BLOOD FLOW ANALYSIS APPARATUS, OPHTHALMOLOGIC APPARATUS, OPERATION METHOD OF BLOOD FLOW ANALYSIS APPARATUS, AND PROGRAM

Abstract

To provide a novel technique for correctly evaluating the state of a blood vessel of a subject.SOLUTION: A blood flow analysis apparatus comprises an analysis unit and a classification unit. The analysis unit performs time frequency analysis of time series data of a blood flow speed in an eyeground of a subject. The classification unit classifies the subject into one of a plurality of groups including a healthy person group and a sick person group based on the feature amount of analysis data obtained by the analysis unit.SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The present invention relates to a blood flow analysis device, an ophthalmic device, and a blood flow analysis Operation of the device The present invention relates to a method and a program. [Background technology]

[0002] Arteriosclerosis is one of the causes of lifestyle-related diseases, and it is known that if it progresses without any noticeable symptoms, it may lead to heart disease or cerebrovascular disease. Evaluation of vascular status is useful for diagnosing and preventing cardiovascular diseases such as arteriosclerosis.

[0003] Indices for evaluating vascular conditions include the Cardio Ankle Vascular Index (CAVI) and the Ankle Brachial Index (ABI). By evaluating CAVI, it is possible to determine whether arterial stiffness is within the normal range, whether there is suspicion of arteriosclerosis, or whether it is in the border area between the normal range and the suspicion of arterial hardening. By evaluating ABI, it is possible to determine whether the occlusion state of the lower limb arteries is within the normal range, in a mild occlusion state, or in a severe occlusion state.

[0004] Meanwhile, in recent years, a method for evaluating the relationship between the dynamics of the systemic circulation and the blood flow of the retina has been attracting attention. For example, Patent Documents 1 and 2 disclose a method for generating blood flow information in the fundus by performing optical coherence tomography (OCT) on the fundus of a subject. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2013-184018 A [Patent Document 2] JP 2019-034237 A Summary of the Invention [Problem to be solved by the invention]

[0006] According to the research by the present inventors, when the vascular condition of a subject is judged using CAVI, 15.8% of the diseased group is judged to be in the normal range, and 18.2% of the healthy group is judged to be in the borderline region. Similarly, when the vascular condition of a subject is judged using ABI, 57.1% of the diseased group is judged to be in the normal range.

[0007] In other words, at present, it is difficult to correctly evaluate the vascular condition using CAVI or ABI alone, and new techniques for correctly evaluating the vascular condition of a subject are desired.

[0008] The present invention has been made in view of the above circumstances, and has an object to provide a new technique for correctly evaluating the vascular condition of a subject. [Means for solving the problem]

[0009] A first aspect of the embodiment includes an analysis unit that performs time-frequency analysis on time-series data of blood flow velocity in the fundus of a subject, and a classification unit that classifies the subject into one of a plurality of groups including a healthy subject group and a diseased subject group based on a feature amount of the analysis data obtained by the analysis unit. The analysis unit performs a time-frequency analysis on the time series data normalized in the time axis direction and the blood flow velocity axis direction. , a blood flow analysis device.

[0010] In a second aspect of the embodiment, in the first aspect, the analysis unit performs a wavelet transform on the time series data for one heartbeat period, and the classification unit classifies the subject into one of the multiple groups based on the scale value and shift value of a mother wavelet at a characteristic position of the wavelet transformed data obtained by the analysis unit.

[0011] In a third aspect of the embodiment, in the first aspect, the analysis unit performs a multi-resolution analysis in which the time series data for one beat period is decomposed into approximation terms and micro terms at a first depth, and the decomposition of the approximation terms at the mth depth (m is a natural number greater than or equal to 2) into approximation terms and micro terms at the (m+1)th depth is repeated up to the nth depth (n is a natural number), and the classification unit classifies the subject into one of the multiple groups based on the sum of squares of the approximation terms at the nth depth and the sum of squares of the micro terms at the nth depth obtained by the analysis unit.

[0012] In a fourth aspect of the embodiment, in the first aspect, the analysis unit includes a first analysis unit that performs a wavelet transform on the time series data during one heartbeat period, and a second analysis unit that performs a multi-resolution analysis to decompose the time series data during one heartbeat period into approximation terms and infinitesimal terms at a first depth, and repeat the decomposition of the approximation terms at the mth depth (m is a natural number greater than or equal to 2) into approximation terms and infinitesimal terms at the (m+1)th depth up to an nth depth (n is a natural number), and the classification unit classifies the subject into one of the multiple groups based on the scale value and shift value of a mother wavelet at a characteristic position of the wavelet transformed data obtained by the first analysis unit, and the sum of squares of the approximation terms at the nth depth and the sum of squares of the infinitesimal terms at the nth depth obtained by the second analysis unit.

[0013] In a fifth aspect of the embodiment, in the second or fourth aspect, the characteristic position is a position where the wavelet transform data is maximum.

[0014] In a sixth aspect of the embodiment, in any one of the first to fifth aspects, the blood flow velocity is acquired based on a Doppler signal due to blood flow obtained by performing Doppler OCT on the fundus.

[0015] A seventh aspect of the embodiment is an ophthalmologic apparatus including an optical system that performs OCT on measurement sites on the fundus at different timings, a blood flow information generating unit that calculates blood flow velocity at the fundus based on multiple signals obtained by the optical system, and the blood flow analysis device described in the sixth aspect.

[0016] An eighth aspect of the embodiment includes an analysis step of performing a time-frequency analysis on time series data of blood flow velocity in the fundus of a subject, and a classification step of classifying the subject into one of a plurality of groups including a healthy subject group and a diseased subject group based on a feature amount of the analysis data obtained in the analysis step. and the analyzing step includes performing a time-frequency analysis on the time series data normalized in the time axis direction and the blood flow velocity axis direction by the one or more processors. , blood flow analysis Operation of the device This is the method.

[0017] In a ninth aspect of the embodiment, in the eighth aspect, the analysis step performs a wavelet transform on the time series data for one beat period, and the classification step classifies the subject into one of the multiple groups based on the scale value and shift value of the mother wavelet at a characteristic position of the wavelet transformed data obtained in the analysis step.

[0018] In a tenth aspect of the embodiment, in the eighth aspect, the analysis step performs a multi-resolution analysis in which the time series data in one beat period is decomposed into approximation terms and infinitesimal terms at a first depth, and the decomposition of the approximation terms at the mth depth (m is a natural number greater than or equal to 2) into approximation terms and infinitesimal terms at the (m+1)th depth is repeated up to the nth depth (n is a natural number), and the classification step classifies the subject into one of the multiple groups based on the sum of squares of the approximation terms at the nth depth and the sum of squares of the infinitesimal terms at the nth depth obtained in the analysis step.

[0019] In an eleventh aspect of the embodiment, in the eighth aspect, the analysis step includes a first analysis step of performing a wavelet transform on the time series data during one beat period, and a second analysis step of performing a multi-resolution analysis of decomposing the time series data during one beat period into approximation terms and infinitesimal terms at a first depth, and repeating the decomposition of the approximation terms at the mth depth (m is a natural number greater than or equal to 2) into approximation terms and infinitesimal terms at the (m+1)th depth up to an nth depth (n is a natural number), and the classification step classifies the subject into one of the multiple groups based on the scale value and shift value of the mother wavelet at the characteristic position of the wavelet transformed data obtained in the first analysis step, and the sum of squares of the approximation terms at the nth depth and the sum of squares of the infinitesimal terms at the nth depth obtained in the second analysis step.

[0020] In a twelfth aspect of the embodiment, in the ninth or eleventh aspect, the characteristic position is a position where the wavelet transform data is maximum.

[0021] In a thirteenth aspect of the embodiment, in any one of the eighth to twelfth aspects, the blood flow velocity is acquired based on a Doppler signal due to blood flow obtained by performing Doppler OCT on the fundus.

[0022] A fourteenth aspect of the embodiment is a blood flow analysis method according to any one of the eighth to thirteenth aspects. Operation of the device A program that executes each step of the method.

[0023] The configurations according to the above-described multiple aspects can be combined in any desired manner. Effect of the Invention

[0024] According to the present invention, a new technique for correctly evaluating the vascular condition of a subject can be provided. [Brief description of the drawings]

[0025] [Figure 1]1 is a schematic diagram illustrating an example of a configuration of an optical system of an ophthalmic apparatus according to a first embodiment. [Diagram 2] 1 is a schematic diagram illustrating an example of a configuration of an optical system of an ophthalmic apparatus according to a first embodiment. [Diagram 3] 2 is a schematic diagram illustrating an example of the configuration of a processing system of the ophthalmic apparatus according to the first embodiment. FIG. [Figure 4] 2 is a schematic diagram illustrating an example of the configuration of a processing system of the ophthalmic apparatus according to the first embodiment. FIG. [Diagram 5] 2 is a schematic diagram illustrating an example of the configuration of a processing system of the ophthalmic apparatus according to the first embodiment. FIG. [Figure 6] 2 is a schematic diagram illustrating an example of the configuration of a processing system of the ophthalmic apparatus according to the first embodiment. FIG. [Figure 7] 2 is a schematic diagram illustrating an example of the configuration of a processing system of the ophthalmic apparatus according to the first embodiment. FIG. [Figure 8] 2 is a schematic diagram illustrating an example of the configuration of a processing system of the ophthalmic apparatus according to the first embodiment. FIG. [Figure 9] FIG. 2 is a schematic diagram for explaining the operation of the ophthalmologic apparatus according to the first embodiment. [Figure 10] FIG. 2 is a schematic diagram for explaining the operation of the ophthalmologic apparatus according to the first embodiment. [Figure 11] FIG. 2 is a schematic diagram for explaining the operation of the ophthalmologic apparatus according to the first embodiment. [Figure 12] FIG. 2 is a schematic diagram for explaining the operation of the ophthalmologic apparatus according to the first embodiment. [Figure 13] 4 is a flow chart illustrating an example of an operation of the ophthalmologic apparatus according to the first embodiment. FIG. [Figure 14] 4 is a flow chart illustrating an example of an operation of the ophthalmologic apparatus according to the first embodiment. FIG. [Figure 15] 4 is a flow chart illustrating an example of an operation of the ophthalmologic apparatus according to the first embodiment. FIG. [Figure 16] FIG. 2 is a schematic diagram for explaining the operation of the ophthalmologic apparatus according to the first embodiment. [Figure 17] FIG. 2 is a schematic diagram for explaining the operation of the ophthalmologic apparatus according to the first embodiment. [Figure 18]11 is a schematic diagram illustrating an example of the configuration of a processing system of an ophthalmic apparatus according to a second embodiment. FIG. [Figure 19A] 10 is a schematic diagram for explaining the operation of an ophthalmologic apparatus according to a second embodiment. FIG. [Figure 19B] 10 is a schematic diagram for explaining the operation of an ophthalmologic apparatus according to a second embodiment. FIG. [Figure 20] FIG. 11 is a flow chart illustrating an example of an operation of the ophthalmologic apparatus according to the second embodiment. [Figure 21] 10 is a schematic diagram for explaining the operation of an ophthalmologic apparatus according to a second embodiment. FIG. [Figure 22] FIG. 13 is a schematic diagram illustrating an example of the configuration of a processing system of an ophthalmic apparatus according to a third embodiment. [Diagram 23] FIG. 13 is a schematic diagram illustrating an example of the configuration of a processing system of an ophthalmic apparatus according to a fourth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] The blood flow analysis device, ophthalmic device, blood flow analysis method, and program according to the present invention will be described in detail with reference to the drawings. Note that the contents of the documents cited in this specification and any publicly known techniques can be used in the following embodiments.

[0027] The blood flow analysis device according to the embodiment performs a time-frequency analysis on time-series data of blood flow velocity of a subject, and classifies the subject into one of a plurality of groups including a healthy subject group and a diseased subject group based on the feature amount of the analysis data obtained by performing the time-frequency analysis. In some embodiments, the blood flow is blood flow in the fundus of the subject. In some embodiments, the time-series data is waveform information. Examples of the time-frequency analysis include a continuous wavelet transform (CWT), a discrete wavelet transform (DWT), a short-time Fourier transform (STFT), and a multiresolution analysis (MRA). In some embodiments, the subject is classified into one of a plurality of groups including a healthy subject group and a diseased subject group based on two or more feature amounts obtained by two or more time-frequency analyses.

[0028] The healthy subject group is a group that has been diagnosed in advance as being healthy by a doctor, etc. The diseased subject group is a group that has been diagnosed in advance as being diseased by a doctor, etc. In some embodiments, the multiple groups include a healthy subject group, a diseased subject group, and a group that is in the intermediate range between the healthy subjects and the diseased subjects (a group suspected of being diseased).

[0029] In some embodiments, the time series data of the blood flow velocity is acquired using optical coherence tomography (OCT). In some embodiments, the function of the blood flow analysis device is installed in an ophthalmic device capable of performing OCT on the subject's eye. In some embodiments, the blood flow analysis device is configured to receive the time series data of the blood flow velocity obtained by performing OCT from an external device.

[0030] The blood flow analysis method according to the embodiment is executed by the blood flow analysis device according to the embodiment. The program according to the embodiment causes a processor (computer) to execute each step of the blood flow analysis method. The recording medium according to the embodiment is a computer-readable non-transitory recording medium (storage medium) on which the program according to the embodiment is recorded.

[0031] In this specification, the term "processor" refers to a circuit such as a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), a field programmable gate array (FPGA)), etc. The processor realizes the functions according to the embodiment by, for example, reading and executing a program stored in a memory circuit or a storage device.

[0032] In the following embodiment, a case will be described in which an ophthalmic apparatus that executes Fourier-Domain type OCT realizes the functions of a blood flow analysis apparatus according to the embodiment.

[0033] In addition, an ophthalmic device combining a swept source OCT and a fundus camera will be described below, but the configuration according to the embodiment is not limited to this. For example, the type of OCT is not limited to swept source OCT, and may be spectral domain OCT or the like. Swept source OCT is a method of splitting light from a wavelength-variable light source (wavelength swept light source) into measurement light and reference light, superimposing return light of the measurement light from the test object on the reference light to generate interference light, detecting this interference light with a balanced photodiode or the like, and forming an image by applying Fourier transform or the like to detection data collected according to the wavelength sweep and the measurement light scan. Spectral domain OCT is a method of splitting light from a low-coherence light source into measurement light and reference light, superimposing return light of the measurement light from the test object on the reference light to generate interference light, detecting the spectral distribution of this interference light with a spectroscope, and forming an image by applying Fourier transform or the like to the detected spectral distribution. In other words, swept-source OCT is a method for acquiring a spectral distribution in a time division manner, and spectral-domain OCT is a method for acquiring a spectral distribution in a space division manner. Note that OCT methods applicable to the embodiment are not limited to these, and any other method (e.g., time-domain OCT) can be applied.

[0034] The ophthalmologic apparatus according to the embodiment may or may not have a function of acquiring a photograph (digital photograph) of the subject's eye, such as a fundus camera. In addition, instead of the fundus camera, any modality, such as a scanning laser ophthalmoscope (SLO), a slit lamp microscope, an anterior eye photographing camera, or a surgical microscope, may be provided. Note that a front image such as a fundus photograph can be used for observing the fundus, setting a scan area, tracking, and the like.

[0035] First Embodiment [composition] As shown in Fig. 1, an ophthalmologic apparatus 1 having a function as a blood flow analysis apparatus includes a fundus camera unit 2, an OCT unit 100, and an arithmetic and control unit 200. The fundus camera unit 2 is provided with an optical system similar to that of a conventional fundus camera. The OCT unit 100 is provided with an optical system and mechanisms for performing OCT. The arithmetic and control unit 200 includes a processor. A chin rest and a forehead rest for supporting the face of the subject are provided in positions facing the fundus camera unit 2.

[0036] [Optical system] (Fundus camera unit 2) The fundus camera unit 2 is provided with an optical system and mechanism for photographing the fundus Ef of the subject's eye E. Images obtained by photographing the fundus Ef (called fundus images, fundus photographs, etc.) include observation images and photographed images. Observation images are obtained, for example, by capturing video using near-infrared light. Photographed images are, for example, color images or monochrome images obtained using visible flash light, or monochrome images obtained using near-infrared flash light. The fundus camera unit 2 may be capable of acquiring fluorescein fluorescent images, indocyanine green fluorescent images, spontaneous fluorescent images, etc.

[0037] The fundus camera unit 2 includes an illumination optical system 10 and an imaging optical system 30. The illumination optical system 10 irradiates illumination light onto the subject's eye E. The imaging optical system 30 detects return light of the illumination light from the subject's eye E. The measurement light from the OCT unit 100 is guided to the subject's eye E through an optical path in the fundus camera unit 2, and the return light is guided to the OCT unit 100 through the same optical path.

[0038] The observation light source 11 of the illumination optical system 10 is, for example, a halogen lamp or a light-emitting diode (LED). The light (observation illumination light) output from the observation light source 11 is reflected by a reflecting mirror 12 having a curved reflecting surface, passes through a condenser lens 13, and passes through a visible cut filter 14 to become near-infrared light. The observation illumination light is then focused once in the vicinity of the imaging light source 15, reflected by a mirror 16, and passes through relay lenses 17 and 18, an aperture 19, and a relay lens 20. The observation illumination light is then reflected by the peripheral portion (the area around the hole) of the aperture mirror 21, passes through a dichroic mirror 46, and is refracted by the objective lens 22 to illuminate the subject's eye E (particularly the fundus Ef).

[0039] The return light of the observation illumination light from the subject's eye E is refracted by the objective lens 22, passes through the dichroic mirror 46, and passes through the hole formed in the central region of the aperture mirror 21. The return light that passes through the hole passes through the dichroic mirror 55, passes through the photographing focusing lens 31, and is reflected by the mirror 32. Furthermore, this return light passes through the half mirror 33A, is reflected by the dichroic mirror 33, and is imaged on the light receiving surface of the CCD image sensor 35 by the condenser lens 34. The CCD image sensor 35 detects the return light at, for example, a predetermined frame rate. When the photographing optical system 30 is focused on the fundus Ef, an observation image of the fundus Ef is obtained, and when the focus is on the anterior segment, an observation image of the anterior segment is obtained.

[0040] The imaging light source 15 is, for example, a visible light source including a xenon lamp or an LED. Light (imaging illumination light) output from the imaging light source 15 is irradiated onto the fundus Ef through the same path as the observation illumination light. Return light of the imaging illumination light from the subject's eye E is guided to the dichroic mirror 33 through the same path as the return light of the observation illumination light, passes through the dichroic mirror 33, is reflected by a mirror 36, and is imaged on the light receiving surface of a CCD image sensor 38 by a condenser lens 37.

[0041] The liquid crystal display (LCD) 39 displays a fixation target for fixating the subject's eye E. A part of the light beam (fixation light beam) output from the LCD 39 is reflected by the half mirror 33A, reflected by the mirror 32, passes through the photographing focusing lens 31 and the dichroic mirror 55, and passes through the hole of the aperture mirror 21. The fixation light beam that passes through the hole of the aperture mirror 21 passes through the dichroic mirror 46, is refracted by the objective lens 22, and is projected onto the fundus Ef. The fixation position of the subject's eye E can be changed by changing the display position of the fixation target on the screen of the LCD 39. Instead of the LCD 39, a matrix LED in which a plurality of LEDs are arranged two-dimensionally, a combination of a light source and a variable diaphragm (such as a liquid crystal diaphragm), or the like can be used as a fixation light beam generating means.

[0042] The fundus camera unit 2 is provided with an alignment optical system 50 and a focus optical system 60. The alignment optical system 50 generates an alignment target used for alignment of the optical system with respect to the subject's eye E. The focus optical system 60 generates a split target used for focus adjustment with respect to the subject's eye E.

[0043] The alignment light output from the LED 51 of the alignment optical system 50 passes through apertures 52 and 53 and a relay lens 54, is reflected by a dichroic mirror 55, and passes through a hole in the aperture mirror 21. The light that has passed through the hole in the aperture mirror 21 passes through the dichroic mirror 46 and is projected onto the subject's eye E by the objective lens 22.

[0044] The corneal reflected light of the alignment light passes through the objective lens 22, the dichroic mirror 46, and the hole, and a part of it passes through the dichroic mirror 55 and passes through the photographing focusing lens 31. The corneal reflected light that passed through the photographing focusing lens 31 is reflected by the mirror 32, passes through the half mirror 33A, is reflected by the dichroic mirror 33, and is projected by the condenser lens 34 onto the light receiving surface of the CCD image sensor 35. Based on the light receiving image (alignment target image consisting of two bright spots) by the CCD image sensor 35, manual alignment or auto alignment can be performed in the same manner as in the conventional method.

[0045] The focus optical system 60 is moved along the optical path (illumination optical path) of the illumination optical system 10 in conjunction with the movement of the photographing focusing lens 31 along the optical path (photographing optical path) of the photographing optical system 30. The reflecting rod 67 is insertable into and removable from the illumination optical path.

[0046] When performing focus adjustment, the reflecting surface of the reflecting rod 67 is obliquely disposed in the illumination light path. The focusing light output from the LED 61 passes through the relay lens 62, is split into two light beams by the split target plate 63, passes through the two-hole diaphragm 64, is reflected by the mirror 65, and is once imaged on the reflecting surface of the reflecting rod 67 by the condenser lens 66 and is then reflected. The focusing light further passes through the relay lens 20, is reflected by the aperture mirror 21, passes through the dichroic mirror 46, is refracted by the objective lens 22, and is projected onto the fundus Ef.

[0047] The fundus reflection of the focusing light passes through the same path as the cornea reflection of the alignment light and is detected by the CCD image sensor 35. Based on the received light image (split target image consisting of two bright line images) by the CCD image sensor 35, manual alignment and auto alignment can be performed in the same way as in the conventional method.

[0048] The dichroic mirror 46 combines the optical path for fundus imaging with the optical path for OCT. The dichroic mirror 46 reflects light in the wavelength band used for OCT and transmits light for fundus imaging. The optical path for OCT includes, in order from the OCT unit 100 side, a collimator lens unit 40, an optical path length changer 41, an optical scanner 42, an OCT focusing lens 43, a mirror 44, and a relay lens 45.

[0049] The optical path length changing unit 41 is movable in the direction of the arrow shown in Fig. 1 and changes the optical path length of the optical path for OCT. This change in the optical path length is used for correcting the optical path length according to the axial length of the subject's eye E, adjusting the interference state, etc. The optical path length changing unit 41 includes, for example, a corner cube and a mechanism for moving it.

[0050] The optical scanner 42 is disposed at a position optically conjugate with the pupil of the subject's eye E. The optical scanner 42 changes the traveling direction of the measurement light LS passing through the optical path for OCT. This causes the subject's eye E to be scanned with the measurement light LS. The optical scanner 42 can deflect the measurement light LS in any direction on the xy plane, and includes, for example, a galvanometer mirror that deflects the measurement light LS in the x direction and a galvanometer mirror that deflects the measurement light LS in the y direction.

[0051] (OCT unit 100) As illustrated in Fig. 2, the OCT unit 100 is provided with an optical system for performing OCT of the subject's eye E. The configuration of this optical system is similar to that of conventional swept-source OCT. That is, this optical system includes an interference optical system that splits light from a light source into measurement light and reference light, superimposes return light of the measurement light from the subject's eye E and the reference light that has passed through a reference light path to generate interference light, and detects this interference light. The detection result (detection signal) obtained by the interference optical system is a signal indicating the spectrum of the interference light, and is sent to the arithmetic and control unit 200.

[0052] The light source unit 101 includes a tunable light source capable of changing the wavelength of emitted light at high speed, similar to a typical swept-source OCT. The tunable light source is, for example, a near-infrared laser light source.

[0053] Light L0 output from light source unit 101 is guided by optical fiber 102 to polarization controller 103, where its polarization state is adjusted. Furthermore, light L0 is guided by optical fiber 104 to fiber coupler 105, where it is split into measurement light LS and reference light LR.

[0054] The reference light LR is guided by an optical fiber 110 to a collimator 111 where it is converted into a parallel beam, and is then guided to a corner cube 114 via an optical path length correction member 112 and a dispersion compensation member 113. The optical path length correction member 112 acts to match the optical path length of the reference light LR with the optical path length of the measurement light LS. The dispersion compensation member 113 acts to match the dispersion characteristics between the reference light LR and the measurement light LS.

[0055] The corner cube 114 bends the incident reference light LR in the opposite direction to the traveling direction of the reference light LR. The incident direction and the exit direction of the reference light LR with respect to the corner cube 114 are parallel to each other. The corner cube 114 is movable in the incident direction of the reference light LR, thereby changing the optical path length of the reference light LR.

[0056] 1 and 2, both an optical path length changer 41 for changing the length of the optical path (measurement optical path, measurement arm) of the measurement light LS and a corner cube 114 for changing the length of the optical path (reference optical path, reference arm) of the reference light LR are provided. In some embodiments, only one of the optical path length changer 41 and the corner cube 114 is provided. It is also possible to change the difference between the measurement optical path length and the reference optical path length using optical members other than these.

[0057] The reference light LR that has passed through the corner cube 114 passes through the dispersion compensation member 113 and the optical path length correction member 112, is converted from a parallel beam to a focused beam by the collimator 116, and enters the optical fiber 117. The reference light LR that has entered the optical fiber 117 is guided to a polarization controller 118 where its polarization state is adjusted, is guided by the optical fiber 119 to an attenuator 120 where the amount of light is adjusted, and is guided by the optical fiber 121 to a fiber coupler 122.

[0058] On the other hand, the measurement light LS generated by the fiber coupler 105 is guided by the optical fiber 127 and converted into a parallel beam by the collimator lens unit 40. The measurement light LS converted into a parallel beam passes through the optical path length change unit 41, the optical scanner 42, the OCT focusing lens 43, the mirror 44, and the relay lens 45, is reflected by the dichroic mirror 46, is refracted by the objective lens 22, and enters the subject's eye E. The measurement light LS is scattered and reflected at various depth positions in the subject's eye E. The return light of the measurement light LS from the subject's eye E travels in the opposite direction along the same path as the outward path and is guided to the fiber coupler 105, and reaches the fiber coupler 122 via the optical fiber 128.

[0059] The fiber coupler 122 generates interference light by superimposing the measurement light LS incident via the optical fiber 128 and the reference light LR incident via the optical fiber 121. The fiber coupler 122 generates a pair of interference light LC by splitting the interference light at a predetermined splitting ratio (for example, 1:1). The pair of interference light LC is guided to a detector 125 via optical fibers 123 and 124, respectively.

[0060] The detector 125 is, for example, a balanced photodiode. The balanced photodiode has a pair of photodetectors that detect a pair of interference lights LC, respectively, and outputs the difference between the detection results by these. The detector 125 sends the detection result (detection signal) to a DAQ (Data Acquisition System) 130.

[0061] The DAQ130 is supplied with a clock KC from the light source unit 101. The clock KC is generated in the light source unit 101 in synchronization with the output timing of each wavelength swept within a predetermined wavelength range by the wavelength-tunable light source. For example, the light source unit 101 optically delays one of two branched lights obtained by branching the light L0 of each output wavelength, and generates the clock KC based on the result of detecting the combined light. The DAQ130 samples the detection signal input from the detector 125 based on the clock KC. The DAQ130 sends the sampling result of the detection signal from the detector 125 to the arithmetic and control unit 200.

[0062] (Arithmetic and control unit 200) The arithmetic and control unit 200 controls each part of the fundus camera unit 2, the display device 3, and the OCT unit 100. The arithmetic and control unit 200 also executes various arithmetic processing. For example, the arithmetic and control unit 200 performs signal processing such as Fourier transform on the spectral distribution based on the detection result obtained by the detector 125 for each series of wavelength scans (for each A-line), thereby forming a reflection intensity profile for each A-line. Furthermore, the arithmetic and control unit 200 forms image data by imaging the reflection intensity profile of each A-line. The arithmetic and control unit 200 performs the arithmetic processing for this purpose in the same manner as in conventional swept-source OCT.

[0063] The arithmetic control unit 200 includes, for example, a processor, a RAM (Random Access Memory), a ROM (Read Only Memory), a hard disk drive, a communication interface, etc. Various computer programs are stored in a storage device such as a hard disk drive. The arithmetic control unit 200 may also include an operation device, an input device, a display device, etc.

[0064] [Processing system] The processing system (control system) of the ophthalmologic apparatus 1 is configured with an arithmetic control unit 200 as its central component.

[0065] Examples of the configuration of the processing system of the ophthalmic apparatus 1 are shown in Figs. 3 to 8. Fig. 3 shows an example of a functional block diagram of the processing system of the ophthalmic apparatus 1. Fig. 4 shows an example of a functional block diagram of the image forming unit 220 of Fig. 3. Fig. 5 shows an example of a functional block diagram of the data processing unit 300 of Fig. 3. Fig. 6 shows an example of a functional block diagram of the blood flow information generating unit 320 of Fig. 5. Fig. 7 shows an example of a functional block diagram of the blood flow information analyzing unit 330 of Fig. 5. Fig. 8 shows an example of a functional block diagram of the analyzing unit 350 of Fig. 7.

[0066] As shown in FIG. 3, the arithmetic and control unit 200 includes a control section 210, an image forming section 220, and a data processing section 300.

[0067] (Control unit 210) The control unit 210 controls each unit of the ophthalmologic apparatus 1. The control unit 210 includes a processor, a RAM, a ROM, a hard disk drive, etc. The functions of the control unit 210 are realized by cooperation between hardware including circuits and control software. The control unit 210 includes a main control unit 211 and a storage unit 212.

[0068] (Main control unit 211) The main controller 211 performs various controls. In particular, the main controller 211 controls the focusing drivers 31A and 43A, the CCD image sensors 35 and 38, the LCD 39, the optical path length changer 41, and the optical scanner 42 of the fundus camera unit 2. The main controller 211 also controls the light source unit 101, the reference driver 114A, the detector 125, and the DAQ 130 of the OCT unit 100. Furthermore, the main controller 211 controls an optical system driver (not shown) that drives the optical system shown in FIGS. 1 and 2.

[0069] The focusing drive unit 31A is controlled by the main control unit 211 and moves the photographing focusing lens 31 along the optical axis of the photographing optical system 30. The focusing drive unit 31A is provided with a holding member that holds the photographing focusing lens 31, an actuator that generates a driving force for moving the holding member, and a transmission mechanism that transmits the driving force. The actuator is, for example, a pulse motor. The transmission mechanism is, for example, a combination of gears or a rack and pinion. Thereby, the focusing drive unit 31A controlled by the main control unit 211 moves the photographing focusing lens 31, thereby changing the focus position of the photographing optical system 30. Note that the focusing drive unit 31A may be configured to move the photographing focusing lens 31 along the optical axis of the photographing optical system 30 manually or by a user's operation on the operation unit 242.

[0070] The focusing driver 43A is controlled by the main controller 211 and moves the OCT focusing lens 43 along the optical axis (optical path of the measurement light) of the interference optical system in the OCT unit 100. The focusing driver 43A is provided with a holding member that holds the OCT focusing lens 43, an actuator that generates a driving force for moving the holding member, and a transmission mechanism that transmits the driving force. The actuator is, for example, a pulse motor. The transmission mechanism is, for example, a combination of gears or a rack-and-pinion. Thereby, the focusing driver 43A controlled by the main controller 211 moves the OCT focusing lens 43, thereby changing the focal position of the measurement light. The focusing driver 43A may be configured to move the OCT focusing lens 43 along the optical axis of the interference optical system manually or by a user's operation on the operation unit 242.

[0071] The main control unit 211 can control the exposure time (charge accumulation time), sensitivity, frame rate, etc. of the CCD image sensor 35. The main control unit 211 can control the exposure time, sensitivity, frame rate, etc. of the CCD image sensor 38.

[0072] The main controller 211 can control the display of fixation targets and visual targets for visual acuity measurement on the LCD 39. This makes it possible to switch the visual target presented to the subject's eye E and change the type of the visual target. In addition, by changing the display position of the visual target on the LCD 39, it is possible to change the visual target presentation position with respect to the subject's eye E.

[0073] The main controller 211 can relatively change the difference between the optical path length of the reference light LR and the optical path length of the measurement light LS by controlling the optical path length changing unit 41. The main controller 211 controls the optical path length changing unit 41 so that the target site of the subject's eye E is depicted in a predetermined range within the frame of the OCT image. Specifically, the main controller 211 can control the optical path length changing unit 41 so that the target site of the subject's eye E is depicted at a predetermined z position (position in the depth direction) within the frame of the OCT image.

[0074] The main controller 211 can change the scanning position of the measurement light LS on the fundus Ef or anterior segment of the subject's eye E by controlling the optical scanner 42. The control of the optical scanner 42 includes control of the scanning position, scanning range or scanning speed by a galvanometer mirror that deflects the measurement light LS in the x direction, and control of the scanning position, scanning range or scanning speed by a galvanometer mirror that deflects the measurement light LS in the y direction. The main controller 211 can also control the scanning pattern of the measurement light LS by the optical scanner 42. Examples of the scanning pattern of the measurement light LS include horizontal scan, vertical scan, cross scan, radial scan, circular scan, concentric circle scan, and spiral scan.

[0075] The main control unit 211 can control the light source unit 101 to switch the light L0 on and off, change the amount of light L0, and the like.

[0076] The main controller 211 can relatively change the difference between the optical path length of the reference light LR and the optical path length of the measurement light LS by controlling the reference driver 114A. The reference driver 114A moves the corner cube 114 provided in the reference optical path. This changes the length of the reference optical path. The main controller 211 controls the reference driver 114A so that the target site of the subject's eye E is depicted in a predetermined range within the frame of the OCT image. Specifically, the main controller 211 can control the reference driver 114A so that the target site of the subject's eye E is depicted at a predetermined z position within the frame of the OCT image. The main controller 211 can relatively change the difference between the optical path length of the reference light LR and the optical path length of the measurement light LS by controlling at least one of the optical path length changer 41 and the reference driver 114A. In the following, the main control unit 211 is described as adjusting the optical path length difference between the measurement light LS and the reference light LR by controlling only the optical path length changing unit 41, but the optical path length difference between the reference light LR and the measurement light LS may also be adjusted by controlling only the reference driving unit 114A.

[0077] The main control unit 211 is capable of controlling the exposure time (charge accumulation time), sensitivity, frame rate, etc. of the detector 125. The main control unit 211 is also capable of controlling the DAQ .

[0078] An optical system driving unit (not shown) three-dimensionally moves the optical system (the optical system shown in Figs. 1 and 2) provided in the ophthalmic device 1. The main control unit 211 can control the optical system driving unit so as to maintain the positional relationship between the subject's eye E and the device optical system. This control is used in alignment and tracking. Tracking refers to moving the device optical system in accordance with the movement of the subject's eye E. When tracking is performed, alignment and focusing are performed in advance. Tracking is a function that maintains a suitable positional relationship with good alignment and focus by moving the device optical system in real time in accordance with the position and orientation of the subject's eye E based on an image obtained by taking a video of the subject's eye E.

[0079] (Storage unit 212) The storage unit 212 stores various types of data. Examples of data stored in the storage unit 212 include image data of an OCT image, image data of a fundus image, information about the subject's eye, and processing results by the data processing unit 300. The information about the subject's eye includes information about the subject, such as the subject's ID and name, identification information for the left eye / right eye, and electronic medical record information.

[0080] (Image forming unit 220) The image forming unit 220 forms image data of a tomographic image of the fundus Ef or the anterior segment based on a detection signal from the detector 125 sampled by the DAQ 130. The image forming unit 220 includes a processor. Note that in this specification, "image data" and an "image" based on the image data may be regarded as the same thing.

[0081] As shown in FIG. 4, the image forming section 220 includes a tomographic image forming section 221.

[0082] (Tomographic image forming unit 221) The tomographic image forming unit 221 forms image data of a tomographic image of the fundus oculi Ef based on the sampling result of the detection signal by the DAQ 130. The tomographic image forming unit 221 can form a tomographic image based on data acquired in the preliminary measurement. The processing performed by the tomographic image forming unit 221 includes noise removal (noise reduction), filtering, FFT (Fast Fourier Transform), and the like, similar to conventional spectral domain OCT. When another type of OCT is applied, the tomographic image forming unit 221 performs known processing according to the type.

[0083] Such a tomographic image forming section 221 is capable of forming a tomographic image based on data acquired by blood flow measurement (first scan and / or second scan).

[0084] For example, as disclosed in JP 2017-79886 A, in an embodiment, a scan (preliminary measurement) is performed to set a target area for blood flow measurement, and a scan (blood flow measurement) is performed to obtain blood flow information about the set area from the results of the preliminary measurement.

[0085] In the preliminary measurement, multiple cross sections of the fundus Ef are repeatedly scanned with the measurement light LS. The scanning of the multiple cross sections is, for example, a raster scan along multiple scanning lines parallel to each other. The number of scanning lines in the raster scan is, for example, 128, and a three-dimensional region of the fundus Ef is scanned (three-dimensional scan). Note that the scanning pattern and the number of scanning lines are not limited to this example.

[0086] In blood flow measurement, two types of scans (first and second scans) are performed based on the area of ​​the fundus Ef set from the results of the preliminary measurement. The area set from the results of the preliminary measurement is called the area of ​​interest. The area of ​​interest includes a blood vessel of interest and a cross section of interest. The blood vessel of interest is a blood vessel of the fundus Ef set from the results of the preliminary measurement. The blood vessel of interest may be a blood vessel in which a pulse wave is clearly observed, for example an artery. The cross section of interest is a cross section that intersects with the blood vessel of interest. Furthermore, the area of ​​interest may include one or more cross sections located in the vicinity of the cross section of interest. These one or more cross sections are the subject of an OCT scan to determine the inclination of the blood vessel of interest.

[0087] In the first scan, two or more cross sections intersecting the blood vessel of interest are scanned with the measurement light LS. The data acquired by the first scan is used to determine the inclination (direction) of the blood vessel of interest in the cross section of interest. Meanwhile, in the second scan, the cross section of interest intersecting the blood vessel of interest is repeatedly scanned with the measurement light LS. The cross section on which the first scan is performed is positioned near the cross section of interest. The second scan is a Doppler measