Ophthalmic image processing program and ophthalmic examination method

Abstract

To detect highly reliable signals even when part or all of the area being analyzed contains tissue with heterogeneous morphology. [Solution] The ophthalmic examination system executes the following steps: a dataset acquisition step in which, as a result of repeatedly photographing a certain area of ​​the subject's eye using an OCT device during an examination period, a dataset consisting of a plurality of OCT data ordered in chronological order is acquired as a result of the photographing, wherein the density of A-scan points in each of the OCT data varies depending on the transverse position on the fundus; a region of interest setting step in which a region of interest is set at a corresponding position among the plurality of OCT data included in the dataset; and an analysis processing step in which time-course change information indicating time-course changes in the tissue of the subject's eye in the region of interest is acquired by an analysis processing that analyzes the dataset.

Description

[Technical Field]

[0001] The present disclosure relates to an ophthalmic image processing program and an ophthalmic examination method. [Background technology]

[0002] Various studies have been conducted to image retinal function. For example, Patent Document 1 discloses an apparatus that captures optical coherence tomography (OCT) images before and after retinal stimulation with stimulating light and extracts intrinsic signals from the retina based on the change in brightness of the OCT images before and after the stimulation. Furthermore, attempts to extract intrinsic signals using, instead of OCT images, frontal fundus images captured by a fundus camera or SLO, or cellular-level retinal images (tomographic or frontal images) captured by an apparatus equipped with adaptive optics, are also known. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2011-135933 Summary of the Invention [Problem to be solved by the invention]

[0004] (1) Because intrinsic signals are weak, they are susceptible to noise caused by the imaging system, which includes the subject's eye and the imaging optical system, posing a problem in ensuring the reliability of the intrinsic signal. For example, image data with a low signal-to-noise ratio is susceptible to noise, and noise is also generated by the movement of the subject's eye during imaging, vibration of the imaging system, etc. In response to this, the present inventors have investigated an analysis method that can suppress the influence of noise caused by the imaging system and obtain a more reliable signal.

[0005] (2) For example, retinal stimulation by stimulating light may cause multiple reactions with different time scales. Furthermore, the blood flow velocity in the blood vessels of the subject's eye varies from region to region. In response to this, the present inventors have investigated a method for appropriately detecting multiple reactions and blood flow velocities with different time scales while minimizing the burden on the examiner and the subject.

[0006] (3) In the fovea, papilla, lesions, and their vicinity (hereinafter referred to as "characteristic regions"), the tissue morphology is not homogeneous. The accuracy of intrinsic signals detected from such tissue is lower than that of intrinsic signals detected from morphologically homogeneous tissue. In response to this, the present inventors have investigated an examination method that can obtain highly reliable signals even when morphologically heterogeneous tissue is included in part or all of the region to be analyzed.

[0007] The present disclosure has been made based on at least one of the problems of the conventional techniques, and aims to (1) provide a novel analysis method capable of suppressing the influence of noise caused by an imaging system; (2) To appropriately detect multiple reactions and blood flow rates with different time scales while minimizing the burden on the examiner and the subject. (3) The technical challenge is to detect highly reliable signals even if part or all of the area being analyzed contains tissues with heterogeneous morphology. [Means for solving the problem]

[0008] (1) An ophthalmic image processing program according to a first aspect of the present disclosure is executed by a processor of an ophthalmic image processing device to cause the ophthalmic image processing device to perform the following steps: a dataset acquisition step of acquiring a dataset based on image data of a test eye, the image data being a plurality of image data ordered in chronological order; and an analysis processing step of dividing the dataset into a plurality of subsets separated by several reference times, obtaining first time-varying information indicating changes in the tissue of the test eye over time from the reference time for each subset, and acquiring second time-varying information by connecting the first time-varying information of each subset. The analysis processing step determines each subset based on an evaluation value of the changes between each image data in the dataset so that each subset does not include image data uncorrelated to the image data at the reference time.

[0009] An ophthalmological image processing apparatus according to a second aspect of the present disclosure executes the ophthalmological image processing program according to the first aspect.

[0010] An ophthalmologic image processing program according to a third aspect of the present disclosure is executed by a processor of an ophthalmologic image processing device to generate a plurality of image data ordered in time series, the image data being light stimulated. The ophthalmologic image processing device is caused to perform the following steps: a dataset acquisition step of acquiring a dataset including image data of the fundus of the test eye; a region setting step of setting a region of interest in a first tissue that responds to light stimuli and setting a reference region in a second tissue that responds less to light stimuli than the first tissue in the fundus tissue including the retina of the test eye for each of the image data; and an analysis processing step of acquiring ORG signals of the tissue of the test eye in the region of interest by analyzing the dataset, wherein the analysis processing step acquires the ORG signals in the region of interest in which time-varying components that occur commonly in the region of interest and the reference region have been reduced based on partial image data in the reference region at each time.

[0011] An ophthalmological image processing apparatus according to a fourth aspect of the present disclosure executes the ophthalmological image processing program according to the third aspect.

[0012] (2) An ophthalmic image processing program according to a fifth aspect of the present disclosure is executed by a processor of an ophthalmic examination system, causing the ophthalmic examination system to perform the following steps: a dataset acquisition step of acquiring a dataset consisting of multiple image data items ordered in chronological order as a result of a certain area of ​​the test eye being repeatedly photographed via an imaging optical system during an examination period, the dataset being a dataset in which the interval between the photographing times of each of the image data items is adjusted according to the elapsed time within the examination period; a time acquisition step of acquiring photographing time information indicating the photographing time of each of the image data items included in the dataset; an analysis processing step of acquiring time-course change information indicating changes over time in a local area of ​​the test eye during the examination period by analysis processing based on the dataset and the photographing time information; and a display control step of displaying the time-course change information on a monitor.

[0013] An ophthalmic examination method according to a sixth aspect of the present disclosure is an ophthalmic examination method performed by a processor of an ophthalmic examination system that analyzes changes over time in a subject's eye, the ophthalmic examination system comprising an illumination optical system that irradiates the subject's eye with stimulus light, an imaging optical system that images the subject's eye, and a monitor, the ophthalmic examination method irradiating the subject's eye via the illumination optical system, and during an examination period in which the stimulus light is irradiated for part or all of the period, controlling the imaging optical system to repeatedly image a certain area of ​​the subject's eye, to obtain a dataset consisting of a plurality of image data ordered in chronological order, in which the interval between the imaging times of each of the image data items is adjusted according to the elapsed time within the examination period, obtaining imaging time information indicating the imaging time of each of the image data items included in the dataset, obtaining time-lapse information indicating changes over time in a local area of ​​the subject's eye during the examination period by analytical processing based on the dataset and the imaging time information timestamp, and displaying the time-lapse information on the monitor.

[0014] (3) An ophthalmic image processing program according to a seventh aspect of the present disclosure is an ophthalmic image processing program executed by a processor of an ophthalmic examination system that analyzes changes over time in a subject's eye. When executed by the processor of the ophthalmic examination system, the ophthalmic examination system executes the following steps: a dataset acquisition step in which a certain range of the subject's eye is repeatedly photographed via an OCT device during an examination period, and the resulting dataset is made up of a plurality of OCT data items ordered in chronological order, and the density of A-scan points in each of the OCT data items varies depending on the transverse position on the fundus; a region of interest setting step in which a region of interest is set at a corresponding position among the plurality of OCT data items included in the dataset; and an analysis processing step in which time-course change information indicating changes over time in the tissue of the subject's eye in the region of interest is acquired by an analysis processing that analyzes the dataset.

[0015] An ophthalmic examination method according to an eighth aspect of the present disclosure is an ophthalmic examination method performed by a processor of an ophthalmic examination system that analyzes changes over time in a subject's eye, the ophthalmic examination system comprising: an illumination optical system that irradiates the subject's eye with stimulus light; and an OCT device that scans the fundus of the subject's eye with measurement light and captures OCT data of the fundus based on a spectral interference signal between the return light of the measurement light and the reference light; the illumination optical system irradiates the fundus of the subject's eye with stimulus light; the OCT device repeatedly captures OCT data of a certain imaging range where the stimulus light is irradiated; and the OCT device acquires, as a result of the imaging, a dataset of a plurality of the OCT data ordered in chronological order, in which the density of A-scan points in each of the OCT data varies depending on the transverse position on the fundus; regions of interest are set at corresponding positions among the plurality of OCT data included in the dataset; and time-course change information indicating changes over time in the tissue of the subject's eye in the region of interest is acquired by an analysis process that analyzes the dataset. [Brief explanation of the drawings]

[0016] [Figure 1]1 is a block diagram showing a schematic configuration of an ophthalmologic information processing system 1 according to an embodiment. [Figure 2] FIG. 1 is a schematic diagram of an optical system according to an embodiment. [Figure 3] 1 is a flowchart showing a flow of an inspection according to an embodiment. [Figure 4] 10 is a timing chart for explaining the time relationship between light stimulation and capturing of OCT data in an examination according to an embodiment. [Figure 5] FIG. 1 is a diagram showing a stimulation range in the retina, an imaging range of OCT data, and a region of interest of ORG data. [Figure 6] 10 is a flowchart illustrating an analysis process according to an embodiment. [Figure 7] 1 is a diagram for explaining a layer region to be measured on a tomographic image of a retina, where a window is indicated by a rectangle superimposed on the tomographic image. [Figure 8] The time evolution of the reliability w(t,Δt) is shown. [Figure 9] This is an example of a weighted graph constructed based on the time evolution of the reliability w(t, Δt). [Figure 10] FIG. 10 is a diagram for explaining a KT-Path derived by a route search for a weighted graph, in which KT-Path(s) are superimposed on FIG. 8. [Figure 11] FIG. 10 is a diagram showing an analysis result obtained by the analysis method of the embodiment. [Figure 12] FIG. 10 is a diagram showing the positional relationship between a stimulation range in the retina, an imaging range of OCT data, a region of interest in ORG data, and a reference region in the second embodiment. [Figure 13] 10 is a flowchart showing an analysis process according to a second embodiment. [Figure 14] FIG. 13 is a diagram showing the positions of the region of interest and the reference region on a tomographic image of the retina, corresponding to FIG. 12. [Figure 15] 10 is an example of a weighted graph constructed in the analysis processing of the second embodiment. [Figure 16]FIG. 15 is a diagram for explaining depth regions suitable for setting a region of interest and a reference region in a modified example of FIGS. 12 and 14. [Figure 17] This is a graph showing a model of the change in the morphology of the photoreceptor outer segment over time in response to a light stimulus, with the time scale adjusted to accommodate fast responses. [Figure 18] This is a graph showing FIG. 17 with a time scale adjusted to accommodate fast reactions. [Figure 19] FIG. 10 is a diagram showing operational waveforms of a scanning unit in the third embodiment. [Figure 20] The test results are shown superimposed on the graph shown in Figure 18, which shows a model of the time change in the morphology of the photoreceptor outer segments in response to light stimulation. [Figure 21] 13 is an example of a setting screen for a scan position in the fourth embodiment. [Figure 22] FIG. 10 is a diagram showing the positional relationship between a stimulation range in the retina, an imaging range of OCT data, a region of interest in ORG data, and a feature region in the fourth embodiment. [Figure 23A] 22 is a diagram showing the operation waveforms of the scanning unit corresponding to the first scan line shown in FIG. 21. FIG. [Figure 23B] 22 is a diagram showing the operation waveforms of the scanning unit corresponding to the second scan line shown in FIG. 21. FIG. [Figure 24] FIG. 22 is a diagram showing the positions of the feature region and the region of interest in the second scan line shown in FIG. 21 on a tomographic image of the retina. [Figure 25A] This is a first variation of a weighted graph. [Figure 25B] This is a second variation of the weighted graph. [Figure 25C] This is a third variation of the weighted graph. DETAILED DESCRIPTION OF THE INVENTION

