taken by an ophthalmic device and information about the ophthalmic device with the shape of a virtual sphere corresponding to the shape of the test eye. [Effects of the Invention]

[0009] According to the present disclosure, it is possible to grasp how much the shape of the subject's eye deviates from the shape of a sphere. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a diagram showing an example of a schematic configuration of an optical coherence tomography apparatus according to a first embodiment. [Figure 2] FIG. 2 is a diagram showing an example of a schematic configuration of a signal processing unit according to the first embodiment. [Figure 3]1A and 1B are diagrams for explaining the structure of an eye, a tomographic image, and a fundus image. [Figure 4] FIG. 3 is a flowchart showing an example of a processing flow according to the first embodiment. [Figure 5] 3A and 3B are diagrams for explaining an example of measurement light incident on an eye to be examined and image correction according to the first embodiment. [Figure 6] FIG. 3 is a diagram for explaining an example of estimating the shape of a virtual sphere according to the first embodiment. [Figure 7] FIG. 2 is a diagram for explaining a screen for displaying an image according to the first embodiment. [Figure 8] FIG. 10 is a diagram showing an example of a schematic configuration of a signal processing unit according to a second embodiment. [Figure 9] FIG. 10 is a diagram for explaining the distance relationship between an optical coherence tomography apparatus according to the second embodiment and a subject's eye. [Figure 10] 10A and 10B are diagrams for explaining an example of measurement light incident on an eye to be examined and image correction according to the second embodiment. [Figure 11] FIG. 10 is a diagram showing an example of a schematic configuration of a signal processing unit according to a fourth embodiment. [Figure 12] FIG. 13 is a diagram showing an example of an evaluation area according to the fourth embodiment. [Figure 13] FIG. 10 is a diagram for explaining the concept of an evaluation model according to a fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments for implementing the disclosed technology will be described in detail with reference to the drawings. However, the dimensions, materials, shapes, and relative positions of components described in the following embodiments are arbitrary and can be changed depending on the configuration of an apparatus to which the disclosed technology is applied or various conditions. In addition, the same reference numerals are used between drawings to indicate identical or functionally similar elements.

[0012] An optical coherence tomography apparatus according to an embodiment of the present disclosure includes an irradiation unit that irradiates a subject with light of a first wavelength band, where the first wavelength band is, for example, 400 nm to 700 nm. The irradiation unit is, for example, an illumination optical system including an objective lens.

[0013] The optical coherence tomography apparatus also includes a planar image acquisition unit that acquires a planar image of the subject based on return light from the subject irradiated by the irradiation unit. Here, the planar image acquisition unit acquires the planar image of the subject based on, for example, an output signal from the imaging unit. The imaging unit outputs an output signal based on return light from the subject irradiated with light of the first wavelength band, which is imaged via the imaging optical system.

[0014] The optical coherence tomography apparatus also includes a light source that emits laser light while sweeping a second wavelength band longer than the first wavelength band. Here, the second wavelength band is, for example, 980 nm to 1100 nm. The light source is, for example, a wavelength-swept light source for SS-OCT.

[0015] The optical coherence tomography apparatus also includes a tomographic image acquisition unit that acquires a tomographic image of the subject based on light obtained by combining, using an irradiation unit, return light from the subject irradiated with laser light emitted from a light source and reference light corresponding to the laser light. Here, the tomographic image acquisition unit can acquire SS-OCT tomographic images. While SS-OCT is used in the present disclosure, the present disclosure is not limited to this. The present disclosure is also applicable to tomographic image acquisition units using different imaging methods, such as SD-OCT (Spectral Domain OCT), PS-OCT (Polarization Sensitive OCT), and AO-OCT (Adaptive Optics OCT).

[0016] The optical coherence tomography apparatus according to the embodiment of the present disclosure can be applied to a subject, such as a subject's eye, skin, or internal organs. When the subject is the fundus of the subject's eye, the planar image is a fundus image. The information processing system according to the embodiment of the present disclosure is a system connected to a network so that an information processing apparatus according to the disclosed technology and an ophthalmic apparatus can communicate with each other. For example, an examiner receives an order (examination instruction) from a doctor's personal computer, photographs the subject's eye using the ophthalmic apparatus according to the order, and transmits the photographed results to the doctor's personal computer.

[0017] [Embodiment 1] In an embodiment of the present disclosure, retinal layers are detected from OCT data captured by an optical coherence tomography apparatus, and retinal shape data is generated from the information on the detected layers.The shape of a virtual sphere of the subject's eye is then estimated from the captured OCT data, and the deviation rate between the estimated shape and the retinal shape is calculated and displayed as a two-dimensional map.

[0018] 1 is a schematic diagram of the overall configuration of an "ophthalmologic apparatus," which is an example of an optical coherence tomography apparatus according to this embodiment. At least a part of a control unit 200, which will be described later, can be considered as an "information processing device." Furthermore, when at least a part of the control unit 200 is considered as an "information processing device," the entire "ophthalmologic apparatus" can also be considered as an "information processing system."

[0019] This device is composed of an SS-OCT (Swept Source OCT; hereinafter, sometimes simply referred to as OCT) 100, a scanning laser ophthalmoscope (hereinafter, sometimes referred to as SLO) 140, an anterior segment imaging unit 160, an internal fixation light 170, and a control unit 200.

[0020] Here, since the spectrometer used in SD-OCT spectroscopically disperses interference light in space using a diffraction grating, crosstalk of interference light easily occurs between adjacent pixels of the line sensor. Interference light from a reflecting surface located at depth position Z = Z0 vibrates at a frequency of Z0 / π with respect to the wave number k. Therefore, as Z0 increases (i.e., as the distance from the coherence gate position increases), the oscillation frequency of the interference light increases, and the influence of crosstalk of interference light between adjacent pixels of the line sensor becomes greater. As a result, in SD-OCT, when attempting to image a deeper position, the sensitivity degradation becomes significant. On the other hand, SS-OCT that does not use a spectrometer is more advantageous for imaging tomographic images at a deeper position than SD-OCT.

[0021] In addition, in the spectrometer of SD-OCT, there is loss of interference light due to the diffraction grating. On the other hand, in SS-OCT, it is easy to improve the sensitivity by adopting a configuration in which interference light is, for example, differentially detected without using a spectrometer. Therefore, SS-OCT can be made faster with sensitivity equivalent to that of SD-OCT, and by taking advantage of this high speed, it is possible to acquire a wide-angle tomographic image.

[0022] Also, in the state where the internal fixation lamp 170 is lit to cause the subject eye to fixate, alignment of the apparatus is performed using an image of the anterior segment of the subject eye observed by the anterior segment imaging unit 160. Then, after the alignment is completed, the apparatus performs imaging of the fundus by OCT100 and SLO140.

[0023] <Configuration of OCT100> An example of the configuration of OCT100 will be described.

[0024] OCT100 corresponds to an example of a tomographic image acquisition means for acquiring a tomographic image of a subject eye.

[0025] The light source 101 is a tunable wavelength light source and emits light with, for example, a center wavelength of 1040 nm and a bandwidth of 100 nm. The wavelength of the light emitted from the light source 101 is preferably swept at a constant interval, and more preferably swept so that the wavenumbers (reciprocals of the wavelengths) are equally spaced due to computational constraints when performing a Fourier transform. The light emitted from the light source 101 is guided to a fiber coupler 104 via a fiber 102 and a polarization controller 103, and then branched into a fiber 130 for measuring the light intensity and a fiber 105 for OCT measurement. The light emitted from the light source 101 passes through the fiber 130 and has its power measured by a PM (Power Meter) 131. The light passed through the fiber 105 is guided to a second fiber coupler 106. In the fiber coupler 106, the light is branched into measurement light (also referred to as OCT measurement light) and reference light.

[0026] The polarization controller 103 adjusts the polarization state of the light emitted from the light source 101 to linear polarization. The branching ratio of the fiber coupler 104 is 99:1, and the branching ratio of the fiber coupler 106 is 90 (reference light):10 (measurement light). Note that the branching ratios are not limited to these values, and other values ​​are also possible.

