JP2017077414A - Ophthalmic analysis apparatus and ophthalmic analysis program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To enable blood vessel analysis included in subject eye data to be performed appropriately.SOLUTION: Provided is an ophthalmic analysis apparatus for analyzing subject eye data including blood vessel information for the subject eye, comprising analysis processing means for analyzing the subject eye data to acquire a measurement result relating to a capillary region. The analysis processing means applies an alleviating process for alleviating the effects of blood vessels with blood vessel diameters larger than those of capillaries on the measurement result to acquire the measurement result relating to the capillary region. For example, the vascular density of the capillary region may be determined.SELECTED DRAWING: Figure 6

Description

  The present disclosure relates to an ophthalmic analysis apparatus and an ophthalmic analysis program for analyzing eye data including blood vessel information of the eye to be examined.

    In recent years, a technique for obtaining motion contrast data of an eye to be examined using the OCT technique has attracted attention (see Non-Patent Document 1).

Roberto Reif et al. “Quantifying Optical Microangiography I mages Obtained from a Spectral Domain Optical Coherence Tomography System”, International Journal of Biomedical Imaging, Vol.2012, Article ID 509783, p.11

    However, although various improvements have been made to the imaging of motion contrast data, there is room for improvement in various aspects regarding analysis.

  For example, when blood vessel measurement is performed, there is a case where blood vessel measurement cannot be obtained accurately due to the influence of a large blood vessel. In addition, the blood vessel measurement is two-dimensional and is not considered in the depth direction. Further, it has not been easy to confirm the correlation between blood vessel information and morphological information (for example, retinal thickness). In addition, follow-up in blood vessel measurement has not been easy.

  In view of at least one of the problems of the prior art, it is an object of the present disclosure to provide an ophthalmic analysis apparatus and an ophthalmic analysis program that can suitably perform blood vessel analysis included in eye data to be examined.

  In order to solve the above problems, the present disclosure is characterized by having the following configuration.

(1)
An ophthalmologic analyzer for analyzing test eye data including blood vessel information of a test eye,
Analysis processing means for analyzing the eye data and obtaining a measurement result related to the capillary region;
The analysis processing means acquires a measurement result relating to the capillary region through a reduction process for reducing an influence on the measurement result by a large blood vessel having a larger blood vessel diameter than the capillary blood vessel.
(2)
An ophthalmologic analyzer for analyzing test eye data including blood vessel information of a test eye,
Analyzing the eye data, comprising an analysis processing means for obtaining a measurement result relating to a blood vessel region at a specific depth region,
The analysis processing unit corrects the measurement result based on a measurement range of the depth region.
(3)
An ophthalmologic analyzer for analyzing data obtained by ophthalmic OCT,
A display control means for simultaneously displaying a blood vessel analysis map based on OCT motion contrast data acquired by the ophthalmic OCT and a morphological analysis map based on OCT data acquired by the ophthalmic OCT on a monitor; To do.
(4)
An ophthalmologic analyzer for analyzing data obtained by ophthalmic OCT,
An analysis processing unit is provided that performs an integrated measurement process that is a measurement process for integrating the blood vessel measurement result based on the OCT motion contrast data acquired by the ophthalmic OCT and the morphological measurement result based on the OCT data acquired by the ophthalmic OCT. It is characterized by that.
(5)
An ophthalmologic analyzer for analyzing data obtained by ophthalmic OCT,
It is characterized by comprising display control means for acquiring time series data of blood vessel measurement results based on OCT motion contrast data from a storage unit and displaying the acquired blood vessel measurement results over time.
(6)
An ophthalmic analysis program for causing a computer to function as various processing means of the ophthalmic analysis apparatus according to any one of (1) to (5).

  Hereinafter, the ophthalmologic analyzer of this embodiment will be described with reference to the drawings. In the following, an OCT motion contrast data analysis device will be described as an example of an ophthalmologic analysis device. An OCT motion contrast data analysis apparatus (hereinafter referred to as an OCT analysis apparatus) 1 shown in FIG. 1 analyzes motion contrast data acquired by the OCT device 10. The OCT motion contrast data includes, for example, blood vessel information of the eye to be examined.

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

  As shown in FIG. 1, for example, a storage unit (for example, a non-volatile memory) 74, an operation unit 76, a display unit 75, and the like are electrically connected to the control unit 70. The storage unit 74 is, for example, a non-transitory storage medium that can retain stored contents even when power supply is interrupted. For example, a hard disk drive, a flash ROM, a removable USB memory, or the like can be used as the storage unit 74.

  Various operation instructions by the examiner are input to the operation unit 76. The operation unit 76 outputs a signal corresponding to the input operation instruction to the CPU 71. For the operation unit 76, for example, at least one of user interfaces such as a mouse, a joystick, a keyboard, and a touch panel may be used.

  The display unit 75 may be a display mounted on the main body of the apparatus 1 or a display connected to the main body. For example, a display of a personal computer (hereinafter referred to as “PC”) may be used. The display unit 75 displays, for example, OCT data acquired by the OCT device 10, motion contrast data, and the like.

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

<OCT device>
Hereinafter, an outline of the OCT device 10 will be described with reference to FIG. For example, the OCT device 10 irradiates the eye E with measurement light, and acquires an OCT signal acquired by the reflected light and the reference light. For example, the OCT device 10 mainly includes an OCT optical system 100.

<OCT optical system>
The OCT optical system 100 irradiates the eye E with measurement light and detects an interference signal between the reflected light and the reference light. The OCT optical system 100 mainly includes, for example, a measurement light source 102, a coupler (light splitter) 104, a measurement optical system 106, a reference optical system 110, a detector 120, and the like. For details of the configuration of the OCT optical system, refer to, for example, JP-A-2015-131107.

  The OCT optical system 100 is a so-called optical coherence tomography (OCT) optical system. The OCT optical system 100 divides the light emitted from the measurement light source 102 into measurement light (sample light) and reference light by the coupler 104. The divided measurement light is guided to the measurement optical system 106 and the reference light is guided to the reference optical system 110, respectively. The measurement light is guided to the fundus oculi Ef of the eye E through the measurement optical system 106. Thereafter, the detector 120 receives interference light obtained by combining the measurement light reflected by the eye E and the reference light.

  The measurement optical system 106 includes, for example, a scanning unit (for example, an optical scanner) 108. For example, the scanning unit 108 may be provided to scan the measurement light in the XY direction (transverse direction) on the fundus. For example, the CPU 71 controls the operation of the scanning unit 108 based on the set scanning position information, and acquires the OCT signal based on the light reception signal detected by the detector 120. The reference optical system 110 generates reference light that is combined with reflected light acquired by reflection of measurement light at the fundus oculi Ef. The reference optical system 110 may be a Michelson type or a Mach-Zehnder type.

  The detector 120 detects an interference state between the measurement light and the reference light. In the case of Fourier domain OCT, the spectral intensity of the interference light is detected by the detector 120, and a depth profile (A scan signal) in a predetermined range is obtained by Fourier transform on the spectral intensity data.

  As the OCT device 10, for example, Spectral-domain OCT (SD-OCT), Swept-source OCT (SS-OCT), Time-domain OCT (TD-OCT), or the like may be used.

<Front shooting optical system>
The front photographing optical system 200 photographs, for example, the fundus oculi Ef of the eye E from the front direction (for example, the optical axis direction of the measurement light), and obtains a front image of the fundus oculi Ef. The front imaging optical system 200 may have, for example, a scanning laser ophthalmoscope (SLO) device configuration (see, for example, JP-A-2015-66242) or a so-called fundus camera type configuration. (See JP2011-10944). As the front imaging optical system 200, the OCT optical system 100 may also be used, and a front image may be acquired based on a detection signal from the detector 120.

<Fixed target projection unit>
The fixation target projecting unit 300 includes an optical system for guiding the line-of-sight direction of the eye E. The projection unit 300 has a fixation target presented to the eye E and can guide the eye E. For example, the fixation target projecting unit 300 has a visible light source that emits visible light, and changes the presentation position of the fixation target two-dimensionally. Thereby, the line-of-sight direction is changed, and as a result, the acquisition site of the OCT data is changed.

<Acquisition of motion contrast data>
The OCT analysis apparatus 1 according to the present embodiment may acquire motion contrast data by processing OCT data detected by the OCT device 10, for example. The CPU 71 controls driving of the scanning unit 108 and scans the measurement light in the region A1 on the fundus oculi Ef. In FIG. 3A, the direction of the z axis is the direction of the optical axis of the measurement light. The x-axis direction is perpendicular to the z-axis and is the left-right direction of the subject. The y-axis direction is perpendicular to the z-axis and is the vertical direction of the subject.

  For example, the CPU 71 scans the measurement light in the x direction along the scanning lines SL1, SL2,..., SLn in the area A1. Note that scanning the measurement light in a direction intersecting the optical axis direction of the measurement light (for example, the x direction) is referred to as “B scan”. The two-dimensional OCT data obtained by one B-scan will be described as one-frame two-dimensional OCT data. For example, the CPU 71 may scan the measurement light two-dimensionally in the xy direction and obtain an A-scan signal in the z direction at each scanning position.

  The CPU 71 may acquire motion contrast data based on the OCT data. The motion contrast may be, for example, information that captures blood flow of the eye to be examined, changes in retinal tissue, and the like. When acquiring motion contrast data, the CPU 71 acquires at least two OCT data that are temporally different with respect to the same position of the eye to be examined. For example, in each scan line, the CPU 71 performs a plurality of B scans with different times, and acquires a plurality of OCT data with different times.

  For example, FIG. 3B shows an OCT signal acquired when a plurality of B scans with different times are performed on the scan lines SL1, SL2,..., SLn. For example, FIG. 3B shows that the scan line SL1 is scanned at times T11, T12,..., T1N, the scan line SL2 is scanned at times T21, T22,. A case where scanning is performed with Tn1, Tn2,..., TnN is shown. For example, the CPU 71 acquires a plurality of OCT data having different times in each scanning line, and stores the OCT data in the storage unit 74.

  As described above, when the CPU 71 acquires a plurality of temporally different OCT data with respect to the same position, the CPU 71 processes the OCT data to acquire motion contrast data. As a calculation method of OCT data for acquiring motion contrast, for example, a method of calculating an intensity difference or an amplitude difference of complex OCT data, a method of calculating an intensity or amplitude variance or standard deviation of complex OCT data (Speckle variance) ), A method of calculating a phase difference or variance of complex OCT data, a method of calculating a vector difference of complex OCT data, and a method of multiplying the phase difference and vector difference of a complex OCT signal. As one of the calculation methods, see, for example, JP-A-2015-131107.

  The CPU 71 may acquire the three-dimensional motion contrast data of the eye E by arranging the motion contrast data on different scanning lines. As described above, the motion contrast data is not limited to the phase difference, and an intensity difference, a vector difference, or the like may be acquired.

<Analysis processing of motion contrast data>
An example of analysis processing of motion contrast data acquired as described above will be described below.

  The CPU 71 may acquire at least one analysis result by setting an analysis region for motion contrast data and performing an analysis process on the set analysis region. In this case, the CPU 71 may set the analysis region in the motion contrast data on the basis of the position information of the analysis chart on the second image data that is image data different from the motion contrast data.

  Hereinafter, as an example of the analysis result, a case will be described in which a blood vessel region of the eye to be examined is extracted by analysis processing on motion contrast data. In this case, a blood vessel analysis region may be set as an analysis region for motion contrast data, and analysis processing for extracting a blood vessel region at least in the blood vessel analysis region may be performed.

  FIG. 4 is a diagram illustrating an example of an analysis screen when extracting a blood vessel region. For example, the CPU 71 may display a motion contrast display area (hereinafter referred to as an MC display area) 400 and a second image display area 500 on the display screen of the display unit 75 on the analysis screen. In this case, the MC display area 400 and the second image display area 500 may be displayed simultaneously, or may be displayed at different timings.

  The MC display area 400 is an area for displaying motion contrast data (hereinafter referred to as MC data) 402. For example, as MC data 402, as shown in FIG. 4, front MC data (also referred to as interface MC data). May be displayed. The front MC data may be acquired, for example, by taking out three-dimensional MC data for at least a partial region in the depth direction (see, for example, Japanese Patent Application No. 2015-121574). For example, the front MC data may be generated by the integrated value or the maximum value in the depth direction in the MC data. Of course, one-dimensional MC data, two-dimensional MC data, and three-dimensional MC data may be displayed as the MC data 402.

  The CPU 71 may display on the MC data 402 a display (for example, a graphic 404) indicating the blood vessel analysis region on the MC data 402. The display indicating the blood vessel analysis region may be a frame display indicating the outer edge of the blood vessel analysis region as in the graphic 404 of FIG. 4, or a display in which the blood vessel analysis region and the non-blood vessel analysis region are color-coded. Also good.

  The second image display area 500 may be an area for displaying second image data 502 that is image data different from the MC data 402. For example, at least one of a front image and an analysis map may be displayed in the second image display area. As the second image data 502, image data that is at least partially overlapped with the MC data 402 is used for the acquisition site. For example, when the MC data 402 is data centered on the macular region, the second image data 502 regarding the macula is displayed, and when the MC data 402 is data centering on the nipple region, the second image data 502 regarding the nipple region is displayed. May be displayed.

  The analysis map may be, for example, a map showing a two-dimensional distribution of measurement results related to the fundus. In this case, for example, a color map that is color-coded according to the measurement value may be used. The analysis map includes, for example, a thickness map indicating the layer thickness, a comparison map indicating a comparison result between the layer thickness of the subject eye and the layer thickness of the normal eye stored in the normal eye database, and the layer thickness of the subject eye and the normal eye database. May be a deviation map indicating the deviation from the normal eye layer thickness stored in the standard deviation, or an inspection date comparison thickness difference map indicating a difference in thickness from each inspection date. In addition, when calculating | requiring layer thickness, the OCT data is divided | segmented for every layer by the image process (for example, segmentation process) with respect to OCT data, for example, and the thickness of each layer is measured based on the space | interval of a layer boundary. Of course, the analysis map is not limited to the layer thickness, and the analysis map is not limited to the layer thickness, and may be a map showing the curvature distribution of the fundus, for example.

  The front image may be, for example, a front image captured by the front imaging optical system 200, or front OCT data (also referred to as interface OCT data) generated from OCT three-dimensional data. In the case of 3D OCT data, it may be 3D OCT data that is the basis of 3D motion contrast data. The analysis map may be, for example, a color map that two-dimensionally represents analysis results on the eye to be examined (for example, the thickness of the fundus layer, curvature, etc.). In FIG. 4, an image in which an analysis map is superimposed on the front image is displayed as the second image data 502.

<Analysis chart>
The CPU 71 may superimpose and display the analysis chart 504 on the second image data 502. In this case, the second image data 502 may be associated in advance with a measurement result (for example, measurement data of an analysis map) based on the analysis result with respect to the three-dimensional OCT data (registration), and the analysis chart 504 is set. The measurement result corresponding to the area is output. In this case, the second image data 502 may be associated with the three-dimensional OCT data, and an analysis process may be performed on the region where the analysis chart is set, or a measurement result may be output based on the analysis result. In this case, the three-dimensional OCT data is preferably three-dimensional OCT data that is the basis of the MC data. This is because the positional association (registration) between the MC data and the second image data is easy and accurate.

  For example, the analysis chart 504 may be an analysis chart for measuring a basic statistic of a measurement result in a preset section, or a basic statistic in a section may be measured. The section forming the analysis chart 504 may be one area or a plurality of sections. In the case of a plurality of sections, the basic statistic may be measured for each divided section. The basic statistic may be a representative value (average value, median value, mode value, maximum value, minimum value, etc.), spread degree (dispersion, standard deviation, coefficient of variation), or the like.

  For example, the analysis chart 504 may be a chart for obtaining an average for each region with respect to a two-dimensional distribution of the fundus layer thickness. Further, the analysis chart 504 may be provided with a numerical display area for displaying the layer thickness in the predetermined area with a numerical value. Instead of numerical display, color coding according to the measurement result in section units may be performed. The layer thickness data may be the total of each layer or the thickness of a certain layer (for example, the optic nerve fiber layer).

  The analysis chart 504 can be arbitrarily selected, and when the thickness map is a macular map, for example, as the analysis chart 504, the examiner can select GCHART (specifically, it is divided into three in the radial direction and vertically and horizontally (Divided chart), S / I chart, and ETDRS. When the thickness map is a nipple map, the analysis chart can be selected from, for example, an overall chart, an upper and lower chart (2 divisions), a TSNIT (Temporal Superior Nasal Inferior Temporal) chart (4 divisions), and a ClockHour chart (12 divisions). is there.

  The CPU 71 may receive an operation signal from the operation unit 76 and change the display position of the analysis chart 504 on the second image data 502. As a result, the analysis region based on the analysis chart 504 on the second image data 502 is changed. The CPU 71 may obtain the analysis result in the analysis region after the change in conjunction with the change in the display position of the analysis chart 504. A part of the analysis chart may protrude from the second image data 502.

  For example, the examiner moves the analysis chart 504 by using the operation unit 76, thereby changing the center 504 c of the analysis chart to the reference site of the fundus (for example, the center of the fovea (see M in FIG. 4), the center of the optic nerve head). , Abnormal sites). Thereby, for example, the measurement result by the analysis chart 504 is obtained centering on the reference part of the fundus. For example, the average layer thickness of the entire chart centered on the reference part, the layer thickness of the reference part, the average layer thickness (for example, 1, 2, 3 mm) in a predetermined area centered on the reference part are measured. Also good. As a result, a measurement result centered on the reference region on the fundus is obtained, which is clinically useful.

<Linking analysis chart and blood vessel analysis area>
For example, the CPU 71 may extract the blood vessel region on the MC data using the position information of the analysis chart 504. More specifically, the CPU 71 may move the position of the blood vessel analysis region on the MC data in conjunction with the movement of the analysis chart 504. In this case, the CPU 71 may move the display (for example, the graphic 404) indicating the blood vessel analysis region on the MC data 402. That is, the position of the blood vessel analysis region changes in conjunction with the movement of the analysis region based on the analysis chart 504.

  As a result of the movement of the blood vessel analysis region, the blood vessel analysis region on the MC data is changed. Therefore, the CPU 71 may obtain an analysis result in the changed blood vessel analysis region in conjunction with the change of the blood vessel analysis region (a blood vessel analysis method will be described later).

  When the analysis chart and the blood vessel analysis region are linked, the CPU 71 places the center 504c of the analysis chart 504 in the second image data 502 and the center 404c of the blood vessel analysis region in the MC data 402 at the same position in the analysis. The blood vessel analysis region may be moved so as to be arranged. That is, the CPU 71 may set the center of the analysis region at a position corresponding to the center position of the analysis chart 504 in the second image data 502 in the MC data 402. In this case, it is preferable that the second image data 502 and the MC data 402 are positionally associated (registered).

  Here, for example, when the examiner sets the center 504c of the analysis chart 504 as a reference site of the fundus (for example, the center of the fovea, the center of the optic disc, or the abnormal site), the center of the blood vessel analysis region is the reference of the fundus. It is automatically set to the site. Thereby, the reference position of the analysis can be matched between the analysis by the analysis chart 504 and the analysis for the MC data 402 without changing the analysis region on the MC data 402 again.

<Setting blood vessel analysis area>
The blood vessel analysis region may be arbitrarily set by the examiner. For example, the CPU 71 may receive an operation signal from the operation unit 76 and change the position of the blood vessel analysis region on the MC data. In this case, the display position indicating the blood vessel analysis region may be changed. The CPU 71 may obtain the analysis result in the changed blood vessel analysis region according to the change in the blood vessel analysis region. In this case, the CPU 71 may move the position of the analysis chart 504 on the second image data 502 in conjunction with the movement of the blood vessel analysis region. As a result, the labor for adjusting the position of the analysis chart 504 can be saved.

  When analyzing a plurality of motion contrast data, it may be used for setting the position of the blood vessel analysis region in the second motion contrast data in conjunction with the change of the position of the blood vessel analysis region in the first motion contrast data. Good. For example, the present invention can be applied to the analysis of a plurality of motion contrast data different in the depth direction of the fundus. Further, the CPU 71 uses the first data area set on the first motion contrast data, and uses the first data area corresponding to the first data area in the second motion contrast data. Artifacts in the region may be removed (for example, the luminance of each luminance in the first data region is subtracted from the luminance of each pixel in the second data region).

  The range (size) of the blood vessel analysis region may be set by the examiner. The range of the blood vessel analysis region may be set according to the acquisition region of the MC data 402 on the fundus. The acquisition area may be set according to different acquisition areas with respect to the surface direction of the fundus. For example, ranges may be set for the macular region and the nipple region, respectively.

  The setting according to the acquisition area may be set according to different acquisition areas with respect to the depth direction of the fundus. For example, ranges may be set for different vascular layers. Of course, the range is not limited to the blood vessel layer, and different ranges may be set for different retinal layers (or choroid layers). As a result, blood vessel analysis corresponding to the acquisition region on the fundus can be performed. When a plurality of front MC data is generated for each blood vessel layer, the range of the blood vessel analysis region may be set in advance by the front MC data of each blood vessel layer. Thereby, blood vessel analysis corresponding to each blood vessel layer becomes possible.

  FIG. 5 shows an example of a setting screen. The macular map shows motion contrast data centered on the macula, and the nipple map shows motion contrast data centered on the nipple, and the range (for example, diameter) in map units. Is set. When setting the range, for example, the CPU 71 may change the range (size) of the graphic 404 by receiving an operation signal from the operation unit 76. Further, the range of the blood vessel analysis region may be the same range as the analysis chart 504. Further, the range of the blood vessel analysis region may be set based on the range of the analysis chart 504.

  In the above description, the position and range (size) of the blood vessel analysis region can be set as the change parameter of the blood vessel analysis region. However, the present invention is not limited to this. For example, the shape of the blood vessel analysis region may be set (for example, a circle, an ellipse, a rectangle, etc.). In this case, the shape of the blood vessel analysis region may be set according to the acquisition region of the MC data 402 on the fundus.

  The blood vessel analysis region may be a region divided into a plurality of sections, and blood vessel analysis may be performed in each section. A plurality of blood vessel analysis regions having different arrangement positions or ranges (sizes) of the sections may be selectable.

  In this case, the division pattern of the blood vessel analysis region may be set according to the acquisition region of the MC data 402 on the fundus. For example, the Superficial Capillary Plexus may be an entire chart (one section), and the Intermediate Capillary Plexus may be an S / I chart (upper and lower divided sections). In this case, the arrangement position and range of each section of the blood vessel analysis region may be set to be the same as the arrangement position and range of each section of the analysis chart 504. Thereby, the analysis result of each section of the analysis chart 504 and the analysis result of each section in the blood vessel analysis region can be correlated and evaluated. Therefore, the relevance between the fundus blood vessel analysis result and the fundus morphological analysis result (for example, layer thickness) can be confirmed in section units.

<Vessel extraction processing, blood vessel measurement>
The CPU 71 may display the measurement result in the set blood vessel analysis region on the display unit 75 by analyzing the MC data 402 in the blood vessel analysis region set as described above. In this way, measurement on the analysis chart and measurement on the MC data 402 can be performed smoothly. The analysis result may be displayed as a numerical value 406 on the MC display area 400, for example.

  For example, the CPU 71 performs a discrimination process between a blood vessel region and a non-blood vessel region by performing analysis by image processing on a region set as a blood vessel analysis region on the MC data. A blood vessel region is extracted by the discrimination process. In this case, the non-blood vessel region may be extracted by the discrimination process.

  For example, threshold processing may be threshold processing, and pixels that satisfy the threshold may be determined as blood vessel regions, and pixels that do not satisfy the threshold may be determined as non-blood vessel regions. The threshold value itself may be arbitrarily set by the examiner, or may be determined in advance as a fixed value. Further, the threshold value may be set through an image analysis process on the MC data 402.

  For example, the CPU 71 may perform measurement related to the blood vessel region based on the result of the discrimination process. The CPU 71 may measure the blood vessel region based on the blood vessel region extracted by the discrimination process. The measurement result may be, for example, a blood vessel density or a blood vessel area. As the density of the blood vessel region, for example, the blood vessel area (blood vessel amount) per unit area is obtained by obtaining the ratio of the blood vessel region in the whole blood vessel analysis region. The measurement result is not limited to this, and may be, for example, the total amount of blood vessels, the degree of blood meandering, the regularity of blood vessels, and the like. When the blood vessel analysis region is divided into a plurality of sections, the CPU 71 may obtain the ratio and difference of the measurement results between the sections. Thereby, for example, the symmetry of the blood vessel can be obtained.

<Determination of capillaries>
Hereinafter, a process for acquiring a measurement result related to the capillary blood vessel region will be described. When performing measurement related to the capillary blood vessel region, the CPU 71 further performs a process of discriminating between the blood vessel region and the non-blood vessel region, and further performs analysis by image processing on the region discriminated as the blood vessel region. A determination process may be performed. A capillary blood vessel region is extracted by the discrimination process.

  The discrimination process may be, for example, a discrimination process based on a blood vessel diameter. A blood vessel having a blood vessel diameter below a threshold value may be determined as a capillary blood vessel region, and a blood vessel having a blood vessel diameter exceeding the threshold value may be determined as a large blood vessel region. This is based on the fact that the capillaries are thin and the large blood vessels are thick with respect to the blood vessel diameter. By using the blood vessel diameter, a capillary blood vessel with a thin blood vessel diameter and a large blood vessel with a large blood vessel diameter can be accurately distinguished. When obtaining the blood vessel diameter, the CPU 71 may measure each blood vessel diameter included in the blood vessel region by image processing. For example, the CPU 71 may thin the blood vessel region detected from the MC data and measure the original blood vessel diameter from the thinned line. The blood vessel diameter measuring method is not limited to this, and for example, a distance between blood vessel walls (for example, an intima distance) may be measured.

  The discrimination processing may be, for example, discrimination processing based on the number of blood vessel branches. A blood vessel in which the number of blood vessel branches exceeds a threshold is determined as a capillary blood vessel region, and a blood vessel in which the number of blood vessel branches is lower than the threshold is determined as a large blood vessel region. May be. This utilizes the fact that there are many capillaries and few large blood vessels with respect to the number of branches of the blood vessels. By using the number of branches, it is possible to accurately distinguish between a capillary vessel located at a relatively distal end and a large blood vessel located at a relatively proximal end. In this case, the number of branches of the blood vessel from the nipple may be the reference, or the number of branches from the large blood vessel having a higher size among the large blood vessels may be the reference. When obtaining the number of branches of the blood vessel, the CPU 71 may extract the branch point of the blood vessel by image processing and measure the number of branch points related to each blood vessel. Note that the discrimination processing may be performed by integrating the blood vessel diameter and the number of branches of the blood vessel. Thereby, the discrimination accuracy is improved.

  In the above description, the blood vessel diameter and the number of branches are described as an example of the discrimination process between the large blood vessel and the capillary blood vessel, but the present invention is not limited to this. For example, discrimination processing based on a difference in blood flow velocity of blood vessels may be used. More specifically, a blood vessel whose blood flow velocity is lower than the threshold may be determined as a capillary blood vessel region, and a blood vessel whose blood flow velocity exceeds the threshold may be determined as a large blood vessel region. This can take advantage of the slow blood capillaries and fast large blood vessels in terms of blood flow velocity. Note that the blood flow velocity may be detected using, for example, the difference in luminance between the capillary blood vessel and the large blood vessel in the MC data. In this case, the capillaries are imaged relatively bright and the large blood vessels are imaged relatively dark.

  Note that when a threshold value related to the blood vessel diameter, the number of branches, or the like is used in the determination process, the threshold value itself may be arbitrarily set by the examiner or may be determined in advance as a fixed value. In the case of a fixed value, the threshold value may be varied according to the characteristics of the subject (for example, either age or sex). That is, the criterion for distinguishing large blood vessels and capillaries may be set by the examiner. Note that the large blood vessel and capillary blood vessel discrimination processing may be performed through the examiner's operation, or the discrimination result may be acquired by the examiner specifying the capillary blood vessel region on the MC data. . Of course, by designating a large blood vessel, a capillary blood vessel region other than the large blood vessel may be specified as a result.

  FIG. 6 shows an example of the result of the discrimination process. As shown in FIG. 6, MC data (upper figure) related to the entire blood vessel is converted into MC data (middle figure) related to capillaries and MC data (lower figure) related to large blood vessels. It may be determined. In the above process, it is only necessary to extract a capillary blood vessel region, and it is not always necessary to extract a large blood vessel region.

<Measurement of capillaries with reduced influence of large blood vessels>
The CPU 71 may acquire the measurement result regarding the capillary blood vessel region by analyzing the MC data. For example, the CPU 71 may acquire the measurement result related to the capillary blood vessel region through a reduction process for reducing the influence on the measurement result by a large blood vessel having a larger blood vessel diameter than the capillary blood vessel.

  As the reduction process, for example, an analysis process specified for a blood vessel region smaller than a certain blood vessel diameter may be used. Further, the CPU 71 may perform processing for specifying a capillary blood vessel region from the MC data, and may acquire a measurement result related to the capillary blood vessel region based on the specified capillary blood vessel region. Further, the CPU 71 may perform processing by discriminating between the large blood vessel region and the capillary blood vessel region in the blood vessel region and performing measurement processing on the region identified as the capillary blood vessel region. That is, the determination result may be used. Alternatively, a region surrounded by the blood vessel region may be specified, and a measurement result related to the capillary blood vessel may be acquired based on the specified region. Further, the capillaries may be specified by the difference between the MC data and the fundus front image. As the fundus front image, a front image acquired by a fundus camera or SLO may be used.

  An example of the mitigation process is shown below. For example, the CPU 71 may perform measurement related to the capillary region based on the result of the discrimination process. In this case, the CPU 71 may measure the blood vessel region of the capillary blood vessel based on the capillary blood vessel region discriminated by the discrimination process. The measurement result may be, for example, the blood vessel density of capillaries or the blood vessel area of capillaries. As the blood vessel density of the capillaries, for example, the area (capillary amount) of the capillaries per unit area is obtained by obtaining the ratio of the capillaries in the whole blood vessel analysis region. The measurement result is not limited to this, and may be, for example, the total amount of capillaries, the degree of meandering of capillaries, the regularity of capillaries, and the like. When the blood vessel analysis region is divided into a plurality of sections, the CPU 71 may obtain the ratio and difference of the measurement results between the sections. Thereby, for example, the symmetry of the capillary can be obtained.

  According to the measurement process, for example, measurement specified for a capillary is performed, so that the influence of a large blood vessel is reduced, and the capillary can be measured more accurately. Therefore, for example, an eye disease related to capillaries can be more accurately evaluated. On the other hand, in the case of measurement including a large blood vessel, the large blood vessel occupies a certain specific gravity in the entire blood vessel region, and can be a factor that causes the measurement blood vessel related to the capillary blood vessel to be buried or changed.

  In the above description, the large blood vessel region and the capillary blood vessel region are discriminated, and the measurement result is obtained for the region discriminated as the capillary blood vessel region. However, the present invention is not limited to this, and the CPU 71 performs a large weight calculation or the like. A measurement process including a large blood vessel region and a capillary blood vessel region may be performed using an arithmetic processing constructed to reduce the specific gravity of the blood vessel region. In this case, the CPU 71 may perform measurement processing including the large blood vessel region and the capillary blood vessel region after performing image processing for thinning the region determined as the large blood vessel region to the same extent as the capillary blood vessel. A measurement result in which the influence of a large blood vessel is reduced is also obtained by the processing as described above. In the above description, an example is shown in which the measurement related to the capillary blood vessel region is performed. However, the present invention is not limited to this, and the measurement related to the large blood vessel region extracted through the discrimination process may be performed.

<Two-dimensional measurement of capillaries and acquisition of blood vessel analysis map>
The CPU 71 may obtain a measurement result related to the capillary blood vessel region two-dimensionally or three-dimensionally. The CPU 71 may output a color map that is color-coded according to the measurement result at each position related to the capillary blood vessel region.

  For example, the CPU 71 may perform measurement processing for each two-dimensional position of the capillary blood vessel region. In this case, for example, the measurement process may be performed for each pixel forming the capillary region or for each pixel group including a plurality of pixels. The measurement range may be, for example, within a set range set in a part of the MC data, or may be the entire MC data. The setting range set in a part of the MC data may be set centering on a reference part of the fundus (eg, fovea, optic disc center, abnormal part). The set range may be a predetermined range set in advance, or a range arbitrarily set by the examiner. Further, the shape of the setting range may be arbitrarily changed.

  For example, the CPU 71 may divide the capillary blood vessel region into a plurality of sections and perform measurement processing on each of the divided sections (see, for example, FIG. 7). In this case, for example, the CPU 71 may acquire a measurement result for each minute region. More specifically, the measurement result in each section may be obtained by dividing 256 × 256 two-dimensional MC data in units of 8 × 8 two-dimensional MC data. Further, the CPU 71 may acquire a measurement result for each relatively wide area (for example, two dimensions). More specifically, the measurement result in each section may be obtained by dividing 256 × 256 two-dimensional MC data in the vertical and horizontal directions (for example, vertical and horizontal division into three and vertical and horizontal division into four). In the capillary blood vessel measurement process, it is not always necessary to obtain a quantitative measurement value, and a technique (for example, grade classification) for obtaining the measurement result stepwise may be used.

  The CPU 71 may acquire a blood vessel analysis map based on the measurement result related to the capillary blood vessel region (see, for example, FIG. 7). The blood vessel analysis map may be displayed on the screen of the display unit 75.

  The blood vessel analysis map may be, for example, a map showing a two-dimensional distribution of measurement results related to capillaries. The blood vessel analysis map may be, for example, a color map that is color-coded according to the measurement value at each two-dimensional position. In this case, the CPU 71 may determine a display color corresponding to the measurement value at each two-dimensional position, and display each position with the determined display color. In addition, about the display color, the display color preset according to the magnitude of the measurement result may be sufficient, and the examiner may set arbitrarily according to a measurement result. More specifically, the blood vessel analysis map may be, for example, a color map that is color-coded according to the measurement result in each section. CPU71 may determine the display color according to the measurement result regarding each section, and may display each section with the determined display color.

  In the blood vessel analysis map, color-coded display may not be performed for large blood vessels. Moreover, the measurement result regarding the large blood vessel may be displayed in a color different from the color coding according to the measurement result of the capillary blood vessel. As a result, the examiner can accurately evaluate the capillaries because the capillaries and the large vessels are discriminated. The blood vessel analysis map may be displayed superimposed on the MC data. In this case, the color coding by the map is not superimposed on the large blood vessel of the MC data, or the color is distinguishable from the capillary blood vessel region. Superposition display may be performed.

  The type of blood vessel analysis map may be at least one of a basic map, a comparison map, a difference map, and an examination date difference map, for example. More specifically, the basic map may be a basic map (for example, a blood vessel density map) in which the magnitudes of the measurement values related to the capillaries of the eye to be examined are two-dimensionally expressed. The comparison map may be a comparison map showing a comparison result between the measurement value of the eye to be examined regarding the capillary blood vessels and the normal eye data regarding the capillary blood vessels stored in the normal eye database. The difference map may be a deviation map (difference map) indicating a deviation between the measured value of the eye to be examined regarding the capillary blood vessels and the normal eye data regarding the capillary blood vessels stored in the blood vessel information database. The examination date difference map may be an examination date difference map indicating a difference between different examination dates with respect to the measurement value of the eye to be examined regarding the capillary blood vessels.

