Ophthalmic information processing program and ophthalmic device

Abstract

To provide an ophthalmic information processing program and an ophthalmic device which make it easy for a user to intuitively understand the state of tissue of a subject eye.SOLUTION: A processor of an ophthalmic device is caused to perform: a measurement data acquisition step of acquiring visual sensitivity distribution data which is measurement data for a subject eye with a static perimeter and represents two-dimensional distribution of visual sensitivity data for the subject eye, and second distribution data representing two-dimensional distribution of second measurement data acquired for a region overlapping with a region of the visual sensitivity distribution data and having a measurement target different from that of the visual sensitivity data; and a superimposition map generating step of generating a superimposition map which is a superposition of a visual sensitivity map based on the visual sensitivity distribution data and an isopleth map based on the second distribution data made of wire frames with a closed section surrounded by one or more isopleth not filled in.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to an ophthalmologic information processing program and an ophthalmologic apparatus. [Background technology]

[0002] Conventionally, in the field of ophthalmology, a method is known in which a two-dimensional distribution of measurement data in tissue of a subject's eye is expressed and displayed as a two-dimensional graph such as a color map.

[0003] As an example, in ophthalmic OCT, various maps (such as tissue thickness maps and tissue density maps) are used as two-dimensional graphs (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-47127 Summary of the Invention

[0005] By correlating multiple maps of different types, it is expected that abnormalities in each area can be confirmed or predicted. However, in the past, sufficient consideration had not been given to enabling users to intuitively understand the correlations between maps. Another problem was that it was difficult with conventional technology to enable users to intuitively understand the distribution of at least one measurement data set using a two-dimensional map.

[0006] The present disclosure has been made in consideration of at least one of the problems of the conventional technology, and has as its technical objective to provide an ophthalmic information processing program and an ophthalmic apparatus that make it easy for a user to intuitively understand the state of the tissue of the subject's eye.

[0007] An ophthalmologic information processing program according to a first aspect of the present disclosure causes a processor of an ophthalmologic device to execute the following steps: a measurement data acquisition step for acquiring visibility distribution data, which is measurement data of a subject's eye measured by a static perimeter and represents a two-dimensional distribution of visibility data of the subject's eye; and second distribution data, which is acquired for an area overlapping with the visibility distribution data and represents a two-dimensional distribution of second measurement data having a different measurement target from the visibility data; and a superposition map generation step for generating a superposition map that superimposes a visibility map based on the visibility distribution data and an isoline map based on the second distribution data, which is a wireframe isoline map in which closed sections surrounded by one or more isolines are not filled in.

[0008]

[0009] The ophthalmologic apparatus according to the second aspect of the present disclosure executes the above-described ophthalmologic information processing program.

[0010] According to the present disclosure, the user can easily intuitively understand at least one of the correlation between the maps and the distribution of the measurement data of the subject's eye.

[0011] The present disclosure will be described below based on embodiments. An ophthalmologic information processing program according to the embodiments is executed by a processor of an ophthalmologic apparatus to process measurement data of a subject's eye.

[0012] The ophthalmologic information processing program executes at least a data acquisition step and a superposition map generation step.

[0013] In the data acquisition step, first distribution data and second distribution data are acquired by the ophthalmologic apparatus. The first distribution data represents a two-dimensional distribution of the first measurement data in the tissue of the subject's eye. The second distribution data is a two-dimensional distribution of the second measurement data. The second measurement data is measured in a tissue of the subject's eye that is different from the first measurement data. Furthermore, the second distribution data represents a two-dimensional distribution of the second measurement data in an area that overlaps with the first distribution data. That is, in the subject's eye, an area corresponding to the first distribution data and an area corresponding to the second distribution data overlap with each other (when each two-dimensional distribution is viewed from the front). Note that each measurement data may be a direct measurement value acquired as a result of eye measurement. However, this is not necessarily limited to this, and the measurement data may also be an analysis result obtained through an analysis process of the direct measurement value.

[0014] In this embodiment, two-dimensional graphs are generated for each of the first distribution data and the second distribution data, allowing visualization. The two-dimensional graphs may be contour maps. In contour maps, for example, individual values ​​of the measurement data are graded based on several thresholds, and lines connecting the measurement data corresponding to the thresholds are expressed as contours.

[0015] In the superposition map generating step, a superposition map is generated. The superposition map is formed by superimposing a contour map representing the first distribution data and a contour map representing the second distribution data. In this case, the first distribution data may be represented as a contour map in which pixels in a closed section surrounded by contours are filled with a color or density corresponding to the value of the first measurement data. Furthermore, the second distribution data may be represented as a contour map using a wire frame.

[0016] An overlaid map may be created by placing two contour maps on different display layers so that they overlap each other, with the contour map showing the second distribution data in wireframe being placed on a higher layer than the contour map showing the first distribution data.

[0017] Because the second distribution data is represented by a wireframe contour map, even if the two contour maps overlap, the examiner can easily view the first distribution data without being obstructed by the second distribution data. Furthermore, because the contour maps representing the first and second distribution data are different in representation format, the user can easily grasp the first and second distribution data in each region individually. As a result, for example, it is easy to compare the first and second distribution data in each region, making it easy to find abnormalities from the two contour maps that are displayed superimposed.

[0018] In a wireframe contour map, closed sections enclosed by adjacent contours are not filled in. Furthermore, in a wireframe contour map, each contour may be represented using a line style setting that corresponds to the value of the measurement data. For example, the line color, pattern, and line thickness of each contour may vary depending on the value of the measurement data.

[0019] In particular, in this embodiment, the distribution of measurement data in a two-dimensional region when the fundus is viewed from the front may be represented by the first distribution data and the second distribution data. Furthermore, one of the first distribution data and the second distribution data may represent a two-dimensional distribution of blood vessel information in the fundus tissue of the subject's eye. The other may represent a two-dimensional distribution of layer thickness information regarding the fundus tissue of the subject's eye.

[0020] Furthermore, each of the first distribution data and the second distribution data may represent the distribution of measurement data for one layer among the multiple layers that make up the retina, or may represent the distribution of several layers combined.

[0021] The blood vessel information may be blood vessel density information indicating the ratio of blood vessels to a unit area (volume or area) (volume ratio or area ratio, i.e., blood vessel density). The blood vessel density information may be density information for all detected blood vessels, or may be density information for some blood vessels such as capillaries. The blood vessel information is not necessarily limited to this, and may be information representing other measurement results, such as information regarding the dimensions of blood vessels. It may also be information indicating changes over time in any of these.

[0022] The two-dimensional distribution of vascular information may be a two-dimensional distribution of one of the multiple layers constituting the retina, or a two-dimensional distribution of several layers combined. The two-dimensional distribution of vascular information may be acquired, for example, by image processing of a fundus image. In the examples described below, three-dimensional motion contrast data captured by an OCT device is used as the fundus image. However, this is not necessarily limited to this, and the two-dimensional distribution of vascular information may be acquired based on a frontal image of the fundus captured by a fundus camera, SLO, or the like. In this case, the frontal image of the fundus may be a fluorescent contrast image or a reflection image based on red or infrared light.

[0023] The layer thickness information may be, for example, an actual measurement value of the layer thickness, or a comparison result between the actual measurement value and normal eye data (statistical values ​​of layer thickness in multiple normal eyes). The comparison result may be information indicating the degree of deviation between the actual measurement value and normal eye data, and may be expressed, for example, as a difference, a ratio, a deviation value, or the like. The layer thickness information may also be information indicating changes over time in any of these values. The layer thickness information may also indicate the thickness of one of the multiple layers that make up the retina, or the total thickness of several layers.

[0024] For example, when one of the first and second distribution data is a two-dimensional distribution of vascular information in the fundus tissue of the test eye and the other is a two-dimensional distribution of layer thickness information related to the fundus tissue of the test eye, a superposition map of the retinal surface layer may be generated. The superposition map of the retinal surface layer is generated based on the first and second distribution data containing information about the retinal surface layer. The retinal surface layer may include at least the retinal nerve fiber layer (NFL) and the retinal ganglion cell layer (GCL). The superposition map based on the first and second distribution data allows the examiner to easily understand the correlation between the distribution of blood vessels and the distribution of layer thickness in the retinal surface layer. In glaucoma, it is generally believed that the thickness of the retinal surface layer decreases due to retinal ganglion cell death, and that the vascular density of the retinal surface layer decreases as a secondary change associated with retinal ganglion cell death. However, recent research has revealed that some circulatory disorders precede the progression of glaucoma, and this has attracted attention. The superimposed map of the retinal surface layer described above is expected to make it possible to easily identify areas where circulatory disorders occur prior to the thinning of the layer on a single map. Therefore, it may be useful not only for checking the progression of glaucoma but also for predicting it.

[0025] When a superposition map is generated based on a two-dimensional distribution of vascular information in the fundus tissue of the test eye and a two-dimensional distribution of layer thickness information related to the fundus tissue of the test eye, it is preferable to use a contour map in which pixels in a closed section surrounded by contours are filled with a color or density corresponding to the measurement data for the two-dimensional distribution of vascular information, and a wireframe contour map in which pixels in a closed section surrounded by contours are filled with a color or density corresponding to the measurement data for the two-dimensional distribution of layer thickness information. Generally, in glaucoma, the range of change in layer thickness information on the fundus (especially the retinal surface) is wider than that of vascular information. Therefore, a contour map representing the two-dimensional distribution of layer thickness information tends to be simpler than a two-dimensional distribution of vascular information. Therefore, even if the contour map is expressed as a wireframe, it is less burdensome for the examiner to interpolate the measurement data in the closed section surrounded by contours, and the viewability of the superposition map is less impaired. Furthermore, a contour map in which pixels in a closed section surrounded by contours are filled with a color or density corresponding to the measurement data for the fundus tissue of the test eye can be more reliably identified.

[0026] In many cases of fundus abnormalities, not just glaucoma, it is believed that there are many cases in which vascular changes are more varied than changes in layer thickness. In such cases, it is preferable to use an isoline map in which pixels in closed sections surrounded by isolines are filled with a color or density corresponding to the measurement data for the two-dimensional distribution of vascular information, and an isoline map using a wireframe for the two-dimensional distribution of layer thickness information. However, this is not necessarily limited to this. For example, in cases in which changes in layer thickness lag behind changes in vascular changes and the changes in layer thickness are more varied, it is appropriate to represent the two-dimensional distribution of vascular information using a wireframe and the two-dimensional distribution of layer thickness information using an isoline map in which pixels in closed sections surrounded by isolines are filled.

[0027] Furthermore, the tissues of the subject's eye corresponding to the first distribution data and the second distribution data do not necessarily have to be the same. The first distribution data and the second distribution data may correspond to different depth regions. For example, the first distribution data and the second distribution data may correspond to different depth regions in the fundus. Furthermore, the first distribution data and the second distribution data may correspond to different depth regions in the ocular optical system (from the cornea to the fundus).

[0028] <Using perimeter measurement results> Furthermore, in the data acquiring step, the ophthalmologic apparatus may acquire third distribution data. The third distribution data may be measurement data of the subject's eye using a static perimeter. In the static perimeter, the distribution of sensitivity to visual stimuli is measured for each point on the coordinates of the fundus. Thus, a two-dimensional distribution of visual sensitivity on the fundus is acquired as the third distribution data. For example, in the case of a glaucomatous eye, the third distribution data indicates the progression of functional impairment.

[0029] In the superposition map generating step, a second superposition map may be generated. The second superposition map is generated by further superimposing a luminosity map based on the third distribution data on the superposition map. In this case, the first distribution data and the second distribution data on which the superposition map is based may each be acquired based on a three-dimensional image or a front image of the fundus. In other words, they may be morphological information. In contrast, the third distribution data, which is measurement data of the subject's eye obtained by a static perimeter, is functional information. Therefore, the second superposition map makes it possible to grasp the state of the fundus from both morphological and functional perspectives.

[0030] In particular, when the first and second distribution data are two-dimensional distributions of vascular information and layer thickness information on the retinal surface, the state of functional impairment at each location confirmed based on visual field test measurement data can be compared with the vascular information / layer thickness information. Because some circulatory disorders precede the progression of glaucoma, comparing visual field test measurement data with vascular information is expected to be useful in predicting areas where functional impairment will progress in the future. Furthermore, comparing visual field test measurement data with layer thickness information is expected to be useful in predicting areas where functional impairment or thinning will progress in the future. Verification of these predictions through follow-up tests is expected to further advance our understanding of the mechanisms of glaucoma.

[0031] Furthermore, the first distribution data and the second distribution data in the superimposed map may be, for example, a combination of the distribution of thickness information for all retinal layers and the distribution of vascular information near the RPE layer. This is useful for detecting exudative AMD. That is, abnormal areas can be easily identified based on the location of neovascularization in the RPE layer and the surrounding layer thickness information. In this case, it may be desirable to directly confirm the course of blood vessels near the RPE layer rather than the distribution of vascular information. In this case, as described below, a two-dimensional image obtained by OCT-Angiography may be superimposed on the thickness map instead of the two-dimensional distribution of vascular information.

[0032] Furthermore, the first distribution data and the second distribution data in the superimposed map may be a distribution of thickness information for all layers of the retina and a distribution of blood vessel information for the superficial layer of the retina. This is considered to be useful for detecting branch retinal artery occlusion (BRAO), which is difficult to identify in fundus camera images in the case of severe myopia.

[0033] In this embodiment, a two-dimensional distribution of blood vessel information may be used as distribution data, but instead, a two-dimensional image obtained by OCT-Angiography may be used.

[0034] In the ophthalmologic information processing program according to the second embodiment, at least a layer thickness distribution data obtaining step and a stereoscopic representation map generating step are executed.

[0035] In the layer thickness distribution data acquisition step, layer thickness distribution data representing a two-dimensional distribution of layer thickness information regarding the fundus tissue when the fundus tissue of the subject's eye is viewed from the front is acquired by the ophthalmologic device. In the three-dimensional representation map generation step, a three-dimensional representation map that imparts a three-dimensional effect to the two-dimensional distribution of layer thickness information is generated by expressing the brightness of an area where the value of the layer thickness distribution data decreases as one progresses in a predetermined one-dimensional direction (reference direction) as lower than the brightness of an area where the value of the layer thickness distribution data increases as one progresses in the same reference direction.

[0036] According to the ophthalmologic information processing program of the second embodiment, the user can easily perceive the change in the value of the layer thickness distribution data due to a three-dimensional effect, compared to when using a map in which at least one of color and density changes according to the value, and therefore the user can easily intuitively grasp the distribution of the layer thickness information.

[0037] A specific method for generating a three-dimensional representation map based on layer thickness distribution data can be selected as appropriate. For example, a three-dimensional effect can be imparted to the two-dimensional distribution of layer thickness information by expressing the shading of an area where the layer thickness distribution data value decreases as one progresses in a reference direction as darker than the imprint of an area where the layer thickness distribution data value increases as one progresses in the same reference direction. The reference direction may be, for example, a predetermined one-dimensional direction or a direction away from the reference point. A specific method for imparting shading can also be selected as appropriate. For example, shading can be imparted by reducing the brightness of pixel values ​​or by imparting a predetermined color. For example, the darkness of the shading (e.g., low brightness) can be made proportional to the amount of decrease in the layer thickness distribution data value per unit distance in the reference direction (i.e., the rate of decrease in value). In other words, the lightness of the shading (low brightness) of that area can be made proportional to the rate of decrease in the layer thickness distribution data value in the reference direction. In this case, the three-dimensional effect of the three-dimensional representation map becomes even easier to understand.

[0038] The three-dimensional representation map may also be a map in which shading corresponding to the rate of increase or decrease of values ​​in a reference direction is further added to the contour map. As described above, in the contour map, for example, individual values ​​of the layer thickness distribution data are graded based on several thresholds, and lines connecting measurement data corresponding to the thresholds are expressed as contours. In this case, the user can more intuitively grasp the distribution of layer thickness information by both the three-dimensional effect of the shading and the contours.

[0039] In addition, in the three-dimensional representation map, pixels in closed sections surrounded by isolines may be filled with a color or density corresponding to the value of the layer thickness distribution data. In this case, the user can more intuitively grasp the distribution of layer thickness information by the three-dimensional effect of the shading, the isolines, and the color or density assigned to each of the multiple closed sections.

[0040] The layer thickness distribution data values ​​acquired in the layer thickness distribution data acquisition step may be the results of comparison between the actual measured values ​​of layer thickness and normal eye data (statistical values ​​of layer thicknesses in multiple normal eyes). The comparison results may be information indicating the degree of deviation between the actual measured values ​​and the normal eye data, and may be expressed, for example, as a difference, a ratio, a deviation value, or the like. In this case, the degree of deviation from the normal eye data can be more intuitively grasped by the three-dimensional effect of shading. Furthermore, the layer thickness information may be information indicating changes over time in any of these. Furthermore, the layer thickness information may indicate information on one layer out of multiple layers constituting the retina, or may indicate a combined value of information on multiple layers.

[0041] The ophthalmologic information processing program according to the second embodiment may further include a vascular distribution data acquisition step and a superposition map generation step. In the vascular distribution data acquisition step, vascular distribution data representing a two-dimensional distribution of vascular information in a region of the fundus tissue that overlaps with the layer thickness distribution data is acquired. In the superposition map generation step, the vascular distribution data is represented as a vascular map in which pixels in a closed section surrounded by isopleths are filled with a color or density corresponding to the value of the vascular distribution data, and a superposition map is generated in which the three-dimensional representation map and the vascular map are superimposed. In this case, the user can appropriately compare the positional relationship between the vascular information represented by the vascular map and the layer thickness information represented by the three-dimensional representation map, and then appropriately experience changes in the value of the layer thickness distribution data through a three-dimensional effect. This allows the state of the fundus tissue to be more appropriately understood.

[0042] The two-dimensional distribution of vascular information may be a two-dimensional distribution of vascular information for one of the multiple layers constituting the retina, or a two-dimensional distribution of vascular information between an upper slab (boundary) and a lower slab across multiple layers. The two-dimensional distribution of vascular information may also be obtained by, for example, image processing of a fundus image (in the embodiment described below, three-dimensional motion contrast data captured by an OCT device). The vascular distribution data may also be, for example, data showing the blood flow (vascular course) of a specific layer in the fundus (e.g., a two-dimensional image obtained by OCT-Angiography). In this case, the user can appropriately compare the positional relationship between the two-dimensional distribution of blood flow and the two-dimensional distribution of layer thickness information, and then appropriately experience the changes in the values ​​of the layer thickness distribution data through a three-dimensional effect. This facilitates various judgments, such as which of blood flow or layer thickness has a greater impact on a disease, more easily.

[0043] The two-dimensional distribution of blood vessel information may be displayed three-dimensionally using shading. In this case, the three-dimensionally displayed blood vessel information makes it easier to understand the consistency of the positional relationship with other maps.

[0044] The ophthalmologic information program according to the second embodiment may further include a luminosity map superimposition step. The luminosity map indicates a two-dimensional distribution of the measurement results (luminosity) of the subject's eye obtained by a static perimeter. In the luminosity map superimposition step, the luminosity map is superimposed on a three-dimensional representation map or a superimposed map of the three-dimensional representation map and the blood vessel map. In this case, the condition of the subject's eye can be more appropriately understood from both the morphology of the subject's eye as understood by the three-dimensional representation map and the function of the subject's eye as understood by the luminosity map. [Brief explanation of the drawings]

[0045] [Figure 1] 1 is a block diagram showing an outline of the present embodiment; [Figure 2] FIG. 1 is a diagram illustrating an example of an optical system of an OCT device. [Figure 3] FIG. 10 is a diagram illustrating acquisition of motion contrast. [Figure 4] 3 is a flowchart showing the flow of operation of the device of the present embodiment. [Figure 5] 10A and 10B are diagrams for explaining a method for generating a superposition map based on a layer thickness analysis map and a blood vessel analysis map. [Figure 6] FIG. 10 is a diagram for explaining a method for generating a superposition map in the second embodiment. [Figure 7] FIG. 10 is a diagram for explaining a method for generating a superposition map in the first modified example. [Figure 8] FIG. 10 is a diagram for explaining a method for generating a superposition map in the second modified example. DETAILED DESCRIPTION OF THE INVENTION

[0046] An embodiment of an ophthalmic apparatus and an ophthalmic information processing program will be described below with reference to the drawings. Note that the following description will use an OCT analysis apparatus 1 as an example of the ophthalmic apparatus. The OCT analysis apparatus 1 shown in FIG. 1 analyzes and processes OCT data acquired by an OCT device 10.

[0047] The OCT analysis device 1 includes, for example, a control unit 70. The control unit 70 is realized by, for example, a general CPU (Central Processing Unit) 71, a ROM 72, a RAM 73, and the like. The ROM 72 stores, for example, an analysis processing program for processing OCT data, a program for controlling the operation of the OCT device 10 to obtain OCT data, initial values, etc. The RAM 73 temporarily stores, for example, various types of information.

[0048] 1, the control unit 70 is electrically connected to, for example, a storage unit (e.g., non-volatile memory) 74, an operation unit 76, and a display unit 75. The storage unit 74 is, for example, a non-transitory storage medium that can retain stored contents even when the power supply is cut off. For example, a hard disk drive, a flash ROM, a removable USB memory, etc. can be used as the storage unit 74.

[0049] Various operation instructions are input by the examiner to the operation unit 76. The operation unit 76 outputs a signal according to the input operation instructions to the CPU 71. The operation unit 76 may be, for example, at least one user interface such as a mouse, a joystick, a keyboard, or a touch panel.

[0050] The display unit 75 may be a display mounted on the main body of the apparatus 1 or a display connected to the main body. For example, a display of a personal computer (hereinafter referred to as "PC") may be used. The display unit 75 displays, for example, OCT data acquired by the OCT device 10 and the results of analysis processing on the OCT data.

[0051] Note that, for example, an OCT device 10 is connected to the OCT analysis apparatus 1 of this embodiment. The OCT analysis apparatus 1 may have an integrated configuration housed in the same housing as the OCT device 10, for example, or may have a separate configuration. The control unit 70 may acquire OCT data from the connected OCT device 10. The control unit 70 may acquire the OCT data acquired by the OCT device 10 via a storage medium.

[0052] <OCT device> Hereinafter, the outline of the OCT device 10 will be described based on FIG. 2. For example, the OCT device 10 irradiates the subject eye E with measurement light and acquires a spectral interference signal between the reflected light and the reference light. By performing predetermined processing on the spectral interference signal, OCT data is generated and acquired. The OCT device 10 mainly includes, for example, an OCT optical system 100.

[0053] <OCT optical system> The OCT optical system 100 irradiates the subject eye E with measurement light and detects a spectral interference signal between the reflected light and the reference light. The OCT optical system 100 mainly includes, for example, a measurement light source 102, a coupler (optical splitter) 104, a measurement optical system 106, a reference optical system 110, a detector 120, and the like. For details of the configuration of the OCT optical system, refer to, for example, Japanese Patent Application Laid-Open No. 2015-131107.

[0054] The OCT optical system 100 is an optical system of a so-called optical coherence tomography (OCT). The OCT optical system 100 splits the light emitted from the measurement light source 102 into measurement light (sample light) and reference light by the coupler 104. The split measurement light is guided to the measurement optical system 106, and the reference light is guided to the reference optical system 110. The measurement light is guided to the fundus Ef of the subject eye E through the measurement optical system 106. Thereafter, the detector 120 receives the interference light formed by the combination of the measurement light reflected by the subject eye E and the reference light.

[0055] The measurement optical system 106 includes, for example, a scanning unit (e.g., an optical scanner) 108. The scanning unit 108 may be provided to scan the measurement light in the X and Y directions (transverse directions) on the fundus. For example, the CPU 71 controls the operation of the scanning unit 108 based on set scanning position information and acquires OCT data based on the spectral interference signal detected by the detector 120. The reference optical system 110 generates reference light to be combined with reflected light acquired by reflection of the measurement light on the fundus Ef. The reference optical system 110 may be a Michelson type or a Mach-Zehnder type.

[0056] The detector 120 detects the interference state between the measurement light and the reference light. In the case of Fourier domain OCT, the detector 120 detects the spectral intensity of the interference light, and a depth profile (A-scan signal) in a predetermined range is acquired by Fourier transforming the spectral intensity data. Scanning the fundus Ef with the measurement light along one scan line is called a "B-scan." Two-dimensional OCT data is obtained by one B-scan. Three-dimensional OCT data is obtained based on scanning the measurement light along multiple scan lines. The three-dimensional OCT data may be acquired based on, for example, raster scanning.

[0057] The OCT device 10 may be, for example, a spectral-domain OCT (SD-OCT), a swept-source OCT (SS-OCT), or a time-domain OCT (TD-OCT).

[0058] <Frontal shooting optical system> The front imaging optical system 200, for example, images the fundus Ef of the subject's eye E from a front direction (for example, in the optical axis direction of the measurement light) to obtain a front image of the fundus Ef. The front imaging optical system 200 may be, for example, a scanning laser ophthalmoscope (SLO) device configuration (for example, see Japanese Patent Application Laid-Open No. 2015-66242), or may be a so-called fundus camera type configuration (for example, see Japanese Patent Application Laid-Open No. 2011-10944). Note that the OCT optical system 100 may also serve as the front imaging optical system 200, and the front image may be acquired based on a detection signal from the detector 120.

[0059] <Fixation target projection unit> The fixation target projection unit 300 has an optical system for guiding the gaze direction of the eye E. The projection unit 300 has a fixation target to be presented to the eye E, and can guide the eye E. For example, the fixation target projection unit 300 has a visible light source that emits visible light, and changes the presentation position of the fixation target two-dimensionally. This changes the gaze direction, and as a result, changes the area from which OCT data is acquired.

[0060] <Acquisition of motion contrast data> The OCT analysis apparatus 1 of this embodiment processes, for example, OCT data detected by the OCT device 10 to obtain motion contrast data (hereinafter abbreviated as "MC data"). The MC data may be information capturing, for example, changes in blood flow and retinal tissue in the subject's eye. For example, by capturing changes in blood flow, blood vessels in the subject's eye can be extracted.

[0061] Here, an example of a method for acquiring MC data will be described. First, the CPU 71 controls the driving of the scanning unit 108 to scan the measurement light over an area A1 on the fundus oculi Ef. In FIG. 3(a), the z-axis direction is the direction of the optical axis of the measurement light. The x-axis direction is perpendicular to the z-axis and corresponds to the left-right direction of the subject. The y-axis direction is perpendicular to the z-axis and corresponds to the up-down direction of the subject.

[0062] For example, the CPU 71 performs a B-scan along scan lines SL1, SL2, . . . , SLn in the region A1. In this way, the CPU 71 causes the measurement light to scan two-dimensionally in the x and y directions, and acquires an A-scan signal in the z direction at each scanning position.

[0063] When acquiring MC data, the CPU 71 acquires at least two pieces of OCT data at different times for the same position on the subject's eye. For example, for each scan line, the CPU 71 performs multiple B-scans at different times to acquire multiple pieces of OCT data at different times.

[0064] 3(b) shows OCT data acquired when B-scans are performed multiple times at different times on scan lines SL1, SL2, . . . , SLn. For example, FIG. 3(b) shows a case where scan line SL1 is scanned at times T11, T12, . . . , T1N, scan line SL2 is scanned at times T21, T22, . . . , T2N, and scan line SLn is scanned at times Tn1, Tn2, . . . , TnN. For example, the CPU 71 acquires multiple OCT data sets at different times on each scan line and stores the OCT data in the storage unit 74.

[0065] As described above, the CPU 71 acquires multiple pieces of OCT data for the same position at different times, and then processes the OCT data to acquire MC data. Examples of OCT data calculation methods for acquiring MC data include calculating the intensity difference or amplitude difference of complex OCT data, calculating the variance or standard deviation of the intensity or amplitude of complex OCT data (Speckle variance), calculating the phase difference or variance of complex OCT data, calculating the vector difference of complex OCT data, and multiplying the phase difference and vector difference of complex OCT signals. For example, see Japanese Patent Application Laid-Open No. 2015-131107 for an example of such a calculation method.

[0066] The CPU 71 may arrange the MC data on different scanning lines to acquire three-dimensional MC data of the subject's eye E. As described above, the MC data is not limited to the phase difference, and an intensity difference, a vector difference, or the like may also be acquired.

[0067] In this embodiment, the blood vessel region in the MC data may be emphasized (extracted) by image processing.

[0068] Next, various analysis maps generated by the OCT analysis device 1 of this embodiment will be described with reference to FIGS.

[0069] The CPU 71 performs analysis processing on each of the OCT data and the MC data.

[0070] <Analysis of layer thickness> For example, the CPU 71 may perform layer thickness analysis on the OCT data. In this case, the CPU 71 may perform segmentation processing to detect layer information from the OCT data and then acquire layer thickness information on the retinal layers of the subject's eye. The layer thickness information is measurement data obtained when the layer thickness is the measurement target. For example, the layer thickness information may be an actual measurement value of the layer thickness, or information indicating a comparison result between the actual measurement value and normal eye data (statistical values ​​of layer thicknesses in multiple normal eyes). The comparison result may be information indicating the degree of deviation between the actual measurement value and the normal eye data, and the comparison result may be expressed, for example, as a difference, a ratio, a deviation value, or the like. Furthermore, the layer thickness information may indicate the thickness of one layer (e.g., the optic nerve fiber layer) among multiple layers constituting the retina, or may indicate the total, average, or statistical value of several layers.

[0071] The CPU 71 generates and acquires an analysis map of layer thickness by analyzing the three-dimensional OCT data. The analysis map of layer thickness represents a two-dimensional distribution of layer thickness information for at least one layer of the retina.

[0072] For convenience, in the following description, the analysis map relating to layer thickness will be referred to as a "layer thickness analysis map."

[0073] The layer thickness analysis map may be a thickness map showing the layer thickness, a comparison map showing the comparison result between the layer thickness of the test eye and the layer thickness of a normal eye, a deviation map showing the deviation between the layer thickness of the test eye and the layer thickness of a normal eye by standard deviation, or a difference map showing the difference in thickness between each examination date. Note that the layer thickness of the normal eye (hereinafter referred to as normal eye data) is pre-stored in a normal eye database.

[0074] In the following explanation, unless otherwise specified, the retinal surface layer (the retinal nerve fiber layer (NFL) and retinal nerve fiber layer) This paper deals with a layer thickness analysis map showing the distribution of layer thickness in the retinal septum (including at least the ganglion cell layer (GCL)) and the results of comparison with normal eye data.

[0075] <Body blood vessel analysis processing> Furthermore, for example, the CPU 71 may perform a blood vessel analysis on the MC data to obtain blood vessel analysis information. Unless otherwise specified, the blood vessel analysis information in this embodiment is blood vessel density information. The blood vessel density information is an example of measurement data when blood vessels are the measurement target. The blood vessel density information may be the ratio (volume ratio or area ratio) of blood vessels to a unit area (volume or area). The blood vessel analysis information is not necessarily limited to blood vessel density information, and may be information representing other measurement results, such as information on blood vessel dimensions or information on blood flow velocity.

[0076] The analysis result regarding blood vessels may be the analysis result regarding one of the multiple layers that make up the retina, or may be the analysis result obtained by analyzing several layers together.

[0077] The CPU 71 analyzes the three-dimensional MC data to generate and acquire a blood vessel analysis map, which represents a two-dimensional distribution of blood vessel analysis information in at least one layer of the retina.

[0078] For convenience, in the following description, an analysis map relating to blood vessels will be referred to as a "blood vessel analysis map."

[0079] In this embodiment, the vascular analysis map shows the two-dimensional distribution of vascular density on the retinal surface. Specifically, it may be a density map showing vascular density, a difference map showing the difference in density between examination days, or other maps.

[0080] As shown in Fig. 5, each analysis map may be expressed as a contour map. In a contour map, individual values ​​of measurement data are graded based on several thresholds, and lines connecting measurement data corresponding to the thresholds are expressed as contours.

[0081] As a representation format of the isoline map, it is known to paint the pixels in the closed section surrounded by the isolines with a color or density according to the measurement data, or to construct the map mainly with lines indicating the threshold without painting (i.e., construct it as a wireframe). The representation format of each map may be the same when the layer thickness analysis map is displayed alone and when the vascular analysis map is displayed alone.

[0082] However, it is preferable that the color coding of each map is different from each other. As an example, the layer thickness analysis map of this embodiment is color-coded in the order of yellow-green ⇒ yellow ⇒ red as the difference from normal eye data increases. Furthermore, the blood vessel analysis map is color-coded in the order of red ⇒ yellow ⇒ yellow-green ⇒ blue as the blood vessel density decreases.

[0083] <Creating overlaid maps> The CPU 71 also generates a superimposed map based on two types of analysis maps: a layer thickness analysis map and a blood vessel analysis map. In the superimposed map, a layer thickness analysis map relating to the retinal surface layer and a blood vessel analysis map are superimposed. The maps are aligned appropriately, for example, based on information indicating the measurement position on the fundus of each map. The information indicating the measurement position may be, for example, position information relative to the fixation position at the time of measurement, or position information based on a certain frontal image of the fundus.

[0084] In the superimposed map shown in FIG. 5, the vascular analysis map is represented by a color map in which pixels in closed sections surrounded by isolines are filled in. On the other hand, in the superimposed map, the layer thickness analysis map is represented by a color map using a wireframe. As an example, the color map represented by a wireframe may be generated by thinning a color map in which pixels in closed sections surrounded by isolines are filled in using each threshold. In this embodiment, the wireframe representation is applied to the entire layer thickness analysis map. In the color map represented by a wireframe, the thinned isolines are color-coded according to the value of the measurement data (here, layer thickness). In the superimposed map shown in FIG. 5, the layer thickness analysis map is placed in a layer higher than the vascular analysis map. By representing the layer thickness analysis map placed in the higher layer using a wireframe, the visibility of each map is less likely to be impaired even when the color maps are superimposed on each other. As a result, it is easy to compare the blood vessel density and layer thickness in the superficial retinal layer in each region. This makes it possible to predict the occurrence of functional abnormalities in areas where the thickness of the superficial retina is reduced and circulatory disorders are present, and to confirm the progression of glaucoma. Furthermore, the distribution of areas where the thickness is reduced and circulatory disorders are present may enable the progression of glaucoma to be understood and even predicted.

[0085] <Overlaying perimeter measurement results> Furthermore, the OCT analysis device 1 can separately acquire the measurement results of the subject's eye by a perimeter as third distribution data. As shown in Fig. 5, the CPU 71 can further merge the measurement results by the perimeter onto the superposition map.

[0086] Second Embodiment Next, a second embodiment will be described with reference to Fig. 6. At least a part of the configuration and processing of the ophthalmologic apparatus etc. of the second embodiment can be the same as that of the ophthalmologic apparatus described above. Therefore, in the following, the description of the configuration and processing that can be the same as that of the above-described embodiment will be omitted or simplified.

[0087] In the second embodiment, the CPU 71 of the OCT analysis device 1 executes analysis processing, stereoscopic representation map acquisition processing, MC data analysis processing, blood vessel map acquisition processing, and superposition processing.

[0088] In the analysis process, the CPU 71 performs layer thickness analysis on the OCT data as described above. As a result, layer thickness distribution data (a "layer thickness map" in the second embodiment) showing a two-dimensional distribution of layer thickness information on fundus tissue is acquired. The layer thickness map in this embodiment is a map showing the results of comparing the actual measured values ​​of layer thickness obtained by analyzing the OCT data with normal eye data (statistical values ​​of layer thickness in multiple normal eyes). In detail, in this embodiment, a map showing the distribution of the degree of deviation between the actual measured values ​​and normal eye data for the layer thickness of the ganglion cell complex (GCC) is acquired. In the example shown in FIG. 6, red, yellow, and green are assigned to each region in descending order of the degree of deviation from the normal eye data.

[0089] In the stereoscopic representation map acquisition process, the CPU 71 generates a stereoscopic representation map that adds a three-dimensional effect to the two-dimensional distribution of layer thickness information by applying shading based on the layer thickness distribution data. Specifically, the CPU 71 renders the shading (in this example, brightness) of a region of the layer thickness distribution data (layer thickness map) where the value of the layer thickness distribution data (in this embodiment, the degree of deviation between the actual measurement value and the normal eye data) decreases as one moves along a predetermined one-dimensional reference direction (in the example shown in FIG. 6, a one-dimensional direction from the lower left to the upper right of the figure) darker (in this example, lower brightness) than the shading of a region where the value of the layer thickness distribution data increases as one moves along the same reference direction. As a result, a stereoscopic representation map is generated. It goes without saying that the reference direction can be set as appropriate. Furthermore, as described above, shading may be applied by adjusting pixel values ​​without adjusting brightness.

[0090] 6, the change in the layer thickness distribution data value can be more easily grasped by the three-dimensional representation map, compared to the layer thickness map in which at least one of the color and the density changes according to the layer thickness distribution data value. In this embodiment, the degree of deviation from the normal eye data can be intuitively grasped by the three-dimensional representation map.

[0091] In this embodiment, the CPU 71 imparts a three-dimensional effect to the layer thickness distribution data by proportionally adjusting the brightness (lightness of shading) to the amount of increase in the layer thickness distribution data value per unit distance in the reference direction (i.e., the rate of increase in value). This makes the three-dimensional effect of the three-dimensional representation map easier to understand. Furthermore, in this embodiment, the three-dimensional effect of shading is further added to the layer thickness map, which is a contour map. This allows the user to more intuitively grasp the distribution of layer thickness information through both the three-dimensional effect of shading and the contour lines. Furthermore, in this embodiment, the three-dimensional representation map fills pixels in closed sections surrounded by contour lines with a color or density corresponding to the value of the layer thickness distribution data. This allows the user to more intuitively grasp the distribution of layer thickness information through the three-dimensional effect of shading, the contour lines, and the colors or densities (red, yellow, and green in this embodiment) assigned to each of the multiple closed sections.

[0092] In the MC data analysis process and the vascular map acquisition process, the CPU 71 performs vascular analysis on the MC data. Furthermore, vascular distribution data (vascular map in the example of FIG. 6) showing the two-dimensional distribution of vascular information in the area of ​​the fundus tissue overlapping with the layer thickness distribution data is acquired. In the example of FIG. 6, two vascular maps are acquired. The first vascular map on the left is a two-dimensional image obtained by OCT-Angiography from MC data for the layer from the upper slab located at the middle of the depth direction in the ganglion cell layer (GCL) to the lower slab located at the middle of the depth direction in the inner plexiform layer (IPL). The second vascular map on the right in FIG. 6 is a two-dimensional image obtained by OCT-Angiography from MC data for the layer from the internal limiting membrane (ILM) to the lower slab located at the middle of the depth direction in the GCL.

[0093] In the superimposition process, at least one of a blood vessel map and a luminosity map is superimposed on the three-dimensional representation map. In the example shown in FIG. 6, from the bottom layer, a color fundus image, a three-dimensional representation map, a second blood vessel map, a first blood vessel map, and a luminosity map are superimposed in a registered state. As described above, the luminosity map indicates a two-dimensional distribution of the measurement results (luminosity) of the subject's eye obtained by a static perimeter. Note that the luminosity map shown in FIG. 6 uses a two-dimensional distribution of the positions of ganglion cells corresponding to each of a plurality of stimulation positions in a visual field test based on a model (in this embodiment, the well-known Drasdo model) that defines the positions of photoreceptors and the direction and distance of displacement of ganglion cells corresponding to the photoreceptors (i.e., to which signals from the photoreceptors are transmitted). Therefore, a user can easily identify the positions of ganglion cells corresponding to stimulation positions in at least one of the three-dimensional representation map and the blood vessel map.

[0094] <Modification> Although the present disclosure has been described above based on the embodiments, it is not necessarily limited to the examples, and various modifications are possible.

[0095] For example, Fig. 5 shows an example in which the entire layer thickness analysis map is expressed in a wire frame. However, this is not necessarily limited to this, and the expression in a wire frame may be applied partially.

[0096] For example, in Figure 7, the area with the most advanced thinning (area colored "red") in the grading of the layer thickness analysis map is represented by a wireframe, while the remaining areas are represented by filling in the pixels in the closed interval surrounded by isolines. In many cases, examiners focus their attention on areas of retinal thinning in particular when using a layer thickness analysis map. Therefore, by making only those areas transparent in the blood vessel density map, examiners can more efficiently identify areas that are likely to be of interest to them.

[0097] In addition, in FIG. 8, in the grading of the layer thickness analysis map, areas that are thinned compared to normal eye data (areas colored "red" and "yellow") are displayed in a wireframe, and areas with normal layer thickness are hidden. Even areas with normal layer thickness may contain areas with low blood vessel density, which may indicate an abnormality other than glaucoma. In such cases, the display format shown in FIG. 8 makes it easy to identify the abnormality.

[0098] In a distribution map of an upper layer (in the embodiment, a layer thickness analysis map) represented by a wireframe, which section (the section in the measurement data separated by a threshold value) is represented by the wireframe may be appropriately switched based on operational input by the examiner.

Claims

1. An ophthalmology information processing program, Visibility distribution data is measurement data of the subject's eye obtained by a static perimeter, and represents a two-dimensional distribution of visibility data of the subject's eye; a measurement data acquisition step of acquiring second distribution data representing a two-dimensional distribution of second measurement data, the second distribution data being acquired for an area overlapping an area of ​​the visibility distribution data and having a measurement object different from that of the visibility data; a superposition map generating step of generating a superposition map by superimposing a visibility map based on the visibility distribution data and a contour map based on the second distribution data, the contour map being a wireframe in which a closed section surrounded by one or more contours is not filled in; An ophthalmic information processing program that causes a processor of an ophthalmic device to execute the above.

2. The ophthalmologic image processing program according to claim 1 , wherein the second distribution data represents a two-dimensional distribution of layer thickness information regarding the fundus tissue.

3. 3. The ophthalmologic information processing program according to claim 1, further comprising the step of: switching, in response to an operation, a section represented by a wire frame in the contour map superimposed on the superimposed map.

4. An ophthalmologic apparatus that executes the ophthalmologic information processing program according to any one of claims 1 to 3.

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