[0017] [overview] The following describes embodiments according to the present disclosure. Each embodiment may be applied to part or all of the other embodiments. For example, the items grouped in < > below may be used independently or in conjunction with each other.

[0018] First Embodiment The ophthalmic image processing device according to the first embodiment executes at least a data set acquisition step and an analysis processing step. An ophthalmic image processing program for executing each step is stored in a non-transitory storage medium accessible by a processor of the ophthalmic image processing device.

[0019] In the data set acquisition step, the ophthalmologic image processing device acquires a data set including a plurality of image data items of the subject's eye, the image data items being arranged in chronological order. The plurality of image data items included in one data set may be acquired consecutively at intervals of less than one second. Furthermore, the image data items included in one data set may be acquired at the same image position on the subject's eye.

[0020] Image data of the subject's eye may be obtained using any of various modalities, as appropriate. For example, OCT data captured by an OCT device, frontal image data of the fundus captured by a fundus camera, SLO, or slit-scanning fundus imaging device, cellular image data captured by OCT or SLO with wavefront compensation, etc. may be used as image data of the subject's eye. However, the above-listed data are merely examples of image data of the subject's eye, and the present invention is not limited to these.

[0021] In the following description, unless otherwise specified, the image data of the subject's eye will be described as an image of the retina of the subject's eye. However, this is not necessarily limited to this, and the image data may be an image of a part other than the retina, such as the cornea, iris, crystalline lens, vitreous body, or sclera.

[0022] In the analysis step, the ophthalmologic image processing device divides the data set into multiple subsets separated by several reference times and calculates first time-varying information for each subset, indicating changes in the tissue of the subject's eye over time from the reference time. For example, the ophthalmologic image processing device may calculate the amount of change in the tissue in image data Δt after the reference time based at least on the image data at the reference time and the image data Δt after the reference time, and perform this process for each image data of the subset to obtain the first time-varying information for each subset. Note that each subset may include multiple image data items ordered in chronological order, with the image data at the reference time for each subset being the first. Note that the following description mainly describes a case in which the amount of change in the tissue is calculated based on changes in the phase of corresponding regions between image data, but is not necessarily limited to this. For example, the amount of change in the tissue may also be calculated based on changes in the number of pixels, intensity, etc. of corresponding tissues between image data.

[0023] The ophthalmologic image processing device also acquires second time-varying information by performing a connection process on the first time-varying information of each subset. The second time-varying information indicates, for example, a change over time in the tissue of the subject's eye during the acquisition period of the data set. The connection process may be, for example, a process of summing or integrating the first time-varying information corresponding to each subset.

[0024] Generally, the correlation between image data at a reference time and image data Δt after the reference time decreases over time (in other words, the larger Δt), and becomes uncorrelated at a sufficiently large Δt. In particular, in phase-based imaging, the correlation becomes uncorrelated in a relatively short time. The decrease in correlation can be caused by, for example, random eye movement, changes in tissue morphology, and vibrations of the imaging system. It is not possible to accurately determine changes over time from two image data sets with low correlation. In contrast, in this embodiment, a data set is divided into multiple subsets separated by several reference times, which makes it easier to suppress the decrease in correlation over time from the reference time. Highly reliable information is easily obtained as the first time-varying information in each subset, and as a result, the reliability of the second time-varying information is also improved.

[0025] The time required for decorrelation varies depending on the morphological changes of tissues during imaging and the movement of the subject's eye. The time required for decorrelation also varies depending on which of the multiple image data included in the data set is used as the image data at the reference time. Furthermore, the SN ratio of each image data also affects the time required for decorrelation. For this reason, it is necessary to determine the SN ratio in advance. Even if a dataset is divided at a predetermined time interval, it is inevitable that a subset will contain image data uncorrelated with the image data at the reference time. Therefore, it may be impossible to obtain an appropriate analysis result (time-dependent change information). In contrast, in the analysis processing step, the ophthalmologic image processing device of this embodiment determines each subset based on an evaluation value of the change between each image data in the dataset, so that each subset does not contain image data uncorrelated with the image data at the reference time. Various image information, such as the correlation between image data, phase difference, brightness change, and change in the number of pixels for the same tissue, may be used as the evaluation value for the change between image data. Of these, particularly when phase-based imaging is performed, it is desirable to use the correlation or phase difference between image data as the evaluation value. When determining subsets (dividing a dataset) based on the evaluation value, the reference time interval is not necessarily constant, and further, the number of image data in each subset may differ depending on the reference time interval. In this embodiment, by using the evaluation value of the change between each image data, the dataset can be flexibly and appropriately divided. Therefore, appropriate analysis results (first time-varying information and second time-varying information) are likely to be obtained.

[0026] In particular, the ophthalmologic image processing device of this embodiment may construct a graph relating to evaluation values ​​of changes between each image data in the dataset in the analysis processing step, and divide the dataset based on a path search for the graph. As a result of dividing the dataset based on a path search for the graph, image data with low correlation with the image data at the reference time is less likely to be included in each subset. This further improves the reliability of the first time-varying information and the second time-varying information obtained from each subset.

[0027] The graph constructed in the analysis processing step may have a graph structure that represents, for example, the evaluation values ​​between each piece of image data included in the data set. As an example, a graph may be constructed in which image data obtained at a candidate time that is a candidate for the reference time (first candidate image data) and image data for which changes over time from the candidate time are to be determined (second candidate image data) are used as nodes, and each node is connected to a temporally consecutive node. In this case, a cost may be assigned between nodes according to the evaluation value of each piece of image data. For example, when phase information of image data is used, the phase correlation or phase difference between two pieces of image data may be used as the cost. For example, when pixel values ​​(luminance values) of image data are used, the correlation between the pixel values ​​of the two pieces of image data, luminance change, etc. may be used as the cost.

[0028] <Examples of route search methods> The path search method (algorithm) for a graph may be, for example, an algorithm for solving a shortest path problem (e.g., Dijkstra's algorithm, Bellman-Ford algorithm, Warshall-Floyd algorithm, etc.). The path search algorithm is not limited to this, and any of various algorithms such as breadth-first search (best-first search, uniform-cost search, A*), depth-first search, iteratively deepening depth-first search, depth-limited search, bidirectional search, branch-and-bound algorithm, beam search, etc. may be appropriately selected. However, the above-listed methods are merely examples of path search methods, and the present invention is not limited to these.

[0029] <Optimization methods other than route search> Furthermore, the division of the data set may be optimized without constructing a graph. For example, in the analysis processing step, a bit array corresponding to the data set, which represents image data at a reference time and other data with different values, may be used, and the bit array may be evaluated using an evaluation function related to the evaluation value each time the bit array is changed, to identify the optimal bit array, and the data set may be divided according to the identified bit array. Examples of such a method for changing the bit array include a brute force method and a genetic algorithm. Any of Zoom, etc. may be appropriately selected. However, these methods are merely examples of methods for changing the bit array and are not limited thereto.

[0030] <Application to Phase-Sensitive OCT> As described above, the image data of the eye to be examined in the data set may be OCT data of the fundus of the eye to be examined. In this case, the ophthalmic image processing apparatus may acquire the first time-varying information and the second time-varying information by obtaining the change over time of the phase difference in a plurality of OCT data ordered in time series in the data set.

[0031] <Application to Optoretinography> In the present embodiment, the image data of the eye to be examined in the data set may include image data of the light-stimulated fundus. In this case, the ophthalmic image processing apparatus may acquire an ORG signal as time-varying information in the analysis processing step. The ORG signal is an endogenous light signal of the retina based on light stimulation.

[0032] <Analysis Method Using a Reference Region> The ophthalmic image processing apparatus according to the first embodiment may further execute a region setting step. In the region setting step, the ophthalmic image processing apparatus may set an interest region and a reference region for each image data. The interest region is set for the first tissue on the retina that responds to light stimulation. The reference region is set for the second tissue on the retina that has a smaller response to light stimulation than the first tissue.

[0033] In this case, the ophthalmologic image processing device may construct a graph based on partial image data in a reference region at each time in the analysis processing step. The data set may be divided into a plurality of subsets based on a path search for the graph. The ophthalmologic image processing device may then acquire first time-varying information by processing partial image data included in the region of interest at each time in each subset. The ophthalmologic image processing device may then acquire second time-varying information by performing a connection process on the first time-varying information of each subset at the reference time.

[0034] This makes it possible to acquire the first and second time-varying information in which the time-varying components occurring in common between the region of interest and the reference region are reduced, thereby further improving the reliability of the first and second time-varying information.

[0035] Second Embodiment The ophthalmologic image processing apparatus according to the second embodiment executes at least a data set acquisition step, a region setting step, and an analysis processing step.

[0036] In the data set acquisition step, the ophthalmologic image processing device acquires a data set including a plurality of image data items arranged in time series, the image data being image data of the fundus of the subject's eye that has been subjected to optical stimulation. As in the first embodiment, the fundus image data may be any of OCT data, en face image data, cellular image data, etc., as appropriate.

[0037] In the region setting step, the ophthalmologic image processing device may set a region of interest and a reference region for each image data. The region of interest is set for a first tissue on the retina that responds to light stimuli. The reference region is set for a second tissue on the retina that responds less to light stimuli than the first tissue.

[0038] For example, the reference region may be set to a tissue located outside the irradiation range of the stimulus light in the optical stimulus as the second tissue. Furthermore, if the image data is fundus OCT data, the reference region may be set to a layer region different from the region of interest. In this case, it is desirable that the layer region in which the reference region is set is a layer region that is not affected by layer thickness changes due to optical stimulus in the region of interest.

[0039] In the analysis step, the ophthalmologic image processing device analyzes the data set to acquire an ORG signal of the tissue of the subject's eye in the region of interest. In particular, in the second embodiment, the analysis process acquires an ORG signal in the region of interest from which time-varying components occurring in common in the region of interest and the reference region are reduced based on partial image data in the reference region at each time.

[0040] For example, as described in the first embodiment, a data set may be divided into multiple subsets separated by several reference times, and first time-varying information (first ORG signals) indicating changes in the tissue of the subject's eye over time from the reference times may be obtained for each subset. The first time-varying information (first ORG signals) obtained for each subset may then be connected to obtain second time-varying information (second ORG signals). For example, as in the first embodiment, a graph may be constructed based on partial image data in a reference region, and the reference times for dividing the data set may be determined based on a path search for the graph. However, this is not necessarily limited to this, and multiple subsets may be obtained by equally dividing the data set by a predetermined number. In this case, evaluation values ​​(e.g., correlation, phase difference, etc.) indicating changes from image data at the reference time in the reference region may be obtained for image data at each time in the subsets, and the first ORG signals may be corrected or connected using weights corresponding to the evaluation values. For example, when the first ORG signals in the subsets are obtained as weighted moving averages of changes over time at each time, the weights may be calculated using weights corresponding to the evaluation values. Furthermore, for example, when correlation is used as an evaluation value, the time when the correlation is equal to or less than a threshold may be interpolated using data obtained at previous and subsequent times. Furthermore, for example, the difference between information indicating the change over time of tissue obtained based on partial image data in the region of interest and information indicating the change over time of tissue obtained based on partial image data in the reference region may be acquired as an ORG signal.

[0041] Third Embodiment The ophthalmic examination system according to the third embodiment executes at least a data set acquisition step, a time acquisition step, an analysis processing step, and a display control step. The ophthalmic examination system may additionally execute at least one of an image capture control step and a light stimulation step. An ophthalmic image processing program for executing each step is stored in a non-transitory storage medium accessible by the processor of the ophthalmic examination system.

[0042] The ophthalmic examination system according to the third embodiment includes at least an ophthalmic image processing device. The ophthalmic examination system may further include an ophthalmic examination device. The ophthalmic examination device has at least one of the functions of applying light stimulation to the subject's eye and photographing the subject's eye. In the ophthalmic examination system, the ophthalmic image processing device and the ophthalmic examination device may be integrated. The ophthalmic examination system may include one or more processors, and the one or more processors may cooperate to perform each of the above steps.

[0043] <Dataset acquisition steps> In the data set acquiring step, the ophthalmic examination system acquires a data set consisting of a plurality of image data items arranged in chronological order as a result of repeatedly capturing images of a certain area of ​​the subject's eye via the imaging optical system during an examination period. In the third embodiment, the data set acquired in the data set acquiring step has the interval between the capture times of each image data item adjusted according to the elapsed time during the examination period. For example, the examination period may be divided into several sections. Specifically, the examination period may include at least a first section and a second section having a different elapsed time from the first section. In this case, for example, the ophthalmic examination system may acquire a data set in which the interval between the capture times of a plurality of image data items corresponding to the first section and a plurality of image data items corresponding to the second section is adjusted to be different from each other. The examination period may include three or more sections. In the data set, the interval between the capture times of a plurality of image data items corresponding to a third section having a different elapsed time from both the first and second sections may be different from the interval between the image data items corresponding to both the first and second sections. Furthermore, the interval between the capture times of the image data items does not necessarily have to be adjusted to be different for each section. For example, the interval between the capture times of the image data items may be adjusted to be different for each image data item.

[0044] For example, when optical stimulation is applied to the retina, it is desirable to set the interval between capture times for each image data set according to the time scale of changes in the tissue (an example of a local region). In this case, a large change occurs in the tissue within a short period of time immediately after the optical stimulation, and then the tissue changes gradually. In contrast, the multiple image data sets included in the data set are set so that the time intervals are short for those captured within a certain time period immediately after the stimulation, and long for those captured after the certain time period has elapsed, thereby obtaining a data set consisting of image data captured at time intervals appropriate for the changes in the tissue.

[0045] As in the first embodiment, the image data of the fundus may be any of OCT data, frontal image data, and cell image data, as appropriate.

[0046] <Time acquisition step> In the time acquisition step, the ophthalmic examination system acquires shooting time information indicating the shooting time of each piece of image data included in the data set. The shooting time information may be acquired, for example, at the timing when the image data is captured, for example, by referring to an internal clock. The shooting time information may be a so-called time stamp. The shooting time information may be stored as meta information in a file of each piece of image data. Furthermore, for example, the shooting time information may be a data table that associates identification information of the image data with the shooting time of the image data for each piece of image data. In this case, the shooting time information is acquired as a file separate from the image data.

[0047] <Analysis processing steps> In the analysis processing step, the ophthalmic examination system obtains time-varying information indicating the temporal changes of the examined eye during the examination period through analysis processing based on a dataset and imaging time information. In the analysis processing, at least the change in information between image data at intervals of imaging times specified by the imaging time information is determined. For example, the time-varying information may indicate a change related to the thickness of the tissue. However, it is not necessarily limited to this, and the time-varying information may indicate other changes in a local region.

[0048] In this embodiment, as a result of the analysis processing of a dataset consisting of a plurality of image data ordered in time series, time-varying information indicating the temporal changes of the examined eye during the examination period is obtained. At this time, in this embodiment, the interval of the imaging times of each image data is adjusted at least according to the elapsed time within the examination period. In other words, the interval of the imaging times of the image data is not uniform throughout the dataset. In contrast, in this embodiment, analysis processing is performed by obtaining and using the imaging time information indicating the imaging time of each image data included in the dataset. Thereby, the change of the examined eye at each imaging time can be appropriately determined, and as a result, time-varying information indicating the temporal changes of the examined eye during the examination period can be accurately obtained. <Application to Optoretinography> The ophthalmic examination system according to the third embodiment may be used for Optoretinography. In this case, the image data of the examined eye in the dataset may include the image data of the fundus oculi stimulated by light. Also, in the analysis processing step, an ORG signal is obtained as the time-varying information.

[0049] Here, when the retina is stimulated by light, a large change occurs in the tissue within a short time immediately after the light stimulation, and then it changes gradually. Therefore, in the third embodiment, immediately after the start of the light stimulation, the interval of the imaging times may be set short, and then the interval of the imaging times may be set long. In this case, it becomes easier to obtain an ORG signal with an appropriate time resolution for the change of the tissue at each timing.

[0050] In optoretinography, dark adaptation is required before the examination. However, in the third embodiment, preparations for the examination such as dark adaptation and alignment for a plurality of responses with different time scales can be completed in one go, so the time required for the examination can be significantly shortened. As a result, the burden on the examiner and the examinee can be reduced.

[0051] When the ophthalmic examination device is a device for photographing an eye to be examined, the ophthalmic examination system may further execute a photographing control step. In the photographing control step, the eye examination system changes the interval of photographing times by controlling the photographing optical system at least according to the elapsed time during the examination period. In parallel, the eye examination system continuously and repeatedly photographs a predetermined area through the photographing optical system. In this case, it is easy to freely set the interval of photographing times of each image data in the data set acquired in the data set acquisition step.

[0052] The method of acquiring a data set in which the interval of photographing times of each image data is adjusted according to the elapsed time within the examination period is not limited to the above-described photographing control. For example, in the photographing optical system, image data may be repeatedly photographed at a constant time interval regardless of the elapsed time. In this case, the interval of photographing times of each image data included in the data set may be adjusted at least according to the elapsed time during the examination period by thinning out some of the image data obtained by photographing.

[0053] <Application to Intra B-scan DOCT> The third embodiment is applicable to intra B-scan DOCT (Doppler OCT). This is particularly useful for acquiring blood flow information in large blood vessels. In this case, the ophthalmic examination system includes at least an OCT device and acquires OCT data of the subject's eye. Furthermore, in the analysis step of intra B-scan DOCT, the blood flow velocity is measured based on the phase difference between adjacent A-scans. In the present disclosure, the interval between the capture times of each OCT data is adjusted according to the elapsed time during the examination period. That is, the interval between the capture times of the flow velocity is changed according to the elapsed time. For example, even when measurements are performed on blood vessels with unknown blood flow velocities, measurement can be easily performed with a time resolution appropriate for the flow velocity in a portion of the data set. Furthermore, if the blood flow velocities in each region are known, the interval between the capture times of OCT data (here, B-scans) may be made different between a first local region and a second local region having a higher blood flow velocity than the first local region. Specifically, the interval between imaging times for the second local region may be shorter than the interval between imaging times for the first local region, which allows measurement at a time resolution appropriate for the flow velocity by increasing the frame rate for regions with high flow velocity and also reduces noise, while the acquisition of unnecessary data can be avoided by decreasing the frame rate for regions with low flow velocity.

[0054] <Multiple preset values ​​for time intervals and how to select a preset value> At least one of the number of sections for the examination period, the length of each section, and the interval between imaging times in each section (hereinafter referred to as "section information") may be changeable. For example, a plurality of section information may be prepared in advance as preset values. For example, preset values ​​may be prepared for each type of tissue to be examined, for each tissue characteristic, or for each disease type in the subject's eye. They may also be prepared for each combination of these. In this case, for example, a preset value may be automatically selected according to the type or characteristic of tissue present at the acquisition position of the OCT data or the position of the region of interest to be analyzed, or the disease type in the subject's eye. Furthermore, one of a plurality of preset values ​​prepared in advance for each tissue may be selected via a user interface.