[0027] The measurement light branched by the fiber coupler 106 passes through a fiber 118 and is emitted as parallel light from a collimator 117. The emitted measurement light passes through an X-scanner 107, which is composed of a galvanometer mirror that scans the measurement light horizontally (up and down on the paper) at the fundus Er, and a lens 108, before reaching a lens 109. The measurement light from the lens 109 then passes through a Y-scanner 110, which is composed of a galvanometer mirror that scans the measurement light vertically (into the paper) at the fundus Er, before reaching a dichroic mirror 111. The X-scanner 107 and the Y-scanner 110 are controlled by a drive control unit 180, allowing the measurement light to scan a desired area on the fundus Er. The dichroic mirror 111 has the property of reflecting light of, for example, 950 nm to 1100 nm and transmitting other light.

[0028] The measurement light reflected by the dichroic mirror 111 passes through a lens 112 and reaches a focus lens 114 mounted on a stage 116. The measurement light that reaches the focus lens 114 passes through the anterior segment Ea of the eye, which is the subject's eye, and is focused on the retinal layer of the fundus Er. In other words, the optical system from the light source 101 to the subject's eye corresponds to an example of an illumination optical system that guides light emitted from the light source to the subject's eye. The measurement light that illuminates the fundus Er is reflected and scattered by each retinal layer and returns to the fiber coupler 106 via the optical path described above. The measurement light from the fundus Er passes from the fiber coupler 106 through a fiber 125 and reaches the fiber coupler 126.

[0029] The movement of the focus lens 114 in the optical axis direction is controlled by the drive control unit 180. In this embodiment, the focus lens 114 is shared by the OCT 100 and the SLO 140. However, this is not limiting, and each optical system may be provided with a separate focus lens. The drive control unit 180 may control the focus lens by driving the focus lens based on the difference between the wavelength used by the light source 101 and the wavelength used by the light source 141. For example, if a common focus lens is provided for the OCT 100 and the SLO 140, the drive control unit 180 moves the focus lens 114 in accordance with the difference in wavelength when switching between imaging using the SLO 140 and imaging using the OCT 100. In addition, if a focus lens is provided for each of the optical systems of the OCT 100 and the SLO 140, when the focus lens of one optical system is adjusted, the drive control unit 180 moves the focus lens of the other optical system in accordance with the difference in wavelength.

[0030] On the one hand, the reference light branched by the fiber coupler 106 is emitted as parallel light from the collimator 120-a through the fiber 119. The emitted reference light is reflected by the mirrors 123-a and 123-b on the coherence gate stage 122 through the dispersion compensation glass 121, and reaches the fiber coupler 126 through the collimator 120-b and the fiber 124. The coherence gate stage 122 is controlled by the drive control unit 180 to correspond to differences in the axial length of the eye to be examined, etc.

[0031] The measurement light and the reference light that reach the fiber coupler 126 are combined into interference light, and the interference light is converted into an electrical signal by a differential detector (balanced receiver) 129, which is a photodetector, via the fibers 127 and 128. That is, the optical system from the eye to be examined to the differential detector 129 corresponds to an example of an imaging optical system that guides the return light from the eye to the imaging means of the light swept by the control means. The converted electrical signal is analyzed by the signal processing unit 190. Note that the photodetector is not limited to the differential detector, and other detectors may be used.

[0032] Also, although the configuration is such that the measurement light and the reference light interfere at the fiber coupler 126, it is not limited thereto. For example, the mirror 123-a may be arranged so that the reference light is reflected to the fiber 119, and the measurement light and the reference light may be made to interfere at the fiber coupler 106. In this case, the mirror 123-b, the collimator 120-b, the fiber 124, and the fiber coupler 126 become unnecessary. In this case, it is preferable to use a circulator.

[0033] <Configuration of SLO140> An example of the configuration of SLO140 will be described.

[0034] SLO140 corresponds to an example of a fundus image acquisition means for acquiring a fundus image of the eye to be examined.

[0035] The light source 141 is, for example, a semiconductor laser, and in this embodiment, emits light with a center wavelength of, for example, 780 nm. The measurement light (also referred to as SLO measurement light) emitted from the light source 141 passes through a fiber 142, is adjusted to linear polarization by a polarization controller 145, and is emitted as parallel light from a collimator 143. The emitted measurement light passes through a hole in a perforated mirror 144, passes through a lens 155, and then passes through an X scanner 146 consisting of a galvanometer mirror that scans the measurement light horizontally on the fundus Er, lenses 147 and 148, and a Y scanner 149 consisting of a galvanometer mirror that scans the measurement light vertically on the fundus Er, before reaching a dichroic mirror 154. Note that the polarization controller 145 may not be provided. The X scanner 146 and Y scanner 149 are controlled by a drive control unit 180, and can scan a desired area on the fundus with the measurement light. The dichroic mirror 154 has the property of reflecting light of, for example, 760 nm to 800 nm and transmitting other light.

[0036] The linearly polarized measurement light reflected by the dichroic mirror 154 passes through the dichroic mirror 111, and then travels along the same optical path as the OCT measurement light of the OCT 100, and reaches the fundus Er.

[0037] The SLO measurement light that has irradiated the fundus Er is reflected and scattered by the fundus Er, travels along the above-mentioned optical path, and reaches the perforated mirror 144. The light reflected by the perforated mirror 144 passes through a lens 150 and reaches an avalanche photodiode (hereinafter also referred to as APD) 152. The light that has reached the APD is converted into an electrical signal and input to a signal processing unit 190.

[0038] The position of the perforated mirror 144 is conjugate with the position of the pupil of the subject's eye. Of the measurement light irradiated onto the fundus Er and reflected and scattered, the light that passes through the peripheral area of ​​the pupil is reflected by the perforated mirror 144.

[0039] <Anterior segment imaging unit 160> An example of the configuration of the anterior eye imaging section 160 will be described.

[0040] The anterior eye imaging unit 160 includes lenses 162 , 163 , and 164 and an anterior eye camera 165 .

[0041] An illumination light source 115, which is composed of LEDs 115-a and 115-b that emit illumination light with a wavelength of, for example, 850 nm, illuminates the anterior segment Ea. The light reflected by the anterior segment Ea passes through a focus lens 114, a lens 112, and dichroic mirrors 111 and 154, and reaches a dichroic mirror 161. The dichroic mirror 161 has the property of reflecting light with a wavelength of, for example, 820 nm to 900 nm and transmitting light other than that. The light reflected by the dichroic mirror 161 passes through lenses 162, 163, and 164 and is received by an anterior segment camera 165. The light received by the anterior segment camera 165 is converted into an electrical signal and input to a signal processing unit 190.

[0042] <Internal Fixation Light 170> The internal fixation light 170 will now be described.

[0043] The internal fixation lamp 170 is composed of a display unit 171 and a lens 172. The display unit 171 may be, for example, a plurality of light-emitting diodes (LDs) arranged in a matrix. The lighting positions (lighting patterns) of the light-emitting diodes are changed according to the area to be imaged under the control of the drive control unit 180. Light from the display unit 171 is guided to the subject's eye via the lens 172. The light emitted from the display unit 171 has a wavelength of, for example, 520 nm.

[0044] <Control unit 200> The following describes the control unit 200. The control unit 200 is an example of an information processing device.

[0045] The control unit 200 is made up of a drive control unit 180 , a signal processing unit 190 , a display control unit 191 , and a display unit 192 .

[0046] The drive control unit 180 controls each unit as described above.

[0047] The signal processing unit 190 generates images, analyzes the generated images, and generates visualized information of the analysis results based on signals output from the differential detector 129, the APD 152, and the anterior eye camera 165. Details of image generation and the like will be described later.

[0048] The display control unit 191 controls the entire device and displays images generated by the signal processing unit 190 on the display screen of the display unit 192. The display unit 192 corresponds to an example of a display means or a display device. The image data generated by the signal processing unit 190 may be transmitted to the display control unit 191 via a wired connection or wirelessly.

[0049] The display unit 192 is, for example, a display such as a liquid crystal display. The display unit 192 displays various information as described below under the control of the display control unit 191. Note that image data from the display control unit 191 may be transmitted to the display unit 192 via a wired connection or wirelessly. Furthermore, although the display unit 192 and the like are included in the control unit 200, the present disclosure is not limited to this, and the display unit 192 and the like may be provided separately from the control unit 200.

[0050] Alternatively, the display control unit 191 and the display unit 192 may be integrally configured as a tablet, which is an example of a device that can be carried by a user. In this case, it is preferable that the display unit 192 is equipped with a touch panel function, and configured so that operations such as moving the display position of an image, enlarging or reducing the image, and changing the displayed image can be performed on the touch panel. Note that the display unit 192 may be equipped with a touch panel function even if the display control unit 191 and the display unit 192 are not integrally configured. In other words, a touch panel may be used as an instruction device.

[0051] Next, image generation and image analysis in signal processing unit 190 will be described with reference to Fig. 2. Fig. 2 shows an example of the configuration of signal processing unit 190. Signal processing unit 190 is made up of a reconstruction unit 1901, an analysis unit 1902, a shape correction unit 1903, a shape calculation unit 1904, and a deviation calculation unit 1905. Reconstruction unit 1901 in signal processing unit 190 performs general reconstruction processing on the interference signal output from differential detector 129 to generate a tomographic image.

[0052] The signal processing unit 190 may be configured using, for example, a general-purpose computer. Alternatively, the signal processing unit 190 may be configured using a computer dedicated to the ophthalmic apparatus. The signal processing unit 190 includes a processor (not shown) and a storage medium including a memory such as an optical disk or a read-only memory (ROM). The processor may be a central processing unit (CPU) or a microprocessing unit (MPU). The processor is not limited to a CPU or an MPU, but may also be a graphics processing unit (GPU) or a field-programmable gate array (FPGA). Each component of the signal processing unit 190 other than the storage medium may be configured by a software module executed by a processor such as a CPU, MPU, or GPU. Each component may be configured by a circuit performing a specific function, such as an ASIC, or an independent device. The processor or circuit may include a digital signal processor (DSP), a data flow processor (DFP), or a neural processing unit (NPU).

[0053] FIG. 3 illustrates the tomographic images captured by the OCT 100 and generated by the reconstruction unit 1901, as well as the structure of the eye. FIG. 3(a) shows a schematic diagram of an eyeball. In FIG. 3(a), Cor represents the cornea, CrLe represents the crystalline lens, V represents the vitreous body, M represents the macula (the center of the macula represents the fovea), and D represents the optic nerve head. The OCT 100 according to this embodiment will be described mainly in connection with a case where it captures images of the posterior pole of the retina, including the vitreous body, macula, and optic nerve head. Although not described in this disclosure, the OCT 100 can also capture images of the anterior segment of the eye, such as the cornea and crystalline lens.

[0054] Figure 3(b) shows an example of a tomographic image of the retina captured by the OCT100. In Figure 3(b), AS represents an A-scan, a unit of image acquisition in OCT tomographic images. Multiple A-scans form a single B-scan. This B-scan is called a tomographic image (or tomogram). In Figure 3(b), Ve represents blood vessels, V represents the vitreous body, M represents the macula, and D represents the optic nerve head. Additionally, L1 represents the boundary between the internal limiting membrane (ILM) and the nerve fiber layer (NFL), L2 represents the boundary between the nerve fiber layer and the ganglion cell layer (GCL), L3 represents the photoreceptor inner-outer segment junction (ISOS), L4 represents the retinal pigment epithelium (RPE), L5 represents Bruch's membrane (BM), and L6 represents the chorio-scleral interface (CSI). In the tomographic image, the horizontal axis (OCT main scanning direction) is the x-axis, and the vertical axis (depth direction) is the z-axis.

[0055] FIG. 3(c) shows an example of a fundus image acquired by the fundus image acquisition means 140. The fundus image acquisition means 140 is a device that captures fundus images of the eye, and an example of such a device is an SLO (Scanning Laser Ophothalmoscope). In FIG. 3(c), M represents the macula, D represents the optic disc, and the thick curve represents the retinal blood vessels. In the fundus image, the horizontal axis (main scanning direction of OCT) is the x-axis, and the vertical axis (sub-scanning direction of OCT) is the y-axis. The tomographic image acquisition means 100 and the fundus image acquisition means 140 may be configured as an integrated unit or as separate units. Furthermore, the fundus image acquisition means 140 may be a fundus camera or the like.

[0056] Next, the processing procedure of the signal processing unit 190 of this embodiment will be shown with reference to Fig. 4. Fig. 4(a) shows the operation processing of the entire system in this embodiment. Fig. 4(b) is a flowchart showing the flow of the processing of estimating the shape of a virtual sphere of the subject's eye from OCT data and the processing of calculating the deviation rate between the shape of the estimated virtual sphere and the shape of the retina in this embodiment.

[0057] <Step S401> In step S401, the subject's eye information acquisition unit (not shown) externally acquires a subject's identification number as information for identifying the subject's eye, and then acquires information about the subject's eye stored in an external storage unit (not shown) based on the subject's identification number and stores the information in the storage unit (not shown).

[0058] <Step S402> In step S402, the subject's eye is scanned and photographed. The operator starts scanning the subject's eye by selecting a scan start button (not shown). When scanning of the subject's eye begins, the OCT 100 controls the drive controller 180 to operate the galvanometer mirror and scan a tomographic image. The galvanometer mirror is composed of an X scanner 107 and a Y scanner 110. Therefore, by changing the orientation of each of these scanners, scanning can be performed in both the horizontal (X) and vertical (Y) directions in the device coordinate system. Furthermore, by simultaneously changing the orientation of these scanners, scanning can be performed in a direction that combines the horizontal and vertical directions, enabling scanning in any direction on the fundus plane. The signal processor 190 adjusts various photographing parameters for photographing. Specifically, the signal processor 190 sets at least the position of the internal fixation light, the scanning range, the scanning pattern, the coherence gate position, and the focus. The drive control unit 180 controls the light-emitting diodes of the display unit 171 to control the position of the internal fixation light 170 so that imaging is performed at a desired location, such as the macula or optic disc. The position of the internal fixation light can be set by the operator using a user interface (not shown). In this embodiment, the scan pattern is set as a scan pattern for 3D data. After these imaging parameters are adjusted, imaging is performed by the operator selecting an imaging start button (not shown). During imaging, imaging is performed while tracking the movement of the subject's eye using a tracking function (not shown). If a blink or other event occurs, a rescan is performed and a tomographic image of the blinking location is reacquired. Tracking is not limited to the XY directions within the fundus plane. Tracking may also be performed in the Z direction, which is the depth direction of the fundus, to maintain a constant distance between the anterior segment and the device.

[0059] <Step S403> In step S403, the reconstruction unit 1901 performs general reconstruction processing on the interference signals output from the differential detector 129, thereby generating a tomographic image.

[0060] First, the reconstruction unit 1901 removes fixed pattern noise from the interference signal by, for example, extracting fixed pattern noise by averaging multiple detected A-scan signals and subtracting the fixed pattern noise from the interference signal.

[0061] Next, the reconstruction unit 1901 performs a desired window function process to optimize the depth resolution and dynamic range, which are in a trade-off relationship when Fourier transform is performed over a finite interval. Next, the reconstruction unit 1901 performs an FFT process to generate a tomographic image.

[0062] <Step S404> In step S404, the shape of a virtual sphere of the subject's eye is estimated from the OCT data, and the deviation rate between the shape of the estimated virtual sphere and the shape of the retina is calculated. The method for calculating the deviation rate will be described with reference to the flowchart in FIG. 4(b) and FIGS. 5 to 7.

[0063] <Step S441> In step S441, the analysis unit 1902 detects the boundaries of retinal layers. The boundaries of retinal layers are detected by a machine learning model such as deep learning or image processing. For example, when data is input to a machine learning model, data according to the design of the machine learning model is output. Output data that is highly likely to correspond to the input data according to trends trained from the training data is output. Furthermore, the likelihood of each type of output data trained from the training data is output as a numerical value. Specifically, for example, when a tomographic image acquired by OCT is input to a machine learning model trained with first training data, the model outputs the label of the imaging region captured in the image, or, depending on the design, the model outputs the probability of each imaging region label. Furthermore, when a tomographic image acquired by OCT is input to a machine learning model trained with second training data, the model outputs a labeled image. The formats and combinations of input data and output data of the pair groups constituting the training data may be implemented in a combination appropriate to the embodiment, such as one being an image and the other being a numerical value, one consisting of multiple images and the other being a string, or both being images. If the image is divided into rectangular regions for learning, the retinal layers are detected in each rectangular region. That is, a group of rectangular region images is obtained in which each pixel is labeled. The analysis unit 1902 combines each of the labeled rectangular region images in the same positional relationship as each of the rectangular region images, thereby obtaining a labeled image in which the retina is detected. Based on the output labeled image, the boundaries between each layer of the retina are detected as borders.

[0064] In this embodiment, the boundary between the ILM and NFL, the boundary between the NFL and GCL, the ISOS, the RPE, the BM, and the CSI are detected. Although not shown, other boundary lines, such as the boundary between the outer plexiform layer (OPL) and the outer nuclear layer (ONL), the boundary between the inner plexiform layer (IPL) and the inner nuclear layer (INL), the boundary between the INL and OPL, and the boundary between the GCL and IPL, may also be detected. Then, a process for smoothing the shape of the detected boundary line is performed. For example, the coordinate values ​​of the boundary line shape may be considered as time-series data based on a signal, and the shape of the boundary line may be smoothed using a Savitzky-Golay filter or smoothing processes such as a simple moving average, a weighted moving average, or an exponential moving average.

[0065] <Step S442> In step S442, the shape corrector 1903 corrects the tomographic image generated by the reconstructor 1901 or the boundary line of the retina detected by the analyzer 1902. This will be described with reference to FIG.

[0066] FIG. 5(a) is a diagram illustrating the display of a tomographic image acquired by a typical OCT device. A tomographic image is typically generated by arranging data corresponding to the angle of the scan mirror in parallel. In FIG. 5(a), the light ray always passes through point P at the center of the pupil. Point P is referred to as the pivot point. Reference numeral 501 denotes an example of data corresponding to the angle of the scan mirror, and reference numeral 502 denotes an example of data displayed by arranging data 501 in parallel. In other words, a tomographic image acquired by a typical OCT displays the captured data indicated by 501 as shown in 502 (B-scan). Therefore, there is scan distortion relative to the actual shape. Furthermore, even if the scanned ranges are different, the aspect ratio of the tomographic image displayed on the screen is the same. For example, if there are two tomographic images obtained by scanning two different ranges, one with a width W of 20 mm and a depth H of 5 mm (reference numeral 502) and the other with a width W of 15 mm and a depth H of 5 mm, the images will be displayed with the same aspect ratio within the screen area set on the display screen, even though the captured ranges are different. To display a cross-sectional image that closely resembles the actual shape, the horizontal (X) and vertical (Z) pixel sizes must be 1:1.

[0067] The correction process in the shape correction unit 1903 can be performed using known methods (e.g., JP 2012-148003 A, JP 2012-147976 A, JP 2012-147977 A, JP 2018-175258 A, etc.). These processes can correct scan distortion and aspect ratio distortion for the actual shape. Furthermore, distortion of the tomographic image caused by alignment eccentricity with the subject's eye can also be corrected. The axial length of the subject's eye can be calculated from the design value of the OCT 100, the coherence gate position for each device, the coherence gate position at the time of imaging, and the position of the retina at the time of imaging. Alternatively, the axial length can be calculated using known methods, or a value calculated using a separate device for measuring the axial length can be used.

[0068] FIG. 5(b) shows an example of a tomographic image in which scan distortion and aspect ratio distortion have been corrected by the correction process of the shape correction unit 1903. Reference numeral 511 is an example of a tomographic image displayed on a general OCT device. Reference numeral 512 is an example of the tomographic image 511 in which the pixel aspect ratio has been corrected to 1:1. Reference numeral 513 is an example of the tomographic image 511 in which the pixel aspect ratio and scan distortion have been corrected. While FIG. 5(b) shows an example of a tomographic image, the position of the retinal boundary line is also corrected in a similar manner. Furthermore, the shape correction unit 1903 may align adjacent tomographic images in the depth direction.

[0069] Although not shown, a similar correction process can also be performed on a three-dimensional motion contrast image obtained by OCT angiography (OCTA).

[0070] <Step S443> In step S443, the shape calculation unit 1904 calculates the shape of a virtual sphere of the subject's eye. The processing of the shape calculation unit 1904 will now be described with reference to FIG. 6. In FIG. 6(a), 601 shows a schematic diagram of an eyeball, where P is the pivot point, PL is the distance from the pivot point P to point L on the fundus surface of the laser light at an elevation angle θ, C is the center point of the distance from the pivot point P to the retina S (θ=0), CS is the radius of the sphere based on the center point C, and CL is the distance from the center point C to point L on the fundus surface for which the deviation rate is to be calculated. The distance from the pivot point P to the retina S can be calculated by subtracting the distance from the corneal apex to the pivot point P from the axial length. As shown in the example of FIG. 6, CS is the radius of the virtual sphere, and CL is the distance from the center point to the retina. Therefore, the lengths of CS and CL vary depending on the shape of the eyeball. The fundus surface for which the deviation rate is to be calculated is, for example, the boundary line of the BM or CSI. 6(a) right is a sphere EC with a radius CS and a center point C. Note that 602 (sphere EC) is an example of the shape of a virtual sphere.

[0071] Specifically, the OCT 100 controls the drive control unit 180 to operate the galvanometer mirror and scan the tomographic image. The galvanometer mirror is composed of an X scanner 107 and a Y scanner 110, and is driven to scan in orthogonal directions θx and θy radians. Note that depending on the configuration of the galvanometer mirror, the laser pivot point may move slightly back and forth in the X and Y scanning directions, so processing may be performed by dividing the pivot position into X and Y. The elevation angle θ can be calculated using equation 1 according to a geometric theorem.

[0072]

number

[0073] With the OCT100 that performs scanning in this manner, for example, the distance PL between the pivot point P and the scanned fundus surface can be calculated based on the position of the coherence gate of the OCT100. To obtain the three-dimensional shape of the subject's eye, the fundus surface is discretely scanned in advance to accumulate the respective distances PL and θx and θy. The radius of the virtual sphere can be obtained based on the distance between a predetermined position on the fundus and the pivot point P. More specifically, the radius of the virtual sphere can be set to CS, which is, for example, half the depth (the distance between the pivot point P and the retina S) at the center of the image (elevation angle θ=0).

[0074] PL and CS can be derived from data acquired by the OCT100. Here, the data refers to, for example, at least one of the design values ​​of the optical system of the OCT100, the coherence gate position serving as the reference for the device, and the coherence gate position at the time of imaging. For example, PL can be derived from the coherence gate position at the time of imaging point L. The coherence gate position serving as the reference for the device has a different value for each OCT device, for example. The coherence gate position at the time of imaging has a different value for each examination, for example. The data acquired by the OCT100 is an example of information related to an ophthalmic device.

[0075] CL can be calculated using the cosine law as in equation 2.

[0076]

number

[0077] Depending on the device configuration, the position of the pivot point P may be located inside the estimated virtual sphere, or differences may occur depending on the distance between the device and the eye to be examined, which will be described later. In such cases, as shown in Figure 6(b), if the vertex position of the virtual sphere is B, C' is the center point C' of the distance from the vertex B to the target retina S, and C'L can be calculated using Equation 3.

[0078]

number

[0079] <Step S444> In step S444, the deviation calculation unit 1905 calculates the deviation rate from the radius CS of the sphere EC calculated by the shape calculation unit 1904 and the distance CL from the center point C to the point L on the fundus surface. The deviation rate De can be calculated using Equation 4. Note that although Equation 4 is expressed using the equations of CL and CS, the deviation rate can also be found for C'L and C'S using the same equation.

[0080]

number

[0081] <Step S445> In step S445, the deviation calculation unit 1905 generates a two-dimensional map of the XY plane (two-dimensional deviation rate map) from the calculated deviation rate De. The two-dimensional map is generated by coloring according to the degree of deviation from the shape of the virtual sphere. For example, green is assigned to areas where the deviation rate De matches the sphere EC (95 to 105%), blue is assigned to areas where the deviation rate De is smaller than the sphere EC (95 to 80%), and red is assigned to areas where the deviation rate De is larger than the sphere EC (105 to 120%). For example, blue is assigned by gradually changing the color from light blue to navy blue as the deviation rate De value ranges from 95 to 80%. Note that the colors and values ​​shown here are merely examples and can be changed as appropriate. The two-dimensional deviation rate map is an example of a comparison result.

[0082] <Step S405> In step S405, the display control unit 191 displays the two-dimensional deviation rate map (two-dimensional map on the XY plane) generated by the deviation calculation unit 1905 on the display unit 192. An example of a display screen in this embodiment is shown in FIGS. 7(a) and 7(b). The basic screen will be described using FIG. 7(a). Reference numeral 700 denotes the entire screen, 701 denotes a patient tab, 702 denotes an imaging tab, 703 denotes a report tab, and 704 denotes a setting tab. The diagonal lines in the report tab 703 indicate the active state of the report screen. In this embodiment, an example will be described in which a report screen is displayed to confirm images after the imaging process is completed. Reference numeral 705 denotes a patient information display unit, 706 denotes an examination sort tab, 707 denotes an examination list, and the black frame in 708 indicates selection of the examination list, and the selected examination data is displayed on the screen. The examination list 707 in FIG. 7 displays thumbnails of SLO images and tomographic images. Reference numeral 710 denotes an SLO image, and 720 denotes a tomographic image. Reference numeral 711 denotes the scanning position of the tomographic image 720 on the SLO image 710, indicating the position of one scanned cross section. Note that the retinal layer boundary detected by the analysis unit 1902 can be superimposed and displayed on the tomographic image 720. Reference numeral 715 denotes a deviation rate map generated from the deviation rate De calculated by the deviation calculation unit 1905. Similar to 711, 716 denotes the scanning position of the tomographic image 720, which is shown on the deviation rate map 715.

[0083] FIG. 7(b) shows an example in which a tomographic image 730 corrected by the shape correction unit 1903, the corrected retinal layer boundary, and a sphere EC 731 calculated by the shape calculation unit 1904 are superimposed on a tomographic image. As shown in the example of FIG. 7(b), the tomographic image 730 and the sphere EC 731 indicate the degree of deviation from the sphere in the XZ plane, and the deviation rate map 715 indicates the degree of deviation from the sphere in the XY plane. Therefore, by simultaneously displaying the sphere EC 731 and the retinal layer boundary and the deviation rate map 715, it becomes easier to grasp the degree of deviation from the shape of the virtual sphere. Although not shown, the shape of the sphere EC calculated by the shape calculation unit 1904 may be superimposed on the tomographic image 720 of FIG. 7(a). In this case, it is desirable to display the shape of the sphere EC by performing an inverse transformation of the correction shown in FIG. 5.

[0084] Instead of the SLO image 710, a front image (Enface) generated from three-dimensional data generated from a tomographic image may be displayed. Furthermore, a color thickness map may be displayed in which colors are assigned according to the thickness values ​​of any multiple layers of the retina. The thickness map may be displayed in conjunction with the deviation rate map 715, or may be displayed side by side. Alternatively, the thickness map may be displayed superimposed on the SLO image 710.

[0085] Furthermore, S404 (sphere deviation estimation) does not necessarily have to be performed by the signal processing unit 190. S404 (sphere deviation estimation) may be performed, for example, by an external server connected to the control unit 200 via a network. In this case, the display control unit 191 displays the results based on the processing performed by the external server on the display unit.

[0086] According to the above-described configuration, the information processing device of this embodiment detects retinal layers from captured OCT data and generates retinal shape data from the detected layer information. The information processing device of this embodiment then estimates the shape of a virtual sphere of the subject's eye from the captured OCT data, calculates the deviation rate between the shape of the estimated virtual sphere and the shape of the retina, and displays the deviation rate as a two-dimensional deviation rate map. This operation allows the user to easily and intuitively grasp how much the shape of the subject's eye deviates from the shape of a sphere.

[0087] [Embodiment 2] In the first embodiment, an example was described in which the shape of a virtual sphere was estimated based on the position of the retina at the center of the image and the deviation rate was displayed. In this embodiment, the object is to acquire the state at the time of image capture, estimate the shape of a virtual sphere based on the image capture state, and calculate the deviation rate.

[0088] Description of components having the same functions as those in the first embodiment will be omitted here. First, the configuration of this embodiment will be described with reference to FIG. 8. FIG. 8 shows a signal processing unit 190 in this embodiment, which includes an imaging state acquisition unit 1906 that acquires the state at the time of imaging, and a correction unit 1907. The imaging state acquisition unit 1906 detects the pupil from an image of the anterior segment of the subject's eye. Alternatively, it detects a characteristic site from the retina. The correction unit 1907 calculates a correction value based on the information acquired by the imaging state acquisition unit 1906.

[0089] The process of the correction unit 1907 calculating the correction value based on the information obtained by the imaging state acquisition unit 1906 will be described with reference to FIG. 9 . FIG. 9 shows an example of an anterior segment image of the subject's eye acquired by the OCT 100. The state of the anterior segment image at the time of imaging can be used to determine the deviation in the distance between the subject's eye and the OCT 100 at the time of imaging (i.e., the working distance). The working distance is the distance between the corneal surface and the objective lens surface. The optical system of the OCT 100 is designed so that the subject's eye and pupil are the rotation center of the scan. FIG. 9(a) shows an example when the distance between the subject's eye and the device is appropriate. FIG. 9(b) shows an example when the distance between the subject's eye and the device is far. FIG. 9(c) shows an example when the distance between the subject's eye and the device is close. When imaging a subject, the operator operates the OCT 100 to adjust the distance between the device and the subject's eye appropriately, referring to the anterior segment image shown in FIG. 9 . However, sometimes the distance between the device and the subject's eye cannot be properly adjusted during imaging. In such cases, imaging is performed in a state such as that shown in Figures 9(b) and 9(c). Therefore, the imaging status acquisition unit 1906 calculates the distance between the OCT 100 and the subject's eye from an anterior segment image of the subject's eye. For example, the imaging status acquisition unit detects the pupil from each of the upper and lower anterior segment images and calculates the deviation of the pupil's center or center of gravity. When detecting the pupil using image processing, since the pupil appears darker than the iris, noise is reduced using a filter such as a Median filter or Gaussian filter, and dark areas are detected using existing binarization processing such as discriminant analysis. Then, holes are filled using dilation and erosion processing, and the pupil area can be determined by performing circle detection on the dark crescent-shaped area using a Hough transform or similar. Alternatively, the pupil area can be detected using a machine learning model such as deep learning. While the example shown here illustrates the method of detecting the pupil and determining the working distance, this is not limiting. An index (split index) not shown may be detected and the working distance may be calculated based on the detected index.

[0090] The correction unit 1907 calculates a correction value from the pupil misalignment between the upper and lower anterior eye images acquired by the imaging state acquisition unit 1906. The difference in distance between the centers (or centers of gravity) of the pupils in the upper and lower anterior eye images is calculated. For example, the upper left corner of the image is set as the origin (0, 0), and the horizontal direction is the X coordinate and the vertical direction is the Y coordinate. If the difference is positive when the X coordinate of the pupil center in the lower image is subtracted from the X coordinate of the pupil center in the upper image, the image is equivalent to the image shown in FIG. 9(b), which means that the distance between the subject's eye and the device is far. If the difference is negative when the X coordinate of the pupil center in the upper image is subtracted from the X coordinate of the pupil center in the lower image, the image is equivalent to the image shown in FIG. 9(c), which means that the distance between the subject's eye and the device is close. For example, if the deviation of the pupil center in an anterior segment image of the OCT100 corresponds to a deviation of 100 μm per pixel when converted into the distance between the subject's eye and the OCT100, the difference in the distance between the centers (or centers of gravity) of the pupils in the upper and lower images is found to calculate a correction value for the deviation in the distance between the subject's eye and the OCT100. Note that if the distance per pixel does not change at equal intervals but nonlinearly, a correction table or function is used to find the deviation per pixel from the deviation between the front and rear.

[0091] The shape calculation unit 1904 can use this value to calculate the distance from the center point to point L on the fundus surface using Equation 3. Then, the deviation calculation unit 1905 calculates the deviation rate from the radius of the sphere calculated by the shape calculation unit 1904 and the distance from the center point to point L on the fundus surface.

[0092] Next, the process in which the correction unit 1907 calculates a correction value based on information obtained by the imaging state acquisition unit 1906 will be described with reference to FIG. 10 . FIG. 10 illustrates an example in which the scan position is shifted due to gaze misalignment or the like. Such a shift can result in the estimated sphere being virtually placed in an inappropriate location. Therefore, the imaging state acquisition unit 1906 acquires the position of the macular region M from the eyeball. The macular region M may be detected from the SLO 140 image by image processing or from a tomographic image. If the macular region M cannot be detected directly, the optic disc D may be detected, and then, using the position of the optic disc D as a reference, a location a certain distance or angle to the left or right of the optic disc D may be used as a reference, depending on whether the eye is right or left. Furthermore, the macular region M may be detected based on the position of the fixation light during imaging. Alternatively, the macular region M may be detected using a machine learning model such as deep learning.

[0093] The correction unit 1907 calculates a correction value for the angle based on the deviation in the positional relationship between the position of the macular region M acquired by the imaging state acquisition unit 1906 and the position scanned by the ophthalmic apparatus. For example, the correction unit 1907 calculates an angle as shown in Equation 5.

[0094]

number

[0095] The shape calculation unit 1904 can use this angle to calculate the distance from the center point C'' to point L on the fundus surface using equation (6).

[0096]

number

[0097] Then, the deviation calculation unit 1905 calculates the deviation rate from the radius of the sphere calculated by the shape calculation unit 1904 and the distance from the center point to point L on the fundus surface. Then, as in the first embodiment, the deviation calculation unit 1905 generates a two-dimensional map of the XY plane from the deviation rate De calculated, and the display control unit 191 displays the generated two-dimensional map on the display unit 192. Note that in this embodiment, the deviation rate can be calculated in the same way even when the position of the pivot point P is inside the estimated virtual sphere, as in Fig. 6(b) of the first embodiment.

[0098] According to the configuration described above, in this embodiment, when calculating the deviation rate, the state at the time of imaging is acquired and the distance and position deviation are corrected according to that state. Then, the deviation rate is calculated and displayed as a two-dimensional deviation rate map. As a result, the deviation rate is calculated using the state of the subject's eye at the time of imaging, so the deviation rate can be calculated regardless of the state of deviation at the time of imaging.

[0099] [Embodiment 3] In the second embodiment, an example was described in which the shape of a virtual sphere is estimated using the state at the time of shooting and the deviation rate is displayed. In the present embodiment, an example will be described in which the shape of a virtual sphere is estimated using statistical values ​​and a spherical model.

[0100] Regarding the radius CS of the center point S of the distance from the sphere center C to the retina S shown in FIG. 6, the shape calculation unit 1904 can calculate it by full scan while changing the elevation angle θ as shown in Equation 7.

[0101]

number

[0102] Therefore, statistical values ​​such as the average, maximum, minimum, and median from the multiple CSs may be used as the CS of the sphere radius to calculate the shape of the virtual sphere and find the deviation rate as in embodiment 1. Note that it is not necessary to find the statistical values ​​of CSs from the entire scan area, and the statistical values ​​may be found within a limited range of elevation angles.

[0103] Alternatively, instead of estimating a sphere from the axial length of each individual, the deviation rate from a general sphere may be calculated. General spheres include, for example, a sphere corresponding to the standard axial length (24 mm), a sphere corresponding to the short axial length (22 mm), and a sphere corresponding to the long axial length (26 mm). Note that the numerical values ​​shown for the axial length are merely examples, and the numerical values ​​may differ depending on the race. Furthermore, the shape of the spherical model may be an ellipsoidal model.

[0104] When calculating the deviation rate by comparing with a general spherical model rather than a sphere estimated from the retina of the subject's eye, it is desirable to compare with a 24 mm sphere of the standard eye axis. However, it is also possible to automatically compare with the closest spherical model using the average or median CS. Alternatively, a spherical model for calculating the deviation rate may be specified by a designation unit (not shown) from the operator. When calculating the deviation rate by comparing with a spherical model, the spherical model used to calculate the deviation rate is displayed on the display unit 192 so that it is clear which spherical model was used to calculate the deviation rate, and is also stored in a memory unit (not shown).

[0105] Then, the deviation calculation unit 1905 calculates the deviation rate from the radius of the sphere calculated by the shape calculation unit 1904 and the distance from the center point to point L on the fundus surface. Then, similar to the first embodiment, the deviation calculation unit 1905 generates a two-dimensional map of the XY plane from the deviation rate De calculated, and the display control unit 191 displays it on the display unit 192.

[0106] According to the configuration described above, in this embodiment, the shape of the virtual sphere is estimated using statistical values ​​and a spherical model, and the deviation rate is calculated and displayed as a two-dimensional deviation rate map. Because statistical values ​​and a spherical model are used, the deviation rate can be calculated even if the state of the subject's eye at the time of imaging is unstable.

[0107] [Embodiment 4] In the first to third embodiments, an example was shown in which the shape calculation unit estimated the shape of a virtual sphere and displayed a two-dimensional deviation rate map. In this embodiment, an evaluation unit is further provided, and an object is to evaluate the eyeball shape and display the evaluation result.

[0108] In FIG. 11, the signal processing unit 190 includes an evaluation unit 1908. The evaluation unit 1908 evaluates the eyeball shape using the deviation rate calculated by the deviation calculation unit 1905. The evaluation by the evaluation unit 1908 will be described with reference to FIG. 12. FIG. 12 shows an example of a region evaluated by the evaluation unit 1908. First, the description will be made with reference to FIG. 12(a). Reference numeral 1200 in FIG. 12(a) is an example of a two-dimensional deviation rate map (a two-dimensional map on the XY plane) calculated by the deviation rate calculation unit 1905. Reference numerals 1201 and 1202 are examples of regions evaluated by the evaluation unit for the two-dimensional deviation rate map. For example, reference numeral 1201 shows an example of calculating the overall deviation degree within a circular range for the two-dimensional deviation rate map. Statistical values ​​such as the average, maximum, minimum, median, mode, variance, and standard deviation of the deviation are calculated as numerical values ​​for the overall deviation evaluation. 1202 shows an example of calculating the deviation between the top and bottom regions within a circular range for a two-dimensional deviation rate map. In this case, statistical values ​​are calculated for each of the top and bottom regions, and the difference or ratio of the statistical values ​​between the top and bottom regions is calculated, thereby making it possible to obtain the difference in deviation between the top and bottom regions as an evaluation value. 1203 shows an example of calculating the deviation between the left and right regions (corresponding to the nose side and ear side) within a circular range for a two-dimensional deviation rate map. In the region 1203, as in 1202, statistical values ​​are calculated for each of the left and right regions, and the difference or ratio of the statistical values ​​between the left and right regions is calculated, thereby making it possible to obtain the difference in deviation between the left and right regions as an evaluation value. 1204 and 1205 are examples of dividing the region into four regions, top, bottom, left, and right. In 1204, the divided regions are horizontal and vertical, while in 1205, they are rotated 45 degrees. In 1204 and 1205, as in 1202 and 1203, statistical values ​​can be calculated for each region, and differences or ratios with other regions can also be calculated. For example, if the upper right region of 1204 is the upper ear side, the lower right region is the lower ear side, and a comparison between the upper right and lower right regions is a comparison between the upper and lower ear sides. Similarly, the upper left region is the upper nasal side, and a comparison between the upper right and upper left regions is a comparison between the upper ear side and the upper nasal side. Furthermore, when calculating evaluation values ​​by dividing into multiple regions as in 1202 to 1205, it is not necessary to display the statistical values ​​for each region as the evaluation value. The maximum and minimum values ​​among the multiple regions may also be displayed.For example, if the maximum deviation is calculated in the region on the lower ear side, that value may be displayed.

[0109] Next, a description will be given using FIG. 12(b). FIG. 12(b) shows an example in which a separate region is set near the center of the same region as in FIG. 12(a). For example, in wide-angle fundus photography, the region near the center corresponds to the range from the macula to the optic optic disc. In particular, the region is set to evaluate the shape of the fundus in which the macula is convex near the optic optic disc, such as in posterior staphyloma. In FIG. 12(b), statistical values ​​of the degree of discrepancy are calculated for the region from the macula to the optic optic disc and other regions. Then, as described in 1202 to 1205, the difference or ratio of statistical values ​​between multiple regions can also be calculated in 1211 to 1215. In FIG. 12(b), there is one central region, but this is not limited to this. The regions may be divided so that the optic optic disc and the macula are separate. Note that while FIG. 12 shows an example in which the region is circular, this is not limited to this. Similar evaluations may be performed using elliptical or rectangular regions, or combinations thereof, instead of circles.

[0110] Furthermore, instead of calculating the numerical values ​​for each region, evaluation values ​​may be calculated based on the calculated values ​​to determine the type classification and the degree of deviation rate. For example, the type classification is performed based on the deviation rate of the superior, inferior, nasal, temporal, macular, and optic disc. For example, Type 1 may be defined as a convexity on the superior side, Type 2 as a convexity on the inferior side, Type 3 as a convexity on the nasal side, Type 4 as a convexity on the temporal side, Type 5 as a convexity on the optic disc, and Type 6 as a convexity on both the optic disc and the macula. Furthermore, instead of defining the optic disc as the only disc, types may be defined as a convexity above the optic disc or a convexity below the optic disc. The degree of deviation rate may be graded into five levels, based on the deviation rate itself, the ratio of the deviation rates between superior and inferior, the ratio of the deviation rates between nasal and temporal sides, the ratio of the deviation rates between the macula, the optic disc, and the surrounding area, etc. The eyeball is defined as being normal and close to a sphere at 1, and as being most deviated from a sphere at 5. However, the evaluation performed by the evaluation unit 1908 is not limited to this.

[0111] For example, the evaluation unit 1908 may evaluate the eyeball shape using a trained model trained using deep learning or the like. This will be described with reference to FIG. 13. FIG. 13 shows an example of a convolutional neural network (CNN). In FIG. 13, reference numeral 1300 denotes an input deviation rate map, 1301 and 1303 denote convolution layers, 1302 and 1304 denote pooling layers, 1305 denotes a fully connected layer, and 1306 denotes an output layer. The convolutional layers 1301 and 1303 perform convolution processing on input value groups according to set parameters such as the kernel size of the filter, the number of filters, the stride value, and the dilation value. Note that the number of dimensions of the filter kernel size may also be changed according to the number of dimensions of the input image. The pooling layers 1302 and 1304 perform processing to reduce the number of output value groups to be smaller than the number of input value groups by thinning or combining the input value groups. Specifically, for example, max pooling processing is used. The fully connected layer 1305 aggregates the outputs of the pooling layers. The output layer 1305 outputs the classification as a probability. That is, the trained model outputs a classification result for the input deviation rate map 1300. The classification result indicates, for example, whether the deviation rate map 1300 is classified as Type 1 to Type 6. The classification result is displayed as, for example, "Type 1." Alternatively, the classification result is displayed as, for example, "Type 1: Probability 80%, Type 2: Probability 40%." The trained model is trained, for example, using a pair of the deviation rate map and the type classification as training data. The trained model may also learn, for example, the type classification shown in the example above and other (forms other than normal or classification) as output results, and may be trained to output one of the types or other as output results. Alternatively, a deep neural network (DNN) (not shown) may be used, inputting the deviation rate value for each region and parameters representing the ratio or difference between regions, and outputting the type classification or the degree of deviation rate. Furthermore, in addition to the deviation rate value, the shape correction unit 1903 may input the value of the boundary line corrected by the shape correction unit 1903 for the boundary line of the retinal layer determined by the analysis unit 1902, and output the type classification and the degree of deviation rate.The deviation rate value and boundary line value may be input together for learning, or may be input separately for learning.

[0112] According to the configuration described above, in this embodiment, not only can the deviation rate be displayed as a map or numerical values, but also the type classification and the degree of deviation rate can be displayed.

[0113] (Variation 1) In the above-described various embodiments, the shape of the virtual sphere is estimated using information about the distance from the pivot position to the retina. However, this is not limiting. For example, a sphere may be estimated by fitting a circle or a sphere to the boundary line of the retinal layer determined by the analysis unit 1902 and the boundary line value corrected by the shape correction unit 1903. For example, the sphere can be calculated by calculating the least squares method for a circle as shown in Equation 8. In Equation 8, (a, b) are the center coordinates of the circle, and r is the radius of the circle. When estimating a two-dimensional circle, it is desirable to use two-dimensional coordinates obtained from a tomography image taken at a position corresponding to the center of the retina. The radius approximated by the two-dimensional circle is then used as the radius of the virtual sphere in the various embodiments described above.

[0114]

number

[0115] Alternatively, a three-dimensional sphere that fits the boundary line of the three-dimensional retinal layer may be found by the least squares method. By expanding Equation 8 into three dimensions, it is possible to estimate the shape of a virtual sphere that fits the boundary line corrected by the shape correction unit 1903. Note that approximation is not limited to a sphere, and an ellipsoid may also be used. The deviation calculation unit then calculates the deviation rate between the shape of the sphere or ellipsoid found by approximation and the shape of the retinal layer. The display control unit can display a deviation rate map generated based on the calculated deviation rate on the display unit.

[0116] (Variation 2) In the above-described various embodiments, examples of displaying tomographic images, SLO images, enface images, color thickness maps, and deviation rate maps have been described. However, this is not limiting. OCTA maps and vascular maps (e.g., choroidal vessels) generated from enface images may also be displayed. In high myopia, the choroid stretches backward, causing stress and potentially leading to neovascularization from the choroid. Therefore, it is meaningful to simultaneously display or analyze not only the retinal morphology but also the state of intraretinal and intrachoroidal vessels. The analysis unit may perform vascular density analysis or vascular skeleton density analysis on vascular maps, and display or switch between these numerical values ​​and the resulting 2D maps along with the deviation rate map. Furthermore, the evaluation unit may perform type classification using these vascular analysis values ​​along with the deviation rate. In other words, images and data indicating the state of blood vessels can be used for display or analysis along with images and data regarding retinal morphology.

[0117] (Variation 3) In the above-described various embodiments, the estimation of the shape of the virtual sphere and the display of the deviation rate may not only be displayed for a test at a certain time, but also for changes over time. For example, if there are test data taken on different dates for the same eye of the same subject, and deviation rates are calculated for the multiple test data, the deviation rates, type classifications, and changes over time for the multiple test data may be displayed as graphs or tables. Furthermore, a difference map between the reference test data and the test data to be compared may be generated for the deviation rate map, and these may be displayed side by side as changes over time.

[0118] (Variation 4) In the display of the various embodiments described above, in addition to the deviation rate map, a depth map may be displayed, in which depth information of the tomographic image is expressed using contour lines or colors, as information representing the shape of the tomographic image. A depth map is, for example, a two-dimensional map that expresses the depth of an arbitrary layer whose retinal shape is to be grasped from a line (plane) horizontally intersecting the center point C. This depth map is preferably displayed using colors according to depth. Furthermore, this depth map may be displayed not only using colors but also using contour lines connecting points of the same depth. Note that the reference horizontal line (horizontal plane) does not necessarily have to intersect the center point C. It may be set so that it intersects the midpoint between the center point C and the layer whose shape is to be grasped, or it may be set as a depth in millimeters from the retina S (θ=0) as the reference point toward the center point C. The deviation rate map and the depth map may be displayed side by side.

[0119] (Configuration 1) An information processing device including a display control unit that displays on a display unit a comparison result obtained by comparing the shape of a subject's eye obtained using a tomographic image of the subject's eye taken by an ophthalmic device and information related to the ophthalmic device with the shape of a virtual sphere corresponding to the shape of the subject's eye.

[0120] (Configuration 2) 2. The information processing device according to configuration 1, wherein the information about the ophthalmic device includes a position of a coherence gate of the ophthalmic device.

[0121] (Configuration 3) 3. The information processing device according to configuration 1 or 2, wherein the information about the ophthalmic device further includes at least one of a distance between the ophthalmic device and the subject's eye, and a positional relationship between the ophthalmic device and the macula of the subject's eye.

[0122] (Configuration 4) 4. The information processing device according to any one of configurations 1 to 3, wherein the comparison result is a two-dimensional map obtained based on a difference between the shape of the subject's eye and the shape of a virtual sphere corresponding to the shape of the subject's eye.

[0123] (Configuration 5) 5. The information processing device according to any one of configurations 1 to 4, wherein the comparison result is a two-dimensional map obtained based on the difference between the shape of the retina of the eye to be examined and the shape of a virtual sphere corresponding to the shape of the retina.

[0124] (Configuration 6) The information processing device according to any one of configurations 1 to 5, wherein the comparison result is a classification result in which the shape of the test eye is classified into one of a plurality of predetermined classifications, obtained based on the difference between the shape of the test eye and the shape of a virtual sphere corresponding to the shape of the test eye.

[0125] (Configuration 7) 7. The information processing device according to claim 6, wherein the classification result is obtained by inputting the comparison result into a trained model.

[0126] (Configuration 8) 8. The information processing device according to any one of configurations 1 to 7, wherein the virtual sphere is obtained based on a distance between a predetermined position on the fundus of the subject's eye and a pivot point of the subject's eye.

[0127] (Configuration 9) 9. The information processing device according to configuration 8, wherein the predetermined position is a position at an elevation angle θ=0 from the pivot point.

[0128] (Configuration 10) 10. The information processing device according to any one of configurations 1 to 9, wherein the virtual sphere is obtained based on the average, maximum, minimum, or median of distances between a plurality of positions on the fundus of the subject's eye and a pivot point of the subject's eye.

[0129] (Configuration 11) and a display control unit that displays on a display unit a comparison result between the shape of the subject's eye obtained using a tomographic image of the subject's eye taken by an ophthalmic device and the shape of a virtual sphere selected corresponding to the shape of the subject's eye.

[0130] (Configuration 12) 12. The information processing device according to configuration 11, wherein the virtual sphere is selected based on either the axial length or the race of the subject's eye.

[0131] (Configuration 13) 13. The information processing device according to claim 11, wherein the virtual sphere is selected by an operator.

[0132] (Configuration 14) an ophthalmic device for photographing an eye to be examined; an information processing device according to any one of configurations 1 to 13, which is communicably connected to the ophthalmologic device; An information processing system comprising:

[0133] (Method 1) An information processing method having a display control step of displaying on a display unit a comparison result obtained by comparing the shape of the subject's eye obtained using a tomographic image of the subject's eye taken by an ophthalmic device and information related to the ophthalmic device with the shape of a virtual sphere corresponding to the shape of the subject's eye.

[0134] (Program 1) A program that causes a computer to execute the information processing method described in Method 1.

[0135] (Other embodiments) The disclosed technology can also be realized by executing the following process. That is, the disclosed technology can also be realized by providing software (programs) that realize one or more functions of the various embodiments described above to a system or device via a network or a storage medium, and having a computer (or a CPU, MPU, or the like) of the system or device read and execute the programs. The computer has one or more processors or circuits, and may include multiple separate computers or a network of multiple separate processors or circuits to read and execute computer-executable instructions. In this case, the processor or circuit may include a central processing unit (CPU), a microprocessing unit (MPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), or a field-programmable gateway (FPGA). The processor or circuit may also include a digital signal processor (DSP), a data flow processor (DFP), or a neural processing unit (NPU). [Explanation of symbols]

[0136] 100 Optical coherence tomography device 200 control section 190 Signal Processing Unit 1904 Shape calculation part 1905 Deviation Calculation Unit 1906 Shooting status acquisition unit 1907 Correction Department 1908 Evaluation Department

Claims

1. An information processing device including a display control unit that displays on a display unit a comparison result obtained by comparing the shape of a subject's eye obtained using a tomographic image of the subject's eye taken by an ophthalmic device and information related to the ophthalmic device with the shape of a virtual sphere corresponding to the shape of the subject's eye.

2. The information processing device according to claim 1 , wherein the information about the ophthalmic device includes a position of a coherence gate of the ophthalmic device.

3. The information processing device according to claim 1 , wherein the information about the ophthalmic device further includes at least one of a distance between the ophthalmic device and the subject's eye, and a positional relationship between the ophthalmic device and the macula of the subject's eye.

4. 2. The information processing apparatus according to claim 1, wherein the comparison result is a two-dimensional map obtained based on a difference between the shape of the subject's eye and the shape of a virtual sphere corresponding to the shape of the subject's eye.

5. 2. The information processing apparatus according to claim 1, wherein the comparison result is a two-dimensional map obtained based on a difference between the shape of the retina of the eye to be examined and the shape of a virtual sphere corresponding to the shape of the retina.

6. 2. The information processing device according to claim 1, wherein the comparison result is a classification result in which the shape of the test eye is classified into one of a plurality of predetermined classifications, obtained based on the difference between the shape of the test eye and the shape of a virtual sphere corresponding to the shape of the test eye.

7. The information processing device according to claim 6 , wherein the classification result is obtained by inputting the comparison result into a trained model.

8. The information processing apparatus according to claim 1 , wherein the virtual sphere is obtained based on a distance between a predetermined position on the fundus of the subject's eye and a pivot point of the subject's eye.

9. The information processing apparatus according to claim 8 , wherein the predetermined position is a position at an elevation angle θ=0 from the pivot point.

10. The information processing device according to claim 1 , wherein the virtual sphere is obtained based on any one of an average, a maximum, a minimum, and a median of distances between a plurality of positions on the fundus of the subject's eye and a pivot point of the subject's eye.

11. and a display control unit that displays on a display unit a comparison result between the shape of the subject's eye obtained using a tomographic image of the subject's eye taken by an ophthalmic device and the shape of a virtual sphere selected corresponding to the shape of the subject's eye.

12. The information processing apparatus according to claim 11 , wherein the virtual sphere is selected based on either the axial length or the race of the subject's eye.

13. The information processing apparatus according to claim 11 , wherein the virtual sphere is selected by an operator.

14. an ophthalmic device for photographing an eye to be examined; The information processing device according to claim 1 , which is communicably connected to the ophthalmologic device; An information processing system comprising:

15. An information processing method having a display control step of displaying on a display unit a comparison result obtained by comparing the shape of the subject's eye obtained using a tomographic image of the subject's eye taken by an ophthalmic device and information related to the ophthalmic device with the shape of a virtual sphere corresponding to the shape of the subject's eye.

16. A program that causes a computer to execute the information processing method according to claim 15.

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