  When obtaining a blood vessel analysis map, for example, MC data may be divided for each layer, and a blood vessel analysis map may be acquired for at least one layer. In this case, a plurality of blood vessel analysis maps having different layer regions may be acquired. In addition, a blood vessel analysis map in a plurality of layer regions may be acquired. The division processing for each layer may be performed by image processing (for example, segmentation) on MC data, or the result of image processing (for example, segmentation) on OCT data that is the basis of MC data is applied to MC data. May be.

  When displaying the blood vessel analysis map, for example, the CPU 71 displays the blood vessel analysis map and MC data (for example, two-dimensional MC data) serving as the basis of the blood vessel analysis map on the same screen of the display unit 75 at the same time. (See FIG. 8, for example).

  For example, the CPU 71 may superimpose the obtained blood vessel analysis map on the MC data of the eye to be examined. Thereby, the disappearance region of the blood vessel on the MC data can be easily confirmed. When superimposing the blood vessel analysis map, the CPU 71 may perform a display in which an area exceeding the normal range in the normal eye data is emphasized as an abnormal area. For example, the CPU 71 may superimpose the obtained blood vessel analysis map on the front image of the eye to be examined. The fundus front image may be an infrared front image, a color front image, or a front image based on OCT data.

  Note that the CPU 71 may change the blood vessel analysis map according to the measurement site. In this case, the output map may be set in advance according to the measurement site, or the examiner may arbitrarily set the map.

  For example, the number of sections in the blood vessel analysis map may be changed according to the measurement site. More specifically, for example, the MC data of the macular region may be set with a relatively large number of sections, and the MC data of the nipple region may be set with a relatively small number of sections. This is because a detailed measurement result may be obtained for the macular region, and an overall measurement result may be obtained for the nipple. Further, the depth region of the MC data serving as the basis of the blood vessel analysis map may be changed according to the measurement site. More specifically, for example, for the MC data of the macular region, a blood vessel analysis map regarding the region in front of the retina (for example, NFL to IPL) is set, and for the papillary region, the region of the entire retina (for example, NFL to RPE). ) Concerning the blood vessel analysis map may be set.

  Further, the CPU 71 may change the blood vessel analysis map according to the analysis disease. In this case, the output map may be set in advance according to the measurement site, or the examiner may arbitrarily set the map.

  For example, according to the analysis disease, at least one of the number of sections in the blood vessel analysis map, the arrangement position of the sections, and the depth region of MC data serving as the basis of the blood vessel analysis map may be changed. For example, in the case of diabetic retinopathy, ischemia occurs in a relatively shallow region (front side) on the retina.

  On the other hand, BRVO (Branch retinal vein occlusion), CRVO (Central retinal vein occlusion), BRAO (Branch retinal artery occlusion), CRAO (Central retinal) In the case of artery occlusion), ischemia occurs throughout the retina. Thus, the lesion can be evaluated appropriately by changing the depth region of the MC data according to the lesion.

  As for the number of sections in the blood vessel analysis map, for example, in the case of CRVO, the area around the nipple is caused, so even if the sections are uniformly set in the entire fundus in the front direction (direction perpendicular to the depth direction). Good. On the other hand, in the case of BRVO, since it is said that it develops on the upper ear side, the number of sections may be large for the upper ear side and the number of sections may be reduced for other regions. That is, according to the lesion, at least one of the number of sections and the arrangement position of the sections may be changed.

<Example of blood vessel density map>
Hereinafter, as an example of the blood vessel analysis map, a blood vessel density map showing a two-dimensional distribution of blood vessel density related to capillaries is shown (see FIGS. 7 and 9).

  The map in FIG. 7 is an example of a color map that is color-coded according to the measurement value (blood vessel density) in each section. For example, the CPU 71 may generate a color map corresponding to a preset measurement value for each section and display the generated color map on the display unit 75. By changing the display color according to the measurement value, it is possible to easily grasp the disappearance of the blood vessel.

  Furthermore, when displaying a color map in section units as described above, at least one of the shape and number of sections may be arbitrarily changed. Thereby, the density display according to the disease may be performed.

  The CPU 71 may compare the blood vessel density distribution data of the normal eye stored in the blood vessel information database with the blood vessel density distribution data of the eye to be examined with respect to the same range as the obtained blood vessel density map. By the display, the examiner can easily confirm the disappearance state of the blood vessels of the subject eye with respect to the normal eye. When displaying the comparison result, a difference map between normal eye data and test eye data is useful. Further, as a comparison result, the CPU 71 may simultaneously display a blood vessel density map based on normal eye data and a blood vessel density map based on test eye data on the display unit 75.

  Further, the CPU 71 may compare the past blood vessel density distribution data of the subject eye stored in the blood vessel information database with the current blood vessel density distribution data of the subject eye for the same range as the obtained blood vessel density map. By displaying the comparison result, the examiner can easily confirm the change over time related to the disappearance state of the blood vessel. When displaying the comparison result, a difference map between past eye data and current eye data is useful. Further, the CPU 71 may simultaneously display a blood vessel density map based on past eye data and a blood vessel density map based on current eye data on the display unit 75 as a comparison result.

  As a display method of the blood vessel analysis map, for example, as shown in FIG. 9, the CPU 71 specifies a region surrounded by blood vessels based on the blood vessel region in the MC data, and as a result, the measurement related to the capillary blood vessels is performed. A two-dimensional distribution of the result may be obtained.

  In this case, for example, the CPU 71 may determine connectivity of each blood vessel by image processing, and specify the region surrounded by the blood vessel depending on whether or not the periphery of the non-blood vessel region is surrounded by the blood vessel. In this case, the non-blood vessel region does not necessarily have to be surrounded by 360 degrees, and it may be determined that the region is surrounded by blood vessels as long as it is surrounded by a certain amount.

  More specifically, the CPU 71 may calculate the area of a region surrounded by blood vessels. The CPU 71 may perform color-coded display according to the calculated area in each region. As the color classification, at least two colors are used. For example, as shown in FIG. 9, a narrow area may be displayed in a first color (for example, red) and a wide area may be displayed in a second color (for example, green). As the capillaries disappear, the first color region decreases and the second color region increases, so the disappearance of the capillaries can be easily grasped by these color distributions. According to such a display method, the blood vessel itself is not counted as an area. As a result, the influence of the large blood vessel is reduced, and the disappearance state of the capillary blood vessel can be accurately grasped.

  In addition, when calculating | requiring the loss | disappearance state of a capillary vessel, you may color-code the part in which the distance from the nearest blood vessel is more than a threshold value among non-blood vessel regions.

  In the above description, the blood vessel density map has been described as an example. However, the present invention is not limited to this, and it is needless to say that the display form as described above can be applied to other blood vessel analysis maps.

<Vessel Information Database>
The storage unit 74 may store a blood vessel information database. The data stored in the blood vessel information database may be used, for example, for comparison with the obtained measurement result of the eye to be examined. The blood vessel measurement result (for example, blood vessel density, blood vessel area, capillary blood vessel) is a measurement result related to the blood vessel. At least one of the total amount, the degree of meandering of capillaries, the regularity of capillaries, etc.) is stored as a database.

  The blood vessel information database may be, for example, a normal eye database, and stores blood vessel measurement results of normal eyes. The normal eye database may be used, for example, for comparison with the measurement result of the actually measured eye.

  The normal eye database may be created by integrating the blood vessel measurement results of a large number of normal eyes. For example, statistical measurement values acquired from the blood vessel measurement results of a large number of normal eyes may be stored. In this case, for example, a normal eye database may be constructed by acquiring blood vessel measurement results for a large number of eyes and integrating blood vessel measurement results for normal eyes. The normal eye database as described above may be constructed for each race, sex, and eye characteristic (for example, axial length) and stored in the storage unit 74.

  The blood vessel information database may be, for example, a follow-up observation (follow-up) database, in which past blood vessel measurement results of each eye to be examined are stored. The follow-up observation database may be used, for example, for comparison with a newly acquired measurement result of the eye to be examined, or may be used for comparison of past measurement results acquired at different times. As the follow-up observation database, for example, a measurement result acquired in follow-up observation may be stored for each subject together with a measurement time (for example, date and time).

  In the blood vessel information database as described above, a database in which the influence of the large blood vessels is reduced is constructed by acquiring the measurement results in a state where the influence of the large blood vessels is reduced and creating a database. By utilizing such a blood vessel information database, it is possible to more accurately analyze the capillaries of the eye to be examined.

<Vessel measurement considering depth direction>
When performing blood vessel measurement, the CPU 71 may analyze the MC data and obtain a measurement result related to the blood vessel region in a specific depth region. Further, the CPU 71 may correct the measurement result based on the measurement range of the depth region. The blood vessel region at a specific depth region may be a predetermined layer (the entire retinal layer or the entire choroid), or data obtained by extracting a partial depth region in the three-dimensional data It may be.

  For example, the CPU 71 may perform measurement related to the blood vessel region in consideration of the measurement range in the depth direction. For example, the CPU 71 may correct the measurement result of the blood vessel region calculated based on the MC data based on the measurement range in the depth direction. For example, a two-dimensional measurement result regarding the blood vessel region calculated based on the front MC data may be corrected based on the measurement range in the depth direction.

  As the measurement range in the depth direction, for example, as shown in FIG. 10, the size (size) of the measurement range D1 in the depth direction corresponding to the front MC data used for blood vessel measurement may be used. In this case, the CPU 71 may correct the measurement result of the blood vessel region calculated based on the front MC data based on the size of the measurement range D1.

  The MC data relating to the specific depth region may be OCT front motion contrast data based on the three-dimensional motion contrast data in the specific depth region, and the CPU 71 specifies the MC data based on the measurement range of the depth region. The two-dimensional measurement result related to the blood vessel region in the depth region may be corrected.

  More specifically, when the front MC data is generated by selecting the 3D MC data related to the partial region in the depth direction from the entire 3D MC data, the CPU 71 sets the depth measurement range as the depth measurement range. You may make it obtain the magnitude | size in the depth direction.

  Here, when the measurement range D1 is larger than the size of the reference measurement range, the CPU 71 may calculate the measurement result lower according to the increment because the measurement range in the depth direction is incremented with respect to the reference. . On the other hand, when the measurement range D1 is smaller than the size of the reference measurement range, the CPU 71 reduces the measurement range in the depth direction with respect to the reference eye. Good.

  According to the above correction, the variation in the measurement result due to the difference in the size of the measurement range in the depth direction is corrected, and the blood vessel region can be measured more quantitatively. For example, in the evaluation of the disappearance state of the blood vessel, when the measurement value is obtained only from the front MC data, the information in the depth direction is not considered. Therefore, when the measurement value related to the blood vessel is constant, the same result is output even if the measurement range is different, so there is room for improvement in the evaluation of the blood vessel density and the like. According to the above configuration, measurement in consideration of the difference in the measurement range is possible, and the blood vessel density and the like can be obtained more accurately. Note that the measurement range in the depth direction may be acquired at each two-dimensional position (for example, section) from which the measurement result is acquired, and correction may be performed for each two-dimensional position.

  The MC data related to the specific depth region may be OCT front motion contrast data related to the specific layer region, and the CPU 71 determines the two-dimensional data related to the blood vessel region in the specific depth region based on the thickness data of the layer region. The dimensional measurement result may be corrected.

  More specifically, when the front MC data is acquired based on the three-dimensional MC data in a predetermined retinal layer (for example, optic nerve fiber layer: NFL), the CPU 71, for example, determines the predetermined retinal layer in the eye to be examined. The thickness data may be acquired, and the measurement result may be corrected based on the acquired thickness data.

  Here, when the retinal thickness of the eye to be examined is larger than the reference thickness data (for example, the measurement value of the predetermined retinal layer corresponding to the normal eye data in the blood vessel information database), the CPU 71 applies the reference eye data to the reference eye data. Since the volume of the predetermined retinal layer is incremented, the measurement result may be calculated to be low according to the thickness increment. On the other hand, when the retinal thickness of the eye to be examined is small with respect to the reference thickness data, the CPU 71 calculates the measurement result higher in accordance with the thickness increment because the volume of the predetermined retinal layer is reduced with respect to the reference eye data. May be. The thickness data of the predetermined retinal layer may be acquired based on the OCT data that is the basis of the MC data, or may be acquired based on the distance between the blood vessel networks in the three-dimensional MC data.

  According to the above correction, the variation in the measurement result due to the difference in the thickness of the retinal layer is corrected, and the blood vessel region can be measured more quantitatively. For example, in the evaluation of the disappearance state of the blood vessel, when the measurement value is obtained only from the front MC data, the information in the depth direction is not considered. Therefore, if the measurement value related to the blood vessel is constant, the same result is output even if the retinal thickness is different, so there is room for improvement in evaluation of the blood vessel density and the like. According to the said structure, the measurement which considered the difference in retinal thickness can be performed and the blood vessel density etc. can be calculated | required more correctly.

  For example, in the early stage of a certain eye disease or the like, when the tissue decrease is faster than the blood vessel density decrease, the correction is performed to increase the blood vessel density result. On the other hand, for example, in the early stage of a certain eye disease, when the decrease in blood vessel density is faster than the decrease in tissue, the correction is performed to reduce the blood vessel density result. In this case, the follow-up observation using the corrected measurement result may lead to early detection of an eye disease.

  In the above description, the NFL is taken as an example of the predetermined retinal layer. However, the present invention is not limited to this, and the CPU 71 applies the above embodiment in the fundus layer region (for example, another retinal layer or choroid layer). May be. The layer region may be a region composed of a single layer or a region composed of a plurality of layers.

  In the above description, the measurement result is corrected in consideration of the measurement range in the depth direction, but the present invention is not limited to this. For example, the CPU 71 may perform three-dimensional measurement based on three-dimensional MC data.

  More specifically, the CPU 71 may obtain the distribution of blood vessel measurement results (for example, blood vessel density) three-dimensionally. In this case, the CPU 71 processes the three-dimensional MC data to three-dimensionally extract a blood vessel region in a predetermined depth region (for example, a predetermined retinal layer), and three-dimensionally displays the measurement result of the extracted blood vessel region. May be required. The CPU 71 may divide the extracted three-dimensional blood vessel region into a plurality of blocks and perform measurement processing for each of the divided blocks. In this case, for example, the CPU 71 may acquire a measurement result for each minute region. More specifically, 256 × 256 × 256 3D MC data may be divided into 8 × 8 × 8 3D MC data units to obtain measurement results in each block (for example, FIG. 11). Further, the CPU 71 may acquire a measurement result for each relatively wide area. More specifically, the measurement result in each block may be obtained by dividing 256 × 256 × 256 three-dimensional MC data in the three-dimensional direction (for example, 3 to 10 divisions).

  The result obtained three-dimensionally may be, for example, the volume of the blood vessel region or the three-dimensional blood vessel density distribution of the blood vessel region. When obtaining the three-dimensional blood vessel density distribution, the CPU 71 processes the three-dimensional MC data to obtain the volume of the blood vessel region in the predetermined depth region, and calculates the volume of the three-dimensional MC data in the predetermined depth region. By dividing by the volume of the region, the three-dimensional density of the blood vessel region can be obtained.

  The CPU 71 may display the distribution of measurement results obtained three-dimensionally as a color map. For example, the CPU 71 may display a three-dimensional image that is color-coded according to the measurement result of each block obtained as described above. In addition, the CPU 71 may obtain a three-dimensional distribution of measurement results related to capillaries as a result of three-dimensionally measuring a region surrounded by blood vessels. In this case, for example, the CPU 71 may determine connectivity of each blood vessel by image processing, and may extract a region surrounded by blood vessels depending on whether or not the periphery of the non-blood vessel region is surrounded by blood vessels. In this case, the non-blood vessel region does not necessarily have to be surrounded by 360 degrees in the three-dimensional direction, and may be determined as a region surrounded by blood vessels as long as it is surrounded by a certain amount. More specifically, the CPU 71 may calculate the volume of the region surrounded by the blood vessels. The CPU 71 may perform color-coded display according to the calculated volume in each region.

<Display of OCT blood vessel analysis map and OCT morphology analysis map>
The CPU 71 may simultaneously display a blood vessel analysis map based on MC data acquired by OCT and a morphological analysis map based on ophthalmic OCT data on a monitor.

  For example, when displaying the blood vessel analysis map related to the blood vessel measurement result of the eye to be examined, the CPU 71 may display the shape analysis map related to the shape measurement result of the eye to be examined (for example, a map related to the retinal thickness) simultaneously (for example, (See FIG. 12). For example, the CPU 71 may display the blood vessel analysis map and the morphological analysis map in parallel.

  For example, the CPU 71 may simultaneously display the blood vessel analysis map and the morphological analysis map for the common layer region in the fundus. In this case, the correlation between the blood vessel information of the eye to be examined and the morphological information can be easily obtained with respect to the common area in the depth direction.

  For example, in the morphological analysis map related to the thickness of a predetermined retinal layer, if the thickness of the retinal layer is thin, by checking the blood vessel analysis map at the same time, whether the decrease in thickness is due to a decrease in blood vessels or other factors Can easily grasp whether or not. In this case, the CPU 71 may acquire a blood vessel analysis map and a morphological analysis map having different measurement times, and display the vascular analysis map and the morphological analysis map simultaneously in time series.

  In addition, as a kind of blood vessel analysis map, for example, as described above, a basic map, a comparison map, a difference map, and an examination date difference map can be considered. Moreover, as a form analysis map, the map which shows the two-dimensional distribution of the form measurement result regarding a fundus, for example may be used. In this case, for example, a color map that is color-coded according to the measurement value may be used. The analysis map includes, for example, a thickness map (basic map) indicating the layer thickness, a comparison map indicating a comparison result between the layer thickness of the eye to be examined and the layer thickness of the normal eye stored in the normal eye database, and the layer thickness of the eye to be examined. A difference map (deviation map) indicating the deviation between the normal eye layer thickness and the normal eye layer thickness stored in the normal eye database, and an inspection date comparison thickness difference map indicating a difference in thickness from each inspection date Good.

  When obtaining the layer thickness, for example, the OCT data may be divided for each layer by image processing (for example, segmentation processing) on the OCT data, and the thickness of each layer may be measured based on the interval between the layers. Of course, the form measurement result is not limited to the layer thickness. Further, the analysis map is not limited to the layer thickness map, and may be a map showing a fundus curvature distribution, for example.

  Here, when the blood vessel analysis map is simultaneously displayed on the display unit 75 together with the morphological analysis map, the comparison between the morphological information and the vascular information is more accurately performed according to the purpose by simultaneously displaying the maps having the same characteristics. Can do. For example, the basic map of the blood vessel analysis map and the basic map of the morphological analysis map may be displayed at the same time. Similarly, each comparison map may be displayed simultaneously, each difference map may be displayed simultaneously, and each examination date difference map may be displayed simultaneously.

  The morphological analysis map may be a display format in which a color map (for example, a map related to layer thickness) is superimposed on the fundus front image. The fundus front image may be acquired by a fundus camera or SLO, or may be an OCT front image acquired based on OCT data. Further, a display format in which a color map (for example, a map related to a layer thickness) is superimposed on the front MC image may be used.

  When the blood vessel analysis map and the morphological analysis map are displayed at the same time, each map may have a format in which a color map is superimposed on the front MC data. With such display, the correlation between the blood vessel analysis result and the morphological analysis blood vessel can be easily obtained for the MC data image. In this case, the MC data, the blood vessel analysis map, and the morphological analysis map may be displayed simultaneously. Each map may have a format in which a color map is superimposed on the fundus front image. With such display, the correlation between the blood vessel analysis result and the morphological analysis blood vessel can be easily obtained for the fundus front image. The fundus front image may be an infrared front image, a color front image, or a front image based on OCT data.

<Integration of blood vessel measurement results and morphological measurement results>
The CPU 71 may perform a measurement process in which the blood vessel measurement result of the eye to be examined and the shape measurement result of the eye to be examined are integrated. When each measurement result is acquired as a two-dimensional distribution, for example, the CPU 71 may display the result of the integrated measurement process as a single color map or an analysis chart (for example, see FIG. 13). The CPU 71 may obtain an integrated value obtained by integrating the blood vessel measurement result of the subject eye and the form measurement result of the subject eye.

  When performing integrated measurement, the CPU 71 may obtain a representative value (for example, an average value or a combined value) of each measurement value of the blood vessel measurement result and the morphological measurement result, or give a certain weight to each measurement value. It may be a weighting operation to be performed. In this case, when the units of the measurement values do not match (for example, density and thickness), a predetermined integrated parameter may be set, and an arbitrary coefficient may be set between the blood vessel measurement result and the morphological measurement result.

  Similarly to the above, the CPU 71 may simultaneously display the result of visual field measurement of the eye to be examined and the result of blood vessel measurement of the eye to be examined on the same screen. Further, the CPU 71 may perform a measurement process in which the visual field measurement result of the eye to be examined and the blood vessel measurement result of the eye to be examined are integrated, and the integrated measurement result may be displayed.

  Of course, the CPU 71 may simultaneously display the result of the morphological measurement of the subject eye, the result of the visual field measurement of the subject eye, and the result of the blood vessel measurement of the subject eye on the same screen. Further, the CPU 71 may perform measurement processing that integrates the result of morphological measurement of the eye to be examined, the result of visual field measurement of the eye to be examined, and the result of blood vessel measurement of the eye to be examined, and may display the integrated measurement result. .

<Vessel analysis chart>
In addition, according to the said description, although the measurement result was displayed as a blood vessel analysis map, it is not limited to this, You may output as a blood vessel analysis chart.

  For example, the blood vessel analysis chart may be a blood vessel analysis chart for measuring a basic statistic of a blood vessel measurement result in a preset section, or a basic statistic in a section may be measured. The section forming the blood vessel analysis chart may be one region or a plurality of sections. In the case of a plurality of sections, the basic statistic may be measured for each divided section. The basic statistic may be a representative value (average value, median value, mode value, maximum value, minimum value, etc.), spread degree (dispersion, standard deviation, coefficient of variation), or the like.

  For example, the blood vessel analysis chart may be a chart for obtaining an average for each region for a two-dimensional distribution of blood vessel measurement results. The blood vessel analysis chart may be attached with a numerical display area for displaying blood vessel measurement results in a predetermined area with numerical values.

<Course observation>
Note that the CPU 71 may acquire time-series data of blood vessel measurement results based on MC data from the storage unit 74 and display the acquired blood vessel measurement results over time (see, for example, FIG. 14). For example, the CPU 71 may display a graph indicating a change over time in the blood vessel measurement result, or may arrange a plurality of blood vessel analysis maps having different acquisition times in time series. A plurality of blood vessel analysis maps having different acquisition times may be displayed as time-lapse images. The CPU 71 may obtain a difference between the first acquisition time and the second acquisition time regarding the blood vessel measurement result. The CPU 71 may display a difference map of the two-dimensional distribution by obtaining a difference between the first acquisition time and the second acquisition time in the two-dimensional distribution of the blood vessel measurement result.

  In the above description, measurement was performed while reducing the influence of large blood vessels. However, <blood vessel analysis map>, <blood vessel density map>, <blood vessel information database>, and <blood vessel considering depth direction> Measurement>, <Display of blood vessel analysis map and thickness analysis map>, <Integration of blood vessel measurement result and morphological measurement result>, <Vessel analysis chart>, <Course observation>, etc. Items described below, or The technical contents shown in other parts can be applied to a technique that can be applied to measurement including large blood vessels. Of course, blood vessel measurement specified for a large blood vessel may be performed. Similarly, other items may be implemented in parallel or independently.