[0055] However, in the future, if analysis processing can be performed in real time during an examination and changes in tissue can be detected in real time, the rate of change in tissue may be fed back as feedback at intervals between imaging times. In other words, if data showing rapid changes is obtained, the frame rate may be increased, and conversely, if data showing slow changes is obtained, the frame rate may be decreased.

[0056] <Fourth embodiment> The ophthalmic examination system according to the fourth embodiment executes at least a data set acquisition step and an analysis processing step. The ophthalmic examination system may additionally execute at least one of an imaging control step, a light stimulation step, and a display control step. An ophthalmic image processing program for executing each step is stored in a non-transitory storage medium accessible by the processor of the ophthalmic examination system.

[0057] The ophthalmic examination system according to the fourth embodiment includes at least an ophthalmic image processing device.

[0058] The ophthalmic examination system according to the fourth embodiment may include an irradiation optical system. The irradiation optical system is used to apply optical stimulation to the subject's eye. The irradiation optical system irradiates the subject's eye with stimulation light.

[0059] In the ophthalmic examination system, the ophthalmic image processing device and the OCT device may be integrated. The ophthalmic examination system may have one or more processors, and the one or more processors may cooperate to perform each of the above steps.

[0060] <Image capture control step and data set acquisition step> In the imaging control step, the ophthalmologic examination system according to the fourth embodiment repeatedly captures OCT data of a certain imaging range on the fundus via the OCT device.

[0061] In the data set acquisition step, a data set consisting of multiple OCT data items arranged in chronological order is acquired as a result of the imaging. In each OCT data item included in the data set, the density of A-scan points varies depending on the transverse position on the fundus. Note that the A-scan points are the acquisition positions of the A-scan OCT data items.

[0062] For example, in the imaging control step, the density of A-scan points may be varied depending on the transverse position on the fundus by sampling each A-scan OCT data while changing the scanning speed of the measurement light on the fundus during imaging. When changing the scanning speed of the measurement light on the fundus, the OCT device scans the measurement light on the fundus. In this case, the scanning method in the OCT device may be a point-scan method or a line-scan method. The OCT device may also have an optical scanner such as a galvanic scanner.

[0063] In addition, after scanning the measurement light to sample A-scan OCT data in the shooting range, the A-scan OCT data may be thinned out according to the transverse position on the fundus, thereby adjusting the density of A-scan points in the OCT data according to the transverse position on the fundus.

[0064] The density of A-scan points in OCT data may be set according to the positional relationship between the acquisition position of the OCT data and the position of the feature region. For example, A-scan points may be set at different densities between a feature region and an area outside it. As an example, A-scan points may be set densely (high density) in a feature region of the fundus and sparsely (low density) outside the feature region. Conversely, A-scan points may be set sparsely (low density) in a feature region of the fundus and densely (high density) outside the feature region. A feature region may be a region where the tissue morphology differs from the surrounding area. Examples of feature regions include the optic disc, fovea, blood vessels, lesions, abnormal areas, and their vicinity. <Region of interest setting step> The ophthalmic examination system according to the fourth embodiment sets a region of interest, which is to be analyzed in the analysis process described below, for multiple OCT data included in a data set. The region of interest is set at a corresponding position between the OCT data. For example, the region of interest may be automatically set at a predetermined position in the OCT data. Alternatively, the region of interest may be set manually via an ophthalmic image showing the imaging range of the subject's eye. The ophthalmic image may be, for example, any of the OCT data included in the data set, or may be front image data including the imaging range. The ophthalmic image is displayed on a monitor, and the region of interest is set based on a position specified on the ophthalmic image via a user interface.

[0065] <Analysis processing steps> The ophthalmic examination system according to the fourth embodiment acquires time-varying information indicating changes over time in tissue of the subject's eye in a region of interest by analyzing a data set. The amount of change in tissue may be calculated based on changes in phase and intensity of OCT data in the region of interest, changes in the number of pixels in the segmented region, etc.

[0066] Here, since the tissue morphology is not homogeneous in the fovea, papilla, lesions, and their vicinity, if these tissues are included in the region of interest that is the subject of the analysis process, large errors are likely to occur in the time-dependent change information obtained as a result of the analysis process.

[0067] In contrast, in the fourth embodiment, the density of A-scan points varies depending on the transverse position on the fundus in each OCT data set, making it easier to accurately acquire time-varying information. For example, if a region of interest includes a feature region, it is desirable that the density of A-scan points differ between the feature region and its outer region in the OCT data. In this case, if the density of A-scan points in the feature region is denser (higher density) than in the outer region, it makes it easier to accurately acquire time-varying information in the feature region. In the feature region described above, there is significant variation in the length of each part of the retinal tissue, such as the length of the outer segment. In such a feature region, the tissue thickness information estimated from the OCT data is likely to vary significantly (variance) for each OCT data due to the influence of fixational eye movement and the like. In contrast, by increasing the density of A-scan points, the variance can be reduced, and as a result, the ORG signal, which represents the time change in thickness information, can also be accurately acquired in the feature region.

[0068] On the other hand, if the density of A-scan points in the feature region is sparse (low density) relative to the outer region, time-varying information in the outer region can be more accurately acquired. Also, the measurement time can be more easily shortened. As a result, the burden on the subject can be more easily reduced, and fixation stability can be more easily improved. Note that whether the density of A-scan points in the feature region is dense (high density) or sparse (low density) relative to the outer region can be set appropriately depending on the purpose of the analysis process.

[0069] In the analysis process, a region of interest for the analysis process may be set for each local region at a different transverse position on the fundus, and the data set may be analyzed to acquire information on changes over time for each local region. Here, the local regions at different transverse positions may be set for each of two or more characteristic regions of different types, or for each of one or more characteristic regions and one or more outer regions. The densities of A-scan points set for each region of interest may be different. If the densities of A-scan points are appropriately set for each region of interest, information on changes over time in each local region can be acquired with high accuracy.

[0070] <Setting A-scan point density using fundus images> Furthermore, the ophthalmic examination system in the fourth embodiment may set the density of A-scan points at each position on the fundus based on a fundus image of the subject's eye that has been captured in advance.

[0071] The fundus image of the subject's eye may be, for example, a tomographic image (in other words, a B-scan image) or a frontal image of the fundus. The fundus image may be generated based on OCT data captured by an OCT device. The frontal image of the fundus may be captured by a fundus camera, an SLO, or the like.

[0072] In this case, the fundus image may be processed to detect the characteristic region (and / or the outer region), and different densities may be set between the characteristic region and the outer region. In this case, the density value corresponding to each region may be determined in advance. Note that any of various known methods may be used for image processing to detect the characteristic region.

[0073] Furthermore, the density of A-scan points at each position on the fundus may be set by inputting the density of A-scan points for each position on the fundus image via a user interface. In this case, for example, a characteristic region (and / or an outer region) may be detected by image processing, and then the density for each region may be input via the user interface. Furthermore, at least one local region may be designated as a region of interest at any position on the fundus image via the user interface, and input of different densities between the region of interest and an outer region of the region of interest, or between multiple regions of interest, may be accepted via the user interface. Note that when inputting the density of A-scan points or the position of the region of interest via the user interface, the ophthalmic examination system may display a fundus image on a display and accept input via the displayed fundus image.

[0074] <Acquisition of feature area position information> The ophthalmic examination system may acquire position information of the characteristic region in advance when setting the density of A-scan points at each position on the fundus. For example, the position information of the characteristic region may be detected based on a detection process on the fundus image. The type of image used to detect the position of the characteristic region and the content of the process (image processing or analysis process) used for the detection can be selected appropriately depending on the type of the characteristic region. Furthermore, the position of the characteristic region may be input via a user interface. A fundus image including the characteristic region may be displayed upon input. By specifying the position on the fundus image via the user interface, the position information of the characteristic region is input and acquired by the ophthalmic examination system.

[0075] When a fundus image is displayed when setting the acquisition position of the OCT data or the position of the region of interest, the characteristic region may be highlighted in the fundus image, allowing the acquisition position of the OCT data or the position of the region of interest to be set at a desired position relative to the characteristic region.

[0076] "In this disclosure, the term 'processor' refers to one or more hardware processors configured to execute program code (i.e., one or more instructions of a program) contained in a program. In other words, a 'processor' is a hardware device capable of executing one or more programmed processes. For example, a 'processor' may be a general-purpose or special-purpose processor, such as, but not limited to, a CPU, a microprocessor, a GPU, and a DFP (data flow processor).

[0077] In this disclosure, the term "memory" refers to one or more hardware memories that are non-transitory tangible storage media configured to store computer program code and / or data accessible to a processor. The "memory" may be implemented using memory technologies such as SRAM, SDRAM, non-volatile / flash-type memory, or other types of memory. Computer program code constituting a program may be stored in the memory and executed by a processor to cause the ophthalmic imaging device or eye examination system to perform various functions.

[0078] [Example] Examples of the respective embodiments will be described below with reference to the drawings.

[0079] The ophthalmologic information processing system 1 according to this embodiment is used to examine the function of the retina of a subject's eye. In this embodiment, imaging of the intrinsic optical signal (IOS) of the retina (referred to as ORG: Optoretinography) is performed as an example of the examination. The IOS may also be referred to as the ORG signal. The examination technique of this embodiment visualizes the intrinsic optical signal based on phase information, and therefore may also be referred to as phase-based ORG.

[0080] OCT imaging based on phase information is sometimes referred to as phase-sensitive OCT. The optical path length of neurons and photoreceptors in the retina changes by tens to hundreds of nanometers, for example, when activated by light stimulation. However, typical OCT systems lack sufficient pixel resolution to detect these changes. However, minute changes in the optical path length in the retina are reflected in the phase of the OCT signal. Therefore, phase-sensitive OCT can measure even minute changes in tissue that are difficult to visualize as pixel changes.

[0081] 1 shows a schematic configuration of an ophthalmologic information processing system 1 according to this embodiment. The ophthalmologic information processing system 1 according to this embodiment includes an OCT unit 10, a photostimulation unit 30, an SLO unit 40, an arithmetic and control unit 50, a storage device 70, and a user interface 90.

[0082] The arithmetic control unit 50 is an ophthalmologic information processing device in this embodiment. The arithmetic control unit 50 includes a CPU (processor), RAM, ROM, etc. The arithmetic control unit 50 performs analysis processing of OCT data of the subject's eye acquired via the OCT unit 10. The arithmetic control unit 50 also serves as a control unit for the entire ophthalmologic information processing system 1.

[0083] The storage device 70 is a non-transitory storage medium (NVM) that can retain its contents even when the power supply is cut off. In this embodiment, the storage device 70 stores various control and analysis programs, fixed data, etc.

[0084] The user interface 90 includes a display device and an operation input unit.

[0085] <OCTユニット> The OCT unit 10 is provided to acquire OCT data of the fundus of the subject's eye using optical interference technology.

[0086] The OCT unit 10 includes at least an OCT optical system 10a, which will be described later. In this embodiment, the OCT unit 10 is a swept-source OCT (SS-OCT), which is a type of Fourier-domain OCT. However, this is not necessarily limited to this, and the OCT unit 10 may be, for example, a spectral-domain OCT (SD-OCT) or a time-domain OCT (TD-OCT). Furthermore, in this embodiment, the OCT unit 10 is a point-scan type that two-dimensionally scans tissue with point-like measurement light, but this is not necessarily limited to this, and other scanning methods such as a line-scan method or a full-field method may also be used.

[0087] <Photostimulation unit> The light stimulus unit 30 irradiates stimulus light onto the fundus of the subject's eye. The light stimulus unit 30 has at least a stimulus light irradiating optical system 30a. The stimulus light is light in a wavelength band different from that of the measurement light and is used to stimulate the fundus. Unless otherwise specified, the stimulus light is visible light. In this embodiment, the stimulus light is white light in a wavelength band λ=400 nm to 800 nm. Note that stimulus parameters such as the wavelength band, intensity, and stimulus time of the stimulus light can be set appropriately within a range in which the desired photobleaching occurs.

[0088] <SLOユニット> The SLO unit 40 is used to capture a front image of the fundus of the subject's eye (an example of a fundus image). The SLO unit 40 has at least a front imaging optical system 40a. A front image of the fundus of the subject's eye is captured via the front imaging optical system 40a. In this embodiment, at least the front image of the fundus is captured as an observation image using infrared light. Additionally, a color image of the fundus may be captured using visible light. The front image of the fundus may be used, for example, for at least one of setting a scan position and tracking in OCT.

[0089] As will be described later, in this embodiment, the photostimulation unit 30 and the SLO unit 40 share at least a portion of their configuration.

[0090] Here, referring to FIG. 2, the optical system included in the ophthalmic information processing system 1 will be described.

[0091] <OCT optical system> The OCT optical system 10a irradiates the fundus of the eye to be examined with measurement light and detects the spectral interference signal between the measurement light and the reference light. As a result of the spectral interference signal being processed by the arithmetic control unit 50, OCT data of the fundus of the eye to be examined is acquired.

[0092] The OCT optical system 10a of the present embodiment mainly includes an OCT light source 11 (measurement light source), a coupler 14, a detector 25, and a scanning unit (optical scanner) 15. Further, the OCT optical system 10a of the present embodiment includes a dichroic mirror 16 and an objective lens 17 between the scanning unit 15 and the eye to be examined.

[0093] In the present embodiment, the OCT light source 11 is a wavelength-sweeping light source (SS-OCT light source). As the OCT light source 11, a light source that emits light having a center wavelength between λ = 1000 nm and 1100 nm may be used. In this wavelength band, the measurement light is hardly absorbed by the light-transmitting body, and it is easy to separate it wavelength-wise from other light such as stimulation light. As an example, it is assumed that a wavelength-sweeping light source with a center wavelength of λ = 1060 nm and a sweeping frequency (A-scan rate) of 200 kHz is used. Also, in this case, since the measurement light is in an invisible region for the eye to be examined, it becomes difficult for the eye to be examined to follow the optical scanning by the OCT optical system 10a. Therefore, OCT data in a state where the fixation of the eye to be examined is stable can be acquired, and as a result, good OCT data can be acquired.

[0094] Light from the OCT light source 11 is split into measurement light and reference light by the coupler 14. The measurement light is guided to the fundus Ef through the measurement optical path, and then traces back along the measurement optical path to the detector 25. The reference light is guided to the reference optical system 20. The reference optical system 20 forms a reference optical path. In this embodiment, the reference optical system 20 is a reflective optical system in which a mirror (not shown) is disposed. The reference light is reflected by the mirror and guided to the detector 25. However, the reference optical system 20 may also be a transmissive optical system.

[0095] The detector 25 detects the interference state between the measurement light irradiated onto the fundus of the subject's eye and the reference light as a spectral interference signal. The detector 25 may be a balanced detector. In the case of Fourier-domain OCT, a complex OCT signal is acquired by Fourier transforming the spectral interference signal.

[0096] By calculating the absolute value of the amplitude of each complex OCT signal, a depth profile based on the OCT signal intensity is obtained. By arranging the depth profiles obtained at each scan point, an OCT image (sometimes called an intensity OCT image) is generated in which each pixel is represented by an intensity value.

[0097] In addition, by arranging the complex OCT signals obtained at each scan point, an OCT image in which each pixel is expressed by a complex number (sometimes referred to as a complex OCT image) is generated.

[0098] The scanning unit 15 is disposed in the measurement light path and scans the fundus of the subject's eye with the measurement light. In this embodiment, the scanning unit 15 scans the fundus with the measurement light in any transverse direction. The scanning unit 15 may also repeatedly scan the subject's eye E with the measurement light. Based on a signal from the detector 25 at each scanning position of the scanning unit 15, OCT data at each scanning position is acquired.

[0099] <Stimulus light irradiation optical system> In this embodiment, the light stimulation unit 30 has a stimulation light irradiating optical system 30a. The stimulation light irradiating optical system 30a irradiates a stimulation range set on the fundus with stimulation light.

[0100] For example, the stimulus light irradiation optical system 30a mainly includes a light source 31 and a scanning unit .

[0101] The light source 31 emits a stimulating light. As an example, the light source 31 may be a white LED.

[0102] The scanning unit 35 is disposed in the optical path of the stimulus light irradiating optical system 30a (for example, at a pupil conjugate position). The scanning unit 35 deflects the stimulus light so that the stimulus light is irradiated onto a stimulus range set on the fundus. The scanning unit 35 may be realized, for example, by a combination of two optical scanners with different scanning directions.

[0103] <Frontal shooting optical system> The front photographing optical system 40a irradiates the fundus of the subject's eye with illumination light and captures a front image of the fundus based on the reflected light of the illumination light from the fundus.

[0104] The front photographing optical system 40a of this embodiment mainly includes an infrared light source 41 (illumination light source), beam splitters 43 and 45, a filter 47, and a detector 49. Furthermore, the front photographing optical system 40a of this embodiment includes a scanning unit 35, a dichroic mirror 16, and an objective lens 17 between the beam splitter 43 and the subject's eye.

[0105] The illumination optical system illuminates the imaging region of the subject's eye with observation light. The light-receiving optical system receives light reflected from the fundus by the observation light using a light-receiving element 39. Observation images are sequentially acquired based on output signals from the light-receiving element 39. The scanning unit 35 scans the light two-dimensionally over the fundus of the subject's eye. The scanning unit 35 may include, for example, a combination of a polygon mirror and a galvano scanner.

[0106] The observation light from the light source 41 passes through the beam splitters 43 and 45, and then reaches the scanning unit 35 through the focusing lens 34. The light passing through the scanning unit 35 passes through the dichroic mirror 16 and is then irradiated onto the fundus of the eye to be examined through the objective lens 17. The fundus reflected light is guided back along the path during light projection to the beam splitter 45, and in FIG. 2, it is guided to the optical path on the reflection side and received by the light receiving element 49. Based on the light receiving signal from the light receiving element 49, a frontal image of the imaging site is formed. The filter 47 cuts the stimulation light when acquiring the observation image simultaneously with the light stimulation. However, by alternately performing the light stimulation and the acquisition of the observation image in a time-sharing manner, the light stimulation and the acquisition of the observation image may be substantially performed simultaneously. In this case, the filter 47 is unnecessary.

[0107] <OCT-ORG examination> Next, referring to FIGS. 3 to 5, the details of the examination using the ophthalmic information processing system 1 will be described.

[0108] In the following, the procedure in the case of using an animal eye (rabbit eye) as the eye to be examined will be described, but it is considered that there is no significant difference in the procedure even when examining a human eye. However, the examination conditions may be appropriately changed according to the human eye.

[0109] In this embodiment, while optically stimulating the fundus of the eye to be examined, OCT data of the fundus is continuously acquired in each of the periods before, during, and after the light stimulation. By analyzing the plurality of continuously acquired OCT data, information regarding the functional activity in the retina of the eye to be examined is obtained.

[0110] <Examination procedure> FIG. 3 shows a flowchart representing the entire examination procedure.

[0111] At the time of the examination, the eye to be examined is sufficiently dark adapted (S1). For example, for experimental animals, general anesthesia and muscle relaxant treatment are performed, and contact lenses are worn and the animals are dark adapted for 20 minutes.

[0112] Next, the acquisition position (scan position) of the OCT data is set (S2). For example, a display device displays a frontal fundus image and superimposes a graphic indicating the scan position (scan line) on the frontal fundus image. An operation to move the position of the scan line may be input, and the scan position on the fundus may be changeable based on the operation. The scan position may also be predetermined with respect to the fundus.

[0113] Next, optical stimulation is applied to the subject's eye, and a data set of OCT data is acquired (S3). The data set of OCT data is composed of multiple OCT data (B scans) captured at consecutive times. Each OCT data is ordered in chronological order by a timestamp (an example of capture time information) indicating the date and time of capture. The calculation and control unit 50 may acquire a timestamp corresponding to each OCT data by referring to a clock (not shown) each time one frame of OCT data is captured. In this embodiment, the calculation and control unit 50 controls the OCT unit 10 to repeatedly acquire OCT data (B scans) from the same scan line, thereby acquiring the data set.

[0114] Here, the acquisition period of OCT data and the irradiation timing of stimulus light will be described using the timing chart of FIG.

[0115] As shown in Figure 4, OCT data acquisition begins before the stimulus. During the data set acquisition period, the stimulus light is irradiated. As shown in Figure 4, the stimulus light is irradiated continuously for 6 seconds. The intensity of the stimulus light is adjusted in advance so that the fundus bleaching level reaches 63% as a result of the 6-second light stimulus.

[0116] In this embodiment, OCT data (B-scans) are repeatedly acquired at 60 millisecond intervals. In this case, 200 frames (12 seconds) of OCT data are acquired before the optical stimulus, 100 frames (6 seconds) during the optical stimulus, and 1500 frames (90 seconds) after the optical stimulus. However, the stimulus conditions and the examination conditions, such as the OCT data acquisition rate, are merely examples and can be changed as appropriate. After the acquisition of the data set is completed, the acquired data set is stored in the storage device 70.

[0117] 5 shows the positional relationship between the OCT data acquisition range and the stimulation range on the fundus. The OCT data acquisition range is indicated by a scan line 110. The OCT data acquisition range and the stimulation range 120 at least partially overlap. In this embodiment, a region of interest (ROI) 130 for analysis processing is set in a portion of the OCT data that is included in the stimulation range, and information regarding the functional activity of the ROI 130 is acquired.

[0118] In this embodiment, after the optical stimulation and acquisition of the data set are completed, an analysis process is performed on the multiple OCT data included in the data set to acquire ORG data (S4).

[0119] The ORG data may be image data that visualizes the ORG signal, which is obtained by processing a plurality of OCT data, or may be data that indicates the change over time of the ORG signal.

[0120] Next, the analysis process in the embodiment will be described with reference to Figures 6 to 11. Figure 6 is a flowchart showing the flow of the analysis process, and the description will be made according to this flowchart.

[0121] First, the calculation and control unit 50 identifies the layer region to be measured in each OCT data set (S11). Hereinafter, one of the outermost layers (or layer boundaries) to be measured will be referred to as lay1 (z1), and the other will be referred to as lay2 (z2). Which of the multiple layers constituting the retina is selected as lay1 or lay2 can be changed appropriately for each examination depending on the purpose. In this example, as shown in FIG. 7, a case will be described where the line of the inner segment of the photoreceptor cell (Ellipsoid Zone: EZ) is laid1 and the retinal pigment epithelium (RPE) is laid2. The calculation and control unit 50 sets windows of appropriate sizes as the region of interest 130 for each of lay1 and lay2 (S12).

[0122] To identify the layer regions, the calculation and control unit 50 may perform retinal layer segmentation on each OCT data. For example, image processing on the intensity OCT image may identify (detect) at least one of the multiple layers constituting the retina. It is desirable that retinal segmentation identify at least two layers, lay1 and lay2. For retinal layer segmentation, known image processing such as edge detection may be used, or a mathematical model previously trained using a machine learning algorithm may be used. For complex OCT images, for example, the results of retinal segmentation on intensity OCT images based on the same OCT data may be applied.

[0123] <Knox-Thompson method> Next, the calculation control unit 50 analyzes the phase information in the OCT data using an analysis method called the KT method (Knox-Thompson method).

[0124] A complex OCT image at a certain time t is denoted as I(x,z,t). Each pixel in a complex OCT image is a complex number. Within I(x,z,t), we focus on two layers, lay1 and lay2. The time-dependent phase difference between the two layers, lay1 and lay2, is defined as Δφ lay1 / lay2It is written as (t,Δt). Δφ lay1 / lay2 (t, Δt) denotes the time-dependent phase difference between two layers at time t+Δt, where t is the reference time.

[0125] where Δφ lay1 / lay2 (t, Δt) is calculated based on the complex OCT image according to the following equation (1): where U denotes the cross spectrum and M denotes the pixel or total number of pixels in the window.

[0126]

number

[0127]

number

[0128]

number

[0129]

number

[0130]

number

[0131] For example, Δφ lay1 / lay2 When calculating (t, Δt) or ΔL(t, Δt), if the subject's eye moves between time t and time t+Δt, noise will be generated due to the movement of the subject's eye caused by the imaging system.

[0132] In contrast, the KT method attempts to reduce noise by updating the reference time t. The KT method introduces the concept of KT-Path (Knox-Thompson Path), which divides a dataset into several sets (subsets) with different reference times t. A KT-Path is a unit of set to which the same reference time is applied, and several reference times and the same number of KT-Paths are set for one dataset. In other words, the dataset is divided into subsets for each KT-Path. The KT method connects the analysis results obtained for each KT-Path, thereby obtaining more likely values ​​as analysis results from the start time of the analysis to each time.

[0133] Here, the calculation control unit 50 constructs a weighted graph corresponding to the data set (S13). To construct the weighted graph, a reliability w(t, Δt) is introduced. The reliability w(t, Δt) is the phase correlation of each pixel in two complex OCT images at time t and time t+Δt. w(t, Δt) can be calculated based on the following equation (6):

[0134]

number

[0135] The calculation control unit 50 calculates w(t, Δt) for each combination of time t and time t+Δt. That is, it calculates the time evolution of w(t, Δt). As an example, FIG. 8 visualizes the time evolution of w(t, Δt). In FIG. 8, the horizontal axis represents time t and the vertical axis represents Δt. For convenience, however, in FIG. 8, the times t and Δt are expressed in units of the number of OCT frames. For each element identified by (t, Δt), a reliability w(t, Δt) is assigned based on the above calculation. In the graph of FIG. 8, each element is color-coded according to its reliability w(t, Δt).

[0136] When the time evolution of w(t, Δt) is simply expressed in the range of t, Δt = 0, 1, 2, a graph like that shown in Figure 9 is constructed. In this embodiment, the calculation control unit 50 constructs a weighted graph in which the time evolution of w(t, Δt) is directly reflected in a directed graph. The constructed weighted graph is shown in Figure 9.

[0137] Here, the cost C(i,j) of an edge in the graph is defined as the inverse of w(t,Δt) (see equation (7)).

[0138]

number

[0139] The calculation control unit 50 identifies a KT-Path by performing a path search on the constructed weighted graph (S14). In this embodiment, the shortest path (path with the smallest sum or product of costs) in the weighted graph is searched for by Dijkstra's algorithm, which is one of the shortest path search methods. However, the path search algorithm is not necessarily limited to this, and various algorithms can be used. For example, any of various algorithms for solving shortest path problems other than Dijkstra's algorithm (e.g., Bellman-Ford algorithm, Warshall-Floyd algorithm, etc.), breadth-first search (best-first search, uniform-cost search, A*), depth-first search, iteratively deepening depth-first search, depth-limited search, bidirectional search, branch-and-bound method, beam search, etc. may be used as appropriate.

[0140] In the shortest path of the weighted graph, each portion from the node j=0 corresponding to the OCT data at the reference time, moving vertically, to the point where it turns back corresponds to one KT-Path. In other words, the time when the shortest path turns back is set as the reference time t for the next KT-Path, and a KT-Path is identified for each reference time t. As an example, Figure 10 shows KT-Paths superimposed on the time evolution of w(t,Δt).

[0141] Next, the calculation control unit 50 calculates Δφ for each KT-Path. lay1 / lay2 (t, Δt). In addition, the calculation control unit 50 calculates Δφ lay1 / lay2 (t, Δt) is connected at the reference time to determine the change in phase difference from the start time of the analysis to each time (S15).

[0142] The analysis results obtained by the analysis method of Example 1 are shown in Fig. 11. Fig. 11 is a graph showing the amount of change in phase difference and layer thickness at each time from the start to the end of acquisition of the data set.

[0143] FIG. 11 shows four series, A to D, with different stimulation conditions and analysis conditions. The stimulation conditions and analysis conditions for series A to D are as follows. Series A and C are comparative examples, and are the results of analysis performed using a KT-Path that separates data sets at regular time intervals, while series B and D are the results of analysis performed using the analysis method of this example. Series A and series B, and series C and series D are each based on the same data set, but using different analysis methods. Series A: No light stimulation. Uniform KT-Path Series B: No light stimulation. KT-Path based on Dijkstra algorithm Series C: With light stimulation. Uniform KT-Path Series D: With light stimulation. KT-Path based on Dijkstra algorithm As shown in Figure 11, in series A and C, the values ​​change discontinuously in places, but in series B and D, the values ​​change continuously overall. Furthermore, in series B, where no optical stimulation was performed, the change is constant at approximately 0, while in series D, where optical stimulation was performed, the layer thickness changes rapidly during the period when optical stimulation was performed, followed by a gradual change in layer thickness, accurately capturing the state of retinal functional activity. Thus, the analytical method of this example can provide highly reliable analytical results.

[0144] <Second Example> Next, the second embodiment will be described, focusing on the differences from the first embodiment, with reference to Figures 12 to 16. In the following description, the description of the first embodiment will be used for matters such as device configuration that are not mentioned.

[0145] In the second embodiment, similar to the first embodiment, a region of interest 130 and a reference region 140 are set for each of a plurality of OCT data sets continuously acquired before, during, and after stimulation. In the retina of the subject's eye, the KT-Path in the data set is determined based on image data of a reference region 140, which is different from the region of interest, rather than the region of interest in each OCT data set where the measurement target exists. Based on the determined KT-Path, temporal changes in the region of interest (e.g., phase change, layer thickness change, etc.) are calculated. Preferably, the reference region 140 is set in tissue that does not generate an ORG signal based on optical stimulation. As an example, in the second embodiment, the reference region 140 is set outside the stimulation range 120 on the retina, as shown in FIG. 12 .

[0146] <Analysis process in the second embodiment> Next, the analysis process in the second embodiment will be described with reference to FIGS.

[0147] The explanation will be made with reference to the flowchart in Fig. 13. First, the calculation and control unit 50 specifies the region of interest 130 and the reference region 140 in each OCT data in the data set (S21). As shown in Fig. 14, the calculation and control unit 50 sets windows of appropriate sizes for each of lay1 and lay2 as the region of interest 130. Similarly, the calculation and control unit 50 sets windows of appropriate sizes for each of lay1 and lay2 as the reference region 140.

[0148] Thereafter, the calculation control unit 50 constructs a weighted graph based on the image data in the reference region (S22).

[0149] First, the calculation control unit 50 calculates the phase difference Δφ given by the equation (1). lay1 / lay2 (t, Δt) is calculated between two windows set as reference regions, and the time evolution of the phase difference is calculated.

[0150] In the second embodiment, as shown in Figure 14, a weighted graph is constructed from the time evolution of the phase difference. The phase difference is used as the cost. A route that minimizes the cumulative sum of the absolute values ​​of the phase difference calculated by the following formula is searched for by a route search algorithm. As in the first embodiment, the Dijkstra algorithm can be used as the route search algorithm.

[0151]

number

[0152] By searching the weighted graph described above using a path search algorithm, a path with less system phase noise due to eye movement or the like is appropriately selected. Based on this path, multiple KT-Paths in the data set are identified. The calculation control unit 50 uses the identified KT-Paths to calculate ORG data for the region of interest based on equation (1). This allows for highly reliable ORG data for the region of interest to be obtained.

[0153] In the second embodiment, as shown in Figures 12 and 14, a reference region 140 was set outside the stimulation range 120 on the retina. However, this is not necessarily limited to this, and the reference region 140 may be set in tissue that does not generate an ORG signal based on light stimulation, even if it is within the stimulation range on the retina. As an example, as shown in Figure 16, reference regions may be set in the superficial layers of lay3 (z3) and lay4 (z4) relative to lay1 and lay2 where regions of interest are set.

[0154] <Third Example> Next, the third embodiment will be described with reference to Figures 17 to 20, focusing on the differences from the first embodiment. In the following description, the description of the first embodiment will be used for matters such as device configuration that are not mentioned.

[0155] First, Figures 17 and 18 show a model of the temporal change in the morphology of photoreceptor outer segments in response to light stimulation. The temporal change in morphology is shown as the change in optical path length (ΔOPL [nm]). Figure 18 shows an enlarged view of the area enclosed by the dashed line in Figure 17. Photoreceptor outer segments exhibit two types of responses to light stimulation, each with a different timescale. As shown in Figure 18, the outer segments contract on a short timescale immediately after stimulation. Then, as shown in Figure 17, they expand on a long timescale. Of the two types of responses shown in Figures 17 and 18, the response with a short timescale is called the "early response," and the response with a long timescale is called the "late response." The early response is believed to be due to "shrinkage of the outer segment due to electrical repulsion caused by hyperpolarization of the disc membrane." The late response is believed to be due to "a combination of components resulting from osmotic expansion due to the generation of phosphate ions in cone photoreceptors, volume expansion of cone opsin, and volume expansion of the lipid membrane due to pigment dissociation."

[0156] In the first example, OCT data (B-scans) were repeatedly acquired at a constant acquisition rate throughout the entire examination period, which consisted of before (12 seconds), during (6 seconds), and after (90 seconds) optical stimulation. As a result, the data set analyzed by the calculation and control unit 50 had a constant acquisition time interval of 60 milliseconds between each OCT data frame. While this interval allows for good detection of slow responses as ORG, it does not allow for accurate detection of fast responses. In other words, an insufficient number of OCT data frames is acquired relative to the timescale of fast responses. On the other hand, if the acquisition time intervals between each OCT data frame were uniformly shortened throughout the entire examination period to ensure that a sufficient number of OCT data frames were acquired relative to the timescale of fast responses, the number of OCT data frames would be excessive for slow responses. This would result in an enormous amount of data per examination and would also lengthen the time required for analysis.

[0157] In contrast, in the third embodiment, the examination period is divided into several sections, including sections corresponding to fast responses and sections corresponding to slow responses, and OCT data is acquired at different acquisition rates for each section. This allows a data set to be acquired in which the interval between the acquisition times of each OCT data is adjusted according to the elapsed time during the examination period. Furthermore, in the third embodiment, a timestamp indicating the acquisition time is acquired each time OCT data is acquired. The timestamp is an example of acquisition time information. The calculation control unit 50 analyzes the OCT data included in the data set using the timestamp corresponding to each OCT data, thereby appropriately determining the ORG signal in each of the sections corresponding to fast responses and slow responses. A detailed description is provided below.

[0158] In the third embodiment, the test period is divided into two sections: section 1 and section 2. Section 1 corresponds to the period from the start of the test (before the light stimulus) to the end of the fast response. Section 1 continues from the start of the test until 30 milliseconds after the start of the light stimulus. Section 2 corresponds to the slow response. Section 2 lasts for 89.97 seconds, from 30 milliseconds after the start of the light stimulus to 90 seconds after the start.

[0159] First, the acquisition position (scan position) of OCT data in Example 3 will be described. As in Example 1, in Example 3, OCT data (B scan) on the same scan line 110 is repeatedly acquired during the examination period (see FIG. 5). That is, multiple OCT data (B scan) on the same scan line 110 are acquired while changing the scanning speed of the measurement light between the first and second sections.

[0160] The operating waveform of the scanning unit 15 at this time is shown in Fig. 19. However, in each scan line, the measurement light is scanned only in the x direction, the scanning unit 15 is a galvano scanner, and the swing angle of the galvano scanner is proportional to the magnitude of the drive signal.

[0161] As shown in FIG. 19 , a wave that increases and drops rapidly in proportion to time is repeatedly input as one cycle of a waveform. One cycle of the waveform corresponds to one B-scan. In the third embodiment, the drive signal in the first section has a shorter cycle than the drive signal input in the second section. That is, in the first section, OCT data is acquired at intervals of 1.5 milliseconds. In the second section, OCT data is acquired at intervals of 60 milliseconds. In FIG. 19 , the difference in cycles between the first and second sections is represented by the difference in waiting time until the next scan. The drive amount and drive speed of the galvanometer scanner during scanning in the second section are the same as in the first section, so imaging in the second section is less susceptible to effects such as fixational eye movement.

[0162] After a series of imaging, an analysis process is performed on the multiple OCT data included in the data set to obtain ORG data. In the third embodiment, the imaging time intervals for the OCT data vary for each section. Therefore, the calculation control unit 50 may perform the analysis process for each section. For example, the KT method described in the first embodiment may be performed for each section to obtain ORG data for each section. This makes it easier to set an appropriate KT-path for each section, thereby enabling more accurate ORG data to be obtained. In this case, a timestamp may be used to associate the optical path length value with the time axis in the ORG data. Furthermore, ORG data for the entire examination period may be obtained by connecting the ORG data obtained for each section.

[0163] FIG. 20 shows test results superimposed on a graph showing a time-varying model of the morphology of photoreceptor outer segments in response to light stimulation, as shown in FIG. 18. In this embodiment, OCT data is acquired at 1.5 millisecond intervals during the first period. Since the first period is immediately after stimulation, where a fast response with a short timescale occurs, capturing images at relatively short intervals facilitates appropriate detection of tissue changes as ORG data. Furthermore, fast responses not only have a short timescale, but also result in smaller tissue changes than slow responses. The short interval between captures of OCT data suppresses noise due to eye movement and instrument fluctuations, making it easier to accurately detect even small changes as ORG data. Furthermore, in this embodiment, OCT data is acquired at the same 1.5 millisecond intervals as when the fast response is progressing, even immediately before stimulation, making it easier to obtain ORG data with less noise for initial changes in the fast response.

[0164] In the second section, OCT data is acquired at 60-millisecond intervals. Because slow reactions occur on a long timescale, images are acquired at relatively long time intervals. This allows for proper detection of tissue changes during slow reactions as ORG data. Furthermore, because slow reactions have a long timescale, increasing the interval between OCT data acquisitions reduces the amount of data per test and the time required for analysis.

[0165] As described above, in the third embodiment, the intervals between the imaging times of the OCT data are different for each section, making it possible to simultaneously test multiple reactions with different time scales (here, fast and slow reactions). While OCT-ORG requires dark adaptation before testing, in the third embodiment, test preparations such as dark adaptation and alignment for multiple reactions with different time scales are completed only once, significantly reducing the time required for testing. As a result, the burden on the examiner and the subject can be reduced.

[0166] <Fourth Example> Next, the fourth embodiment will be described with reference to Figures 21 to 24, focusing on the differences from the first embodiment. In the following description, the description of the first embodiment will be used for matters such as device configuration that are not mentioned.

[0167] In the first embodiment, no particular mention is made of the density of A-scan points in the transverse direction on the fundus, whereas in the fourth embodiment, the density of A-scan points can vary depending on the position on the fundus in the transverse direction.

[0168] First, the acquisition position (scan position) of OCT data in the fourth embodiment will be described with reference to Fig. 21. In the fourth embodiment, the acquisition position of OCT data is set via a frontal fundus image. The acquisition position is indicated by the position of a scan line 210 superimposed on the frontal fundus image displayed on the display device. The position of the scan line 210 can be changed based on an operation via the user interface 90, and the acquisition position of the OCT data is also changed according to the position of the scan line 210.

[0169] In the fourth embodiment, when setting the acquisition position of OCT data, the calculation control unit 50 also sets the density of A-scan points according to the acquisition position. More specifically, the density of A-scan points at each position is set according to the positional relationship between the acquisition position of OCT data and the position of the feature region. For example, A-scan points are set at different densities between the inside and outside of the feature region on the fundus. When the acquisition position of OCT data and the position of the feature region partially overlap, there will be areas in one B-scan where A-scans are dense (high density) and areas where they are sparse (low density). As will be described later, A-scan points are set denser (high density) inside the feature region compared to outside.

[0170] A characteristic region may be a region where the tissue morphology differs from the surrounding area. Examples of characteristic regions include the optic disc, fovea, blood vessels, lesions, abnormal areas, and their vicinity. When setting the acquisition position of OCT data, it is desirable to acquire the position of the characteristic region in advance. As shown in FIG. 21 , a graphic 240 indicating the characteristic region may be superimposed on the frontal fundus image at the position of the characteristic region.

[0171] The characteristic region may be detected from an image. At least the position information of the characteristic region is obtained as a result of the detection. The type of image used to detect the position of the characteristic region and the content of the processing (image processing or analysis processing) used for the detection can be selected appropriately depending on the type of the characteristic region. The image used to detect the characteristic region may be a frontal fundus image acquired via the front imaging optical system 40a, or wide-area OCT data. The position of the characteristic region may also be input via the user interface 90. A fundus image including the characteristic region may be displayed upon input. The position of the characteristic region is input by specifying the position on the fundus image via the user interface 90.

[0172] In the fourth embodiment, the arithmetic and control unit 50 performs sampling of A-scans at regular time intervals. In order to set the density of A-scan points according to the acquisition position, the arithmetic and control unit 50 changes the scanning speed of the measurement light on the fundus according to the position on the fundus.

[0173] 22, 23A, and 23B, the control of the scanning unit 15 during inspection will be described in more detail. The control of the scanning unit 15 for the first scan line 210a that does not overlap the feature region 240 and the control of the second scan line 210b that partially overlaps the feature region 240 will be described while comparing them.

[0174] For convenience, the following explanation will be given based on the following assumptions a) to c), but the present invention is not necessarily limited to these. a) In each scan line, the measurement light is scanned only in the x direction. b) The scan length of each scan line is equal. c) The scanning unit 15 is a galvanometer scanner. In this case, the swing angle of the galvanometer scanner is proportional to the magnitude of the drive signal.

[0175] 23A and 23B show operating waveforms for the respective scan lines 230a and 230b. When the OCT data acquisition position is the first scan line 230a, a sawtooth wave as shown in FIG. 23A is input to the scanning unit 15 as a drive signal. Specifically, a wave that increases and drops sharply in proportion to time is input repeatedly at regular intervals as one cycle of the waveform. One cycle of the waveform corresponds to one B-scan. As a result of inputting such a drive signal to the scanning unit 15, the first scan line 210a is repeatedly scanned with the measurement light at a constant speed.

[0176] When the OCT data acquisition position is the second scan line 210b, the drive signal shown in FIG. 23B is input to the scanning unit 15. The input sawtooth waveform is different from the waveform shown in FIG. 23A for comparison. The waveform shown in FIG. 23B changes the slope midway through the section where the drive signal monotonically increases. This changes the scanning speed of the measurement light midway through scanning the second scan line 210b. In FIG. 23B, the section where the drive signal monotonically increases is shown as three sections V1, V2, and V3. Section V2, which has a slope relatively smaller than sections V1 and V3, corresponds to the outside of the feature region. Sections V1 and V3, which have a relatively smaller slope, correspond to the inside of the feature region. As a result, in the fourth embodiment, the measurement light scans at a slower speed inside the feature region than outside, and therefore the A-scan points are set densely (high density). The timing at which the slope of the drive signal changes is set in advance based on the positional relationship between the OCT data acquisition position and the feature region.

[0177] In the fourth embodiment, the time required for one B-scan when scanning the second scan line 210b is longer than when scanning the first scan line 210a. Therefore, by adjusting the time from the end of each B-scan to the start of the next B-scan, the interval between the acquisition times of the OCT data is kept constant regardless of the acquisition position of the OCT data.

[0178] Based on the control of the scanning unit 15, a data set consisting of a plurality of OCT data is acquired for each scan line.

[0179] Next, the analysis process in the fourth embodiment will be described. Here, the analysis process for the data set acquired in the second scan line 210b will be described. Note that the explanations in the other embodiments can be used for the analysis process for the data set acquired in the first scan line 210a.

[0180] In the analysis process of the fourth embodiment, one or more regions of interest are first set in the OCT data. A plurality of regions of interest may be set at different positions in the transverse direction. Here, as an example, two regions of interest 230a and 230b are set, one in the characteristic region and one in the region outside the characteristic region.

[0181] In the fourth embodiment, the positions of the regions of interest 230a, 230b are input via a user interface. A fundus image is displayed upon input, and the regions of interest 230a, 230b are set by specifying the positions of the regions of interest 230a, 230b on the fundus image via the user interface. For example, as shown in FIG. 24 , any OCT data (B-scan) included in the data set may be displayed as the fundus image. As described in the first embodiment, the regions of interest 230a, 230b are set by specifying the positions of windows indicating the regions of interest on the OCT data. In this case, the positions of the regions of interest 230a, 230b in the transverse direction and the depth direction are specified. However, this is not necessarily limited to this. Alternatively, a frontal image of the fundus may be displayed as the fundus image, and the positions of the regions of interest 230a, 230b may be specified on the frontal image to set the regions of interest 230a, 230b. In this case, the positions of the regions of interest 230a, 230b in the transverse direction are specified. Alternatively, both the B-scan and the frontal image may be displayed.

[0182] When setting the regions of interest 230a, 230b via a fundus image, it is desirable that the characteristic region 240 be emphasized in the fundus image. In FIG. 24 , the transverse range in the OCT data in which the characteristic region 240 exists is shaded, thereby emphasizing the characteristic region 240. However, the manner of emphasis is not necessarily limited to this and can be changed as appropriate. The emphasis of the characteristic region 240 on the fundus image makes it easy to set the region of interest 230b in the characteristic region 240, or conversely, to set the region of interest 230a in a position that avoids the characteristic region 240.

[0183] In the fourth embodiment, the same analysis process as in the first embodiment is performed on each of the two regions of interest 230a, 230b, and ORG data is acquired for each region of interest 230a, 230b. ORG data is acquired with high accuracy for the region of interest 230a set outside the feature region 240. Furthermore, in the region of interest 230b set inside the feature region 240, in the fourth embodiment, A-scan points are set more densely (high density) than outside the feature region 240, thereby suppressing a decrease in the accuracy of the ORG data.

[0184] <Modification> Although the embodiments of the technology disclosed herein have been described in detail above, these are merely examples and do not limit the scope of the claims. The technology described in the claims includes various modifications and alterations of the above-described embodiments.

[0185] <Other graph construction examples> For example, the graphs used in the route search in the first and second embodiments are merely examples, and the present invention is not necessarily limited to these.

[0186] For example, the graphs shown in Figures 25A to 25C can be used as graphs in the first embodiment. For example, Figure 25A shows the time evolution of reliability w(t, Δt) used as a graph as is. Also, Figure 25B is a graph showing the elements of KT-Path as Pm,n. Figure 25C is a weighted directed graph consisting of all KT-Paths. In Figure 25C, Pm,n is used as a node, and the cost shown in the following equation (8) is assigned to the edge.

[0187]

number

[0188] This method uses a bit array that corresponds to the data set. The bit array is expressed as the number of image data included in the data set and the corresponding number of bits. Here, image data at the reference time is expressed as "1" and all other data as "0". For example, below is an example of a bit array when a data set consists of image data for 8 frames. First, only frame number 0 indicates the bit array of the image data at the reference time.

[0189]

number

[0190]

number

[0191] An example of the evaluation function is shown in equation (9).

[0192]

number

[0193] Bit array optimization techniques include, for example, brute force and genetic algorithms (GA). In the brute force method, calculations are performed for all array patterns, and the array with the smallest evaluation value is selected as the optimal bit array. However, the amount of calculation required for the brute force method increases exponentially with the number of frames, making it unrealistic when there are a large number of frames.

[0194] A genetic algorithm is an algorithm that mimics the mechanism of biological evolution, and is a method for finding optimal patterns by repeatedly performing operations such as crossover (combining sequences with good evaluation values) and mutation (randomly changing sequences with a certain probability to avoid falling into a local optimum).By repeating operations using a genetic algorithm, it is possible to find an optimal or near-optimal bit sequence.

[0195] <About the application of tracking control> In the first to fourth embodiments described above, a so-called tracking process may be applied while capturing a plurality of OCT data during an examination period. By the tracking process, the scanning position of the measurement light follows the movement of the subject's eye while capturing a plurality of OCT data. In this case, when movement of the subject's eye equal to or greater than a threshold is detected, control for correcting the scanning position is intervened between each B-scan. For example, a frontal fundus image acquired via an SLO optical system may be used to detect the movement of the subject's eye.

[0196] However, in the third embodiment, during the first interval corresponding to a fast response, tracking processing intervenes, preventing OCT data capture during that interval, potentially resulting in insufficient OCT data being acquired during that interval. In response to this, tracking processing may be turned off during the first interval, or the threshold for the subject's eye movement that triggers tracking processing may be set to a larger value than the threshold for the second interval. This allows OCT data to be appropriately acquired during each interval, even when tracking processing is applied to the third embodiment. [Explanation of symbols]

[0197] 10 OCT units 70 Control Unit

Claims

1. An ophthalmic image processing program executed by a processor of an ophthalmic examination system for analyzing temporal changes in a subject's eye, Executed by a processor of the ophthalmic examination system, the ophthalmic examination system, A data set of a plurality of OCT data items arranged in chronological order as a result of repeatedly capturing images of a certain area of ​​a subject's eye using an OCT device during an examination period, a data set acquisition step of acquiring, as a result of the imaging, a data set in which the density of A-scan points in each of the OCT data varies depending on a position on the fundus in a transverse direction; a region of interest setting step of setting a region of interest at a corresponding position among the plurality of OCT data included in the data set; an analysis processing step of acquiring time-varying information indicating time-varying changes in the tissue of the subject's eye in the region of interest by an analysis processing that analyzes the data set.

2. 2. The ophthalmologic image processing program according to claim 1, In the region of interest setting step, the region of interest is set for each local region having a different transverse position on the fundus; In the analysis processing step, the data set is analyzed to obtain the time-dependent change information for each local region.

3. 2. The ophthalmologic image processing program according to claim 1, a second acquisition step of acquiring the position of a feature region on the fundus; A density adjustment step is further executed in which the density of A-scan points in the OCT data is adjusted according to the positional relationship between a characteristic region in the subject's eye and the imaging range in the OCT data.

4. An ophthalmic examination method implemented by a processor of an ophthalmic examination system for analyzing temporal changes in a subject's eye, comprising: The ophthalmic examination system an illumination optical system that illuminates the subject's eye with stimulating light; an OCT device that scans a fundus of an eye to be examined with measurement light and captures OCT data of the fundus based on a spectral interference signal between return light of the measurement light and a reference light, irradiating a fundus of the subject's eye with stimulating light via the irradiation optical system; Repeatedly capturing OCT data of a certain imaging range irradiated with the stimulus light via the OCT device; Acquiring, as a result of the imaging, a data set including a plurality of the OCT data items ordered in time series, in which the density of A-scan points in each of the OCT data items varies depending on a position on the fundus in a transverse direction; setting a region of interest at a corresponding position among the plurality of OCT data included in the data set; An ophthalmic examination method, comprising: acquiring time-dependent change information indicating time-dependent changes in tissue of the subject's eye in the region of interest by performing an analysis process that analyzes the data set.

Images