  In the above description, MC data acquired by OCT has been described as an example, but acquired by a fundus imaging device (for example, a fundus camera, a scanning laser ophthalmoscope (SLO)) that captures a frontal fundus image of the eye to be examined. The above-described embodiment can also be applied to the measurement of fundus blood vessels included in the fundus front image. In this case, the fundus front image may be at least one of front image data (for example, a color fundus image) by reflected light from the eye to be examined and front image data by fluorescence from the eye to be examined. In addition, analysis combining OCT motion contrast data and these may be performed.

  In the case of automatically performing the discrimination process between the blood vessel region and the non-blood vessel region, the CPU 71 applies a binarization process (for example, a discriminant analysis method) from the luminance value of the MC data 402 to thereby obtain a blood vessel / non-blood vessel. An area may be defined. When the blood vessel analysis region is divided into a plurality of sections, the threshold value may be set for each region corresponding to each section, or the threshold value may be set for the entire blood vessel analysis region.

<Development of blood vessel information to MC data>
The CPU 71 may acquire blood vessel information regarding the blood vessel region included in the MC data. Further, the CPU 71 may give the acquired blood vessel information to the blood vessel region included in the MC data. In this case, blood vessel information may be acquired for at least one blood vessel included in the blood vessel region, blood vessel information may be acquired for one blood vessel included in the MC data, and blood vessel information may be added. Blood vessel information may be acquired for each of a plurality of blood vessels included in the blood vessel, and the blood vessel information may be assigned thereto. The blood vessel information may include blood vessel information for each blood vessel, and the position information and blood vessel information of each blood vessel may be acquired as a set.

  Regarding blood vessel information, for example, the CPU 71 may acquire arteriovenous information related to a blood vessel region included in the MC data as blood vessel information. Further, the CPU 71 may give the arteriovenous information to the blood vessel region included in the MC data (see, for example, FIG. 15). That is, the blood vessel information may be information related to the blood vessel function.

  Further, for example, the CPU 71 may acquire bleeding information regarding the blood vessel region included in the MC data as blood vessel information. Further, the CPU 71 may give bleeding information to the blood vessel region included in the MC data.

  When obtaining blood vessel information, the CPU 71 may acquire blood vessel information from data different from the MC data. As a result, for example, blood vessel information that is difficult to detect with only the MC data is added to the MC data, and the blood vessel evaluation by the examiner is performed better. In the case of different data, at least a part of the MC data may be data having a common acquisition area. Further, in different data, it may be distribution data related to blood vessel information, or may be registered with respect to MC data.

  For example, the CPU 71 may acquire blood vessel information from OCT data or Doppler OCT data acquired by OCT. The OCT data or Doppler OCT data may be data acquired by an OCT device that acquires MC data, or may be data acquired by an OCT device that is different from the OCT device that acquires MC data. The CPU 71 may acquire blood vessel information from the MC data itself. For example, as an OCT device that acquires MC data, for example, Doppler-OCT may be used.

  Further, the CPU 71 may acquire image data captured by a modality (imaging unit) different from OCT. Different modalities may be used as long as blood vessel information can be acquired. For example, a fundus camera, SLO, or LSFG (Laser Speckle Flowography) may be used.

  The blood vessel information is not limited to arteriovenous information and bleeding information, but includes blood flow velocity information, layer position information, color information, polarization characteristic information, travel direction information, hardness information, blood vessel inner and outer wall ratio information, and the like. May be acquired. That is, the blood vessel information may be, for example, information related to the characteristics of the blood vessel region included in the MC data. The blood flow velocity information may be acquired by Doppler OCT, for example. The layer position information may be acquired by, for example, OCT data or MC data. The color information may be acquired by, for example, a fundus camera or spectral OCT. For example, the polarization characteristic information may be acquired by PS-OCT.

  When outputting the MC data, for example, the CPU 71 may perform image processing on the MC data based on the acquired blood vessel information and display the MC data including the blood vessel information on the display unit 75. Note that the display is not limited to display on the display unit 75, and MC data including blood vessel information may be printed or output to an external server. The MC data may be, for example, front MC data, 3D MC data, or 2D MC data. In this case, MC data including blood vessel information may be displayed on the display unit 75 based on the blood vessel information previously assigned to the MC data. Moreover, you may provide the display regarding the acquired blood vessel information to MC data.

  When analyzing the MC data and acquiring the measurement result related to the blood vessel region, for example, the CPU 71 may acquire the measurement result related to the blood vessel region using the acquired blood vessel information. In this case, the CPU 71 may obtain the measurement result two-dimensionally or three-dimensionally. A measurement result may be displayed as an analysis map, for example, and may be displayed as an analysis chart.

  CPU71 may acquire a measurement result using the blood vessel information previously provided with respect to MC. After acquiring the measurement result regarding the blood vessel region in advance, blood vessel information may be added to the acquired measurement result.

  When the arteriovenous information is acquired as the blood vessel information, the CPU 71 may acquire at least one of a measurement result related to the arterial region and a measurement result related to the vein using the arteriovenous information. For example, when bleeding information is acquired as blood vessel information, the CPU 71 may acquire a measurement result regarding the bleeding blood vessel region using the bleeding information. That is, the CPU 71 may perform measurement processing for each artery and vein using the arteriovenous information of each blood vessel.

  The blood vessel information related to the blood vessel region included in the MC data may be stored in the storage unit 74 together with the MC data. In this case, for example, position information in each blood vessel in the MC data and blood vessel information may be stored in association with each other. In this case, a specific method is not limited as long as blood vessel information of at least one blood vessel included in the MC data can be referred to in at least one of the later analysis / measurement processing and display processing.

  As the addition of blood vessel information, for example, blood vessel information including position information of each blood vessel may be registered in the MC data so that each blood vessel in the MC data is associated with the blood vessel information. In addition, a table indicating the correspondence between each blood vessel and blood vessel information in the MC data may be set. Further, blood vessel information may be assigned to the MC data of each blood vessel, and the CPU 71 may refer to blood vessel information corresponding to the MC data of each blood vessel.

<Acquisition and provision of arteriovenous information>
Below, an example in the case of acquiring and giving arteriovenous information is shown. For example, the CPU 71 may acquire determination information for determining whether at least one blood vessel included in the MC data is an artery or a vein as the arteriovenous information. The arteriovenous information may be arteriovenous information in which it is specified whether each blood vessel is an artery or a vein. The arteriovenous information may be information relating to either an artery or a vein, and may be, for example, information on only an artery or information on only a vein. The arteriovenous information may be blood vessel distribution information regarding at least one of an artery and a vein.

  For example, the CPU 71 may perform image processing on the MC data based on the arteriovenous information and display the MC data on which the arteriovenous information is reflected on the display unit 75. In this case, the CPU 71 may display each blood vessel region in the MC data in a different color (for example, red for an artery and blue for a vein) depending on whether it is an artery or a vein. In this case, the color may be superimposed on the blood vessel region of the MC data imaged in black and white expression, or the MC data itself may be colored. Further, for example, the CPU 71 may display MC data in which only the artery or only the vein is imaged using the arteriovenous information. In this case, the CPU 71 may image one of the artery region and the vein region by extracting one of the artery region and the vein region using the arteriovenous information. Further, the CPU 71 may switch-display or display in parallel the MC data related to the arterial region and the MC data related to the vein region. In this case, the artery and vein may be displayed in different colors. The arteriovenous information may be displayed when a specific blood vessel is designated.

  If it does as mentioned above, it will become possible to evaluate the state of the blood vessel of the eye to be examined including the functional aspect by specifying whether the blood vessel included in the MC data is an artery or a vein. Think useful.

  Further, by adding the arteriovenous information to the MC data, it is possible to display, measure, etc. the MC data reflecting this. For example, when the CPU 71 analyzes the MC data and acquires the measurement result related to the blood vessel region, the CPU 71 may acquire at least one of the measurement result related to the arterial region and the measurement result related to the vein using the arteriovenous information. . As the measurement result, for an artery or vein, for example, at least one of blood vessel density, blood vessel area, total amount of blood vessels, degree of meandering of blood vessels, regularity of capillaries, and blood vessel diameter may be calculated.

  CPU71 may acquire the integrated measurement result which integrated the measurement result regarding the artery area | region of MC data, and the measurement result regarding a vein. For example, the CPU 71 may acquire the ratio or difference between the measurement result related to the arterial region and the measurement result related to the vein. More specifically, the CPU 71 may obtain an arteriovenous ratio (A / V ratio) that is a ratio of the blood vessel diameter in the artery region to the blood vessel diameter in the vein region.

  The CPU 71 may obtain the measurement result two-dimensionally or three-dimensionally regarding at least one of the measurement result related to the arterial region and the measurement result related to the vein. Further, the CPU 71 may display the obtained measurement result on the display unit 75 as a blood vessel analysis map. Further, the CPU 71 may switch-display or display the blood vessel analysis map related to the arterial region and the blood vessel analysis map related to the vein region. Of course, the measurement result obtained is not limited to the blood vessel analysis map, and may be displayed as a blood vessel analysis chart.

  The CPU 71 may assign the arteriovenous information to a part or the whole of the blood vessel region included in the MC data, and may further assign the arteriovenous information to a part of at least one blood vessel. Arteriovenous information may be given to one whole blood vessel. Of course, arteriovenous information may be given to each of a plurality of blood vessel regions. Further, the CPU 71 may classify each blood vessel region with respect to the blood vessel diameter and give the arteriovenous information with respect to some blood vessel diameters. For example, the arteriovenous information may be given to either the capillary blood vessel region or the large blood vessel region.

  The arteriovenous discrimination method is a method of discriminating the arteriovenous using OCT, a method of discriminating the arteriovenous by applying the principle of the pulse oximeter, or discriminating the arterial vein from color information of the fundus color image or SLO image. Possible ways to do this. Specific examples are shown below.

<Arteriovenous discrimination based on OCT data>
When acquiring the arteriovenous information, for example, the CPU 71 may acquire the arteriovenous information based on the fundus OCT data or Doppler OCT data acquired by OCT. The OCT data may be, for example, OCT data (for example, two-dimensional OCT data) acquired for an area common to MC data, or OCT data that is the basis of MC data. In this case, OCT data is distinguished from MC data in that it is fundus reflectance data. The OCT data may be, for example, tomographic image data including fundus morphology information.

  When acquiring arteriovenous information from the OCT data, for example, it may be determined whether the blood vessel of the eye to be examined is an artery or a vein based on the luminance value of the OCT data. More specifically, as shown in FIG. 16, in the OCT data, the artery is displayed brighter and the vein is displayed darker than the artery.

  When detecting a blood vessel region in the OCT data, for example, a luminance value in a region on the NFL side of the RPE with respect to each A scan data may be obtained, and a region having a relatively low luminance value may be set as a blood vessel region candidate. The CPU 71 may detect a region having a predetermined width or more in the scanning direction as a blood vessel region among the blood vessel candidate regions. When detecting a blood vessel region in the OCT data, the CPU 71 may detect a blood vessel region in the OCT data using the blood vessel information included in the MC data. In this case, when the OCT data that is the basis of the MC data is used as the OCT data, registration between the data is easy, and the blood vessel region can be easily specified.

  Next, the CPU 71 may determine an artery and a vein based on the luminance value of the blood vessel region on the OCT data. For example, the CPU 71 may determine the artery / vein using the luminance average of the blood vessel region in each region of the fundus region TSNIT as shown in FIG. The CPU 71 may calculate a luminance average from the ILM to the IPL / INL of the blood vessel region in each region of the TSNIT, and determine a threshold value that maximizes the interclass variance by the discriminant analysis method. The CPU 71 may use blood vessels with higher luminance than this threshold as arteries and blood vessels with lower luminance as veins.

  Note that the technique for discriminating between arteries and veins in the OCT data is not limited to this, and the difference in luminance of the blood vessel wall between the arteries and veins may be used. Moreover, you may utilize the difference in the brightness | luminance of RPE below the blood vessel between an artery and a vein. That is, there is no particular limitation as long as it is a technique for discriminating between an artery and a vein by using a difference (for example, luminance and shape) between an artery and a vein imaged on OCT data. That is, the present inventors have found a discrimination method using the fact that the drawing state differs between the artery and the vein in the blood vessel included in the OCT data.

  When the arteriovenous information based on the OCT data is acquired as described above, the CPU 71 may add the arteriovenous information to the corresponding blood vessel region in the MC data related to the region common to the OCT data. In the case of three-dimensional MC data, the CPU 71 acquires arteriovenous information based on each two-dimensional OCT data forming the three-dimensional OCT data, and uses the acquired arteriovenous information as each two-dimensional MC data of the three-dimensional MC data. It may be applied to the corresponding blood vessel. Of course, the CPU 71 may acquire three-dimensional distribution information related to an artery or vein in the dimensional OCT data, and give the arteriovenous information based on the acquired three-dimensional distribution information to the blood vessel region of the three-dimensional MC data. In this case, the CPU 71 may acquire the arteriovenous information using the front OCT data, and give the acquired arteriovenous information to the blood vessel region of the front OCT data.

  In the above description, the arteriovenous information is acquired based on the OCT data. However, the present invention is not limited to this, and the arteriovenous information may be acquired using the sign of the phase change amount in the Doppler OCT data. For example, the CPU 71 may obtain the advancing direction of the blood flow based on the phase change amount, or may acquire the arteriovenous information based on the advancing direction of the blood flow. In this case, for example, if the destination of the blood flow is the nipple, the vein may be determined, and if the destination is the opposite, the artery may be determined.

<Applying the principle of pulse oximeter>
The pulse oximeter is a hemoglobin that is not bound to hemoglobin that is bound to oxygen, and measures oxygen saturation in blood by utilizing the property that the absorbance of red light and infrared light changes. Applying this, the OCT light source performs the same measurement as a pulse oximeter. The higher oxygen saturation is determined as an artery, and the lower oxygen saturation is determined as a vein.

  That is, the CPU 71 may acquire the arteriovenous information based on the oxygen saturation data of the eye to be examined. In this case, the arteriovenous information may be given by registering the obtained arteriovenous information of each blood vessel and the MC data.

<Acquisition of arteriovenous information from other devices>
The CPU 71 may acquire arteriovenous information based on image data acquired by imaging means (modality) different from OCT. As another modality, for example, image data acquired by a fundus camera, SLO, or LSFG (Laser Speckle Flowography) may be used. For example, the arteriovenous information may be acquired based on a difference in color tone between an artery and a vein in a fundus image acquired by a fundus camera or SLO. In addition, the arteriovenous may be determined using the property that the artery is lighter in blood vessel color than the vein.

  In addition, the arteriovenous information may be acquired based on the difference in blood flow direction due to the time-series change of the fluorescence contrast image acquired by the SLO or the fundus camera. Furthermore, you may acquire arteriovenous information based on the difference in the relative blood flow velocity and blood flow direction acquired by LSFG.

  In addition, when acquiring arteriovenous information as described above, a modality for obtaining an image that is the basis of the arteriovenous information is arranged together with the OCT optical system, and these images may be acquired simultaneously with the acquisition of motion contrast data. . Moreover, it is not limited to this, The modality may be arrange | positioned separately from an OCT apparatus. Further, as described above, the CPU 71 does not necessarily need to execute the determination process, and the CPU 71 may acquire the previously acquired arteriovenous information from the outside or the storage unit 74.

<Measurement of blood vessel direction>
The CPU 71 may calculate edge strength for each direction in the three-dimensional direction with respect to the three-dimensional MC data, and obtain travel direction information regarding the blood vessel region based on the edge strength in each direction. Further, the CPU 71 may give the acquired traveling direction information to the blood vessel region included in the three-dimensional MC data. Thereby, since the direction of each blood vessel included in the MC data can be detected, the degree of meandering can be easily measured.

  More specifically, as shown in FIG. 18, the CPU 71 may perform edge detection on the volume data of the three-dimensional MC data with respect to the XYZ directions, respectively, and acquire the edge detection results with respect to the XYZ directions as volume data. In this case, the volume data of the edge detection result for each direction is acquired. Here, the traveling direction of each blood vessel can be easily detected based on the edge detection result in each volume data corresponding to each point of the three-dimensional MC data. For example, when only a blood vessel extending straight in the X direction is present in the three-dimensional MC data, an edge is detected in the YZ direction, and no edge is detected in the X direction, so that the traveling direction is detected based on this. It is possible to detect the traveling direction of each blood vessel using such a relationship. In addition, although the simple example was shown for convenience of explanation, of course, even when a plurality of blood vessels travel in different directions, the above method can be applied.

  The CPU 71 may perform image processing on the three-dimensional motion contrast data based on the acquired traveling direction information, and display the three-dimensional motion contrast data reflecting the traveling direction information on the display unit (for example, FIG. 19). In this case, an arrow may be displayed on each blood vessel, or a color corresponding to the traveling direction may be given.

  When the CPU 71 analyzes the three-dimensional motion contrast data and acquires the measurement result regarding the blood vessel region, the CPU 71 may acquire the meandering degree of the blood vessel using the traveling direction information.

  Note that the detection of the traveling direction is not limited to detection of a three-dimensional direction, and may be applied to detection of a two-dimensional direction. For example, in the front MC data, edge detection may be performed in the XY directions, and a two-dimensional traveling direction may be detected based on the edge detection result.

  Note that the present invention is not limited to the above method, and it is possible to detect the running direction by performing the thinning process on each blood vessel of the MC data and obtaining the connectivity of each pixel of the skeleton generated by the thinning process. Good. For example, in the case of two dimensions, a two-dimensional traveling direction may be detected by obtaining connectivity with the surrounding 8 pixels. Similarly, in the case of three dimensions, the three-dimensional traveling direction may be detected by three-dimensionally determining the connectivity of surrounding pixels.

<Vessel running state with respect to fundus layer>
The CPU 71 may acquire layer running information indicating the running state of the blood vessel region included in the MC data with respect to the layer region formed on the eye to be examined (see, for example, FIG. 20). Further, the CPU 71 may give layer running information to the blood vessel region included in the MC data. Thereby, for example, the running state of the blood vessel relative to the layer region can be acquired, and normality / abnormality determination of the eye to be examined can be performed.

  The layer running information may be, for example, information that can be discriminated regarding the layer region in which at least one blood vessel included in the MC data is running. As the layer running information, for example, information regarding whether or not at least one blood vessel included in the MC data is running in a specific layer region may be used. Further, it may be information regarding whether or not at least one blood vessel included in the MC data travels from which layer region to which layer region.

  The MC data may be MC data relating to a plurality of layers formed on the fundus of the eye to be examined, and the CPU 71 performs processing for identifying a layer in which at least one blood vessel travels among the plurality of layers, You may acquire stratum running information.

  More specifically, the CPU 71 may acquire the layer direction information based on the traveling position of the blood vessel in the MC data and the layer position acquired by the segmentation process on the MC data or the OCT data.

  For example, the CPU 71 may compare the three-dimensional position information of a specific blood vessel included in the MC data with the three-dimensional position information of each fundus layer to specify the fundus layer in which the blood vessel K travels. In this case, the CPU 71 may specify the layer region in which the blood vessel K travels by determining whether or not the blood vessel K exists in the specific layer. For example, it is detected that the blood vessel K in which the choroid layer exists reaches the RPE layer.

  In this case, whether or not the CPU 71 has reached (extended) the second layer (for example, the RPE layer) different from the first layer with respect to the blood vessels distributed in the first layer (for example, the choroid layer). It may be determined. In addition, the CPU 71 may determine which layer the blood vessel region has reached from the first layer in the MC data. According to the above method, since it is possible to easily determine whether or not the blood vessel existing in the choroid layer has reached the RPE layer, it is possible to easily evaluate diabetic retinopathy and the like.

  As described above, by acquiring the layer region information regarding each blood vessel, the layer region information can be given to each blood vessel included in the MC data. The CPU 71 may perform image processing on the 3D motion contrast data based on the assigned layer area information, and display the 3D motion contrast data reflecting the layer area information on the display unit. In this case, a color corresponding to the layer region information may be given.

  CPU71 may acquire the measurement result regarding a blood vessel based on the provided layer area | region information. For example, the CPU 71 may acquire, as a measurement result, a two-dimensional distribution of blood vessels that have reached the second layer with respect to blood vessels that exist in the first layer, for example, obtain a density distribution specified for the blood vessels. May be. Moreover, CPU71 may acquire the measurement result regarding the blood vessel which crossed the several layer in MC data.

  The CPU 71 may obtain a blood vessel measurement result based on the layer region information two-dimensionally or three-dimensionally. Further, the CPU 71 may display the obtained measurement result on the display unit 75 as a blood vessel analysis map. Of course, the measurement result obtained is not limited to the blood vessel analysis map, and may be displayed as a blood vessel analysis chart.

<Separation of blood vessel layer using motion contrast data>
The CPU 71 may acquire blood vessel distribution information related to the two-dimensional existence amount of blood vessels in each depth region of the three-dimensional MC data. Further, the CPU 71 may separate the blood vessels included in the three-dimensional MC data for each layer based on the acquired blood vessel distribution information. That is, the blood vessel layer may be separated using MC data.

  Thereby, the blood vessel layer can be reliably separated. In the prior art, the blood vessel layer of MC data is separated using the layer boundary detection result of OCT data. For example, the NFL blood vessel layer is determined by a fixed value such as a predetermined range of the NFL / GCL layer boundary. For this reason, some blood vessels cannot be separated successfully. In addition, when OCT layer boundary detection fails, vascular layer separation may also fail. The blood vessels of the retina are present in a specific area. Therefore, from the result of OCT Angiography, it is considered that the blood vessel layer can be accurately separated by separating each blood vessel.

  More specifically, for example, the number of pixels in which the blood vessel is detected in the front MC data of each depth region may be measured as the two-dimensional existence amount of the blood vessel, and the number of blood vessel pixels in each depth region A histogram indicating the distribution of the image may be acquired (see FIG. 21). Further, the CPU 71 may obtain the density distribution or the blood vessel area of the blood vessels in the front MC data of each depth region as the two-dimensional existence amount of the blood vessels. That is, the blood vessel distribution information may be, for example, information indicating the distribution of the two-dimensional existence amount of blood vessels in each depth region of the three-dimensional MC data with respect to the depth direction.

  Note that the CPU 71 may measure the number of pixels in which a blood vessel is detected as the two-dimensional existence amount of the blood vessel, and may acquire a histogram indicating the distribution of the number of blood vessel pixels in each depth region. Note that when the three-dimensional MC data is divided in the depth direction, it may be divided in units of one pixel or in units of two or more pixels.

  The blood vessels of the fundus are generally divided into layers of the fundus to form a blood vessel layer. Therefore, the acquired blood vessel distribution information includes a mountain corresponding to each blood vessel layer. Therefore, the CPU 71 may separate blood vessels for each layer by separating each mountain in the blood vessel distribution information. In the case of separation for each mountain included in the blood vessel distribution, for example, the CPU 71 may detect the mountain depending on whether a region exceeding a certain abundance has a predetermined width in the depth direction.

  Note that the three-dimensional MC data may be divided into a plurality of parts in the front direction (direction orthogonal to the depth direction), and blood vessel distribution information may be acquired in each divided region. In this case, blood vessels may be separated into layers in each divided region. For example, the blood vessel layers may be separated in units of blocks B1, B2, and B3 in FIG.

  The CPU 71 may give layer information that can be discriminated with respect to blood vessels existing in other layers to each blood vessel separated for each layer.

<Acquisition and provision of bleeding information>
The CPU 71 may acquire bleeding information related to the blood vessel region included in the MC data. Further, the CPU 71 may give the acquired bleeding information to the blood vessel region included in the MC data.

  The bleeding information may be positional information of the bleeding region in the blood vessel region. Further, the bleeding information may be bleeding information for determining whether or not there is bleeding related to at least one blood vessel. Further, the bleeding information may be bleeding information in which the presence or absence of bleeding is specified for each blood vessel. Further, the bleeding information may be information indicating the distribution of the bleeding area.

  The bleeding information may be acquired based on front OCT data, for example. The front OCT data may be front OCT data regarding a part in the depth direction in the three-dimensional OCT data, or front OCT data regarding the entire depth direction in the three-dimensional OCT data. Of course, the bleeding information may be acquired based on the two-dimensional OCT data. In this case, position information of bleeding information may be acquired.

  With respect to the bleeding area where bleeding has occurred from the blood vessel in the OCT data, the luminance value is attenuated from the bleeding area to the back side, and the layer structure is not drawn. In the example of FIG. 22, bleeding has occurred in the left region of the image. Therefore, the position information of the bleeding region can be determined by detecting the bleeding region using these characteristics.

  Here, information regarding the bleeding region acquired by the OCT data may be added to the MC data. In this case, the CPU 71 may display the MC data to which the bleeding area is added based on the bleeding area information given to the MC data (see, for example, FIG. 23). Thereby, the blood vessel region where bleeding has occurred can be confirmed in the MC data. In this case, the position of the bleeding area is registered on the MC data by associating the position information of the bleeding area with the position information of the MC data.

  In this case, as the MC data to which the bleeding region is given, for example, MC data to which a graphic corresponding to the bleeding region is given on the MC data may be displayed, or the blood vessel region with respect to the bleeding region is displayed in a distinguishable manner. MC data may be displayed. The bleeding information may be acquired from an image acquired by other imaging means such as a fundus camera or SLO.

<Labeling>
In the analysis / display of the three-dimensional data, the CPU 71 may give a label indicating the analysis result to each voxel of the three-dimensional data. The types of labels include blood vessel region, non-blood vessel region, blood flow, blood vessel wall, blood vessel direction, blood vessel layer and depth, blood flow rate, blood flow speed, blood flow oxygen saturation, blood vessel The connection may be at least one of a normal blood vessel, an abnormal blood vessel, and bleeding.

  The label may be indicated by a numerical value, or may be indicated by a color or a graph. Further, the label may be displayed alone, or may be displayed superimposed on the OCT data (tomographic image), the fundus front image, and the OCTMC data (OCT Angiography image). Alternatively, a plurality of labels may be combined and displayed. Further, as described above, the CPU 71 may discriminate the artery and vein from other devices, OCT data, and OCTMC data, and record the discrimination result in the voxel as a label.

  Further, the CPU 71 may determine the direction of the blood vessel and record the determination result in the voxel as a label. More specifically, the MC data may be edge-detected in the XYZ directions, the direction of the blood vessel may be detected from the size of the edge component in each direction, and recorded as a label. The meandering degree of the blood vessel may be calculated from the direction label of the blood vessel, and the meandering degree may be recorded as a label. As a method for calculating the degree of meandering, a method of accumulating the difference in the direction label of the blood vessel between the previous voxel and the current voxel while tracing the blood vessel is considered.

  Further, the CPU 71 may determine whether the blood vessel is a normal blood vessel or an abnormal blood vessel by looking at the connection between the blood vessels. For example, if a blood vessel coming out of the choroid extends in the NFL direction, it is determined as abnormal. By attaching labels of normal blood vessels and abnormal blood vessels, only one of them can be displayed, or the colors can be displayed separately.

  Further, the CPU 71 may record the layer information of the blood vessel layer in the voxel as a label. That is, by applying the result of separating the blood vessel layer using the MC data to the voxel as a label, the blood flow for each layer can be displayed or used for normal / abnormal blood vessel determination as described below.

  In the measurement of the blood vessel diameter, the CPU 71 may thin the region to which the label of the blood vessel region is given and record the thinned region in the voxel as the label of the skeleton of the blood vessel (see FIG. 24). Further, the CPU 71 may measure the change amount of the blood vessel before and after thinning as the blood vessel diameter, and record the blood vessel diameter as a label in the voxel.

  More specifically, the blood vessel region detected by the MC data may be thinned, and the original blood vessel diameter may be measured from the thinned line. The thinning result and blood vessel diameter may be recorded in the voxel as a label. The skeleton of the blood vessel may be displayed by displaying only the voxel of the thinning label. In addition, by using the blood vessel diameter label, only blood vessels having a diameter greater than or equal to a certain blood vessel diameter, which changes the color of the blood vessel depending on the blood vessel diameter, may be displayed. In addition, a normal eye database of blood vessel diameters may be constructed.

  Further, the CPU 71 may discriminate the bleeding part from the OCT data and the OCTMC data and record it in the voxel as a label. In the OCT data, the bleeding part is relatively highly reflective. Further, the signal is attenuated below the bleeding part. By combining this information with the OCTMC data, a portion having the above-mentioned bleeding portion characteristic may be detected as a bleeding portion in a place where there is a blood vessel and recorded as a label.

  The label display may be displayed in chronological order for follow-up observation (for follow-up). Further, the label display may be displayed in combination with the inspection result of another device.

  FIG. 25 is an example when an OCT label is selected as a display label, and data such as a 3D map is displayed. Here, various analysis results may be given to each voxel, and a single or a plurality of labels desired by the examiner may be displayed superimposed on the three-dimensional data.

  Here, when a blood vessel label is selected, only the blood vessels of the retina are displayed, and the structure of the blood vessels is easily confirmed. Further, the blood vessel label and the layer label may be combined and displayed in different colors for each layer.

  The CPU 71 may display the arteries and veins in different colors by combining the blood vessel label and the arteriovenous label. Further, only one of the artery and vein may be displayed. Further, a blood vessel of only a desired retinal layer may be displayed by combining a blood vessel label, an arteriovenous label, and a layer boundary label.

  Thus, various data can be easily confirmed by combining a plurality of labels. Also, the examiner can freely set the combination of labels. In addition, the results of other devices may be displayed together. For example, the correlation between the visual field defect and the blood vessel defect can be confirmed by combining the results of the perimeter and the OCT Angiography result.

  According to the label display as described above, various data including OCT data, MC data, and the like are analyzed as three-dimensional data and presented to the user, so that diagnosis can be supported.

  In the above description, the fundus of the eye to be examined has been described as an example. However, the present invention is not limited to this and can be applied to the anterior eye portion of the eye to be examined. Furthermore, the present invention can be applied not only to the eye to be examined but also to other motion contrast data acquired by OCT (for example, motion contrast data in other tissues other than the eye).

It is a block diagram which shows the outline of a present Example. It is a figure which shows an example of the optical system of an OCT device. It is a figure for demonstrating acquisition of motion contrast. It is a figure which shows an example of a display screen. It is a figure which shows an example of a setting screen. It is an example of the result of a discrimination | determination process. This is an example in which measurement is performed by dividing a capillary blood vessel region into a plurality of sections. It is a display example of a blood vessel analysis map and MC data. It is an example of a blood vessel density map. It is a figure which shows an example in the case of performing the blood vessel measurement in consideration of the depth direction. It is a figure which shows an example in the case of calculating | requiring the distribution of the blood vessel measurement result three-dimensionally. It is a display example of a blood vessel analysis map and a morphological analysis map. It is an example of integration of a blood vessel measurement result and a morphological measurement result. It is an example in the case of displaying a blood vessel measurement result with time. It is an example in the case of giving arteriovenous information to a blood vessel region. It is an example of the arterial image and vein image in OCT data. This is an example of discriminating an artery and a vein based on the luminance value of the blood vessel region on the OCT data. It is a figure which shows an example in the case of acquiring driving direction information. It is an example in the case of displaying the three-dimensional motion contrast data in which traveling direction information was reflected on a display part. It is an example in the case of calculating | requiring the running state of the blood vessel with respect to a layer area | region. It is an example in the case of separating a blood vessel layer using motion contrast data. It is an example which shows the bleeding area | region in OCT data. It is an example which displays MC data to which a bleeding field was given. It is a figure which shows an example of a thinning process. This is an example when an OCT label is selected as the display label.

1 OCT motion contrast data analyzer 10 OCT device 70 Control unit 75 Display unit

Claims (18)

An ophthalmologic analyzer for analyzing test eye data including blood vessel information of a test eye,
Analysis processing means for analyzing the eye data and obtaining a measurement result related to the capillary region;
The analysis processing means obtains a measurement result related to the capillary region through a reduction process for reducing an influence on the measurement result by a large blood vessel having a larger blood vessel diameter than a capillary blood vessel. apparatus. The analysis processing means performs a process of specifying the capillary region in the eye data as the reduction process,
The ophthalmic analysis apparatus according to claim 1, wherein a measurement result regarding the capillary region is acquired based on the identified capillary region. The analysis processing means specifies, as the reduction processing, a region surrounded by blood vessels in the eye data based on a blood vessel region included in the eye data,
The ophthalmic analysis apparatus according to claim 1, wherein a measurement result relating to the capillary region is acquired based on the identified region.   4. The test eye data is at least one of OCT motion contrast data, front image data based on reflected light from the test eye, and front image data based on fluorescence from the test eye. Ophthalmic analysis device.   The ophthalmic analysis apparatus according to claim 1, wherein the analysis processing unit acquires a blood vessel density in the capillary blood vessel region as a measurement result related to the capillary blood vessel region.   The ophthalmic analysis apparatus according to claim 1, wherein the analysis processing unit obtains a measurement result related to the capillary region in a two-dimensional or three-dimensional manner.   The ophthalmic analysis apparatus according to claim 1, further comprising display control means for displaying a color map color-coded according to a measurement result at each position relating to the capillary blood vessel region.   The ophthalmic analysis apparatus according to claim 1, further comprising a blood vessel information database based on a measurement result regarding the capillary blood vessel region acquired through the reduction processing. An ophthalmologic analyzer for analyzing test eye data including blood vessel information of a test eye,
Analyzing the eye data, comprising an analysis processing means for obtaining a measurement result relating to a blood vessel region at a specific depth region,
The ophthalmic analysis apparatus characterized in that the analysis processing unit corrects the measurement result based on a measurement range of the depth region. The eye data is OCT front motion contrast data based on 3D motion contrast data in a specific depth region,
The ophthalmic analysis apparatus according to claim 9, wherein the analysis processing unit corrects a two-dimensional measurement result related to a blood vessel region in a specific depth region based on the measurement range of the depth region. The fundus data is OCT front motion contrast data for a specific layer region,
The ophthalmic analysis apparatus according to claim 9, wherein the analysis processing unit corrects a two-dimensional measurement result related to a blood vessel region at a specific depth region based on the thickness data of the layer region. An ophthalmologic analyzer for analyzing data obtained by ophthalmic OCT,
A display control means for simultaneously displaying a blood vessel analysis map based on OCT motion contrast data acquired by the ophthalmic OCT and a morphological analysis map based on OCT data acquired by the ophthalmic OCT on a monitor; Ophthalmic analysis device.   The ophthalmic analysis apparatus according to claim 12, wherein the display control means simultaneously displays the blood vessel analysis map and the morphological analysis map regarding a common layer region in the fundus.   The blood vessel analysis map and the morphological analysis map are characteristics of any one of a basic map, a difference map, a comparison map, and an examination date difference map, and maps having the same characteristics are simultaneously displayed. Any one of 13 ophthalmic analysis apparatuses. An ophthalmologic analyzer for analyzing data obtained by ophthalmic OCT,
An analysis processing unit is provided that performs an integrated measurement process that is a measurement process for integrating the blood vessel measurement result based on the OCT motion contrast data acquired by the ophthalmic OCT and the morphological measurement result based on the OCT data acquired by the ophthalmic OCT. An ophthalmologic analyzer characterized by that.   The ophthalmic analysis apparatus according to claim 15, further comprising display control means for displaying the result of the integrated measurement process as a single color map or an analysis chart when each measurement result is acquired as a two-dimensional distribution. An ophthalmologic analyzer for analyzing data obtained by ophthalmic OCT,
An ophthalmic analysis apparatus comprising: display control means for acquiring time-series data of blood vessel measurement results based on OCT motion contrast data from a storage unit and displaying the acquired blood vessel measurement results over time. An ophthalmic analysis program for causing a computer to function as various processing means of the ophthalmic analysis apparatus according to claim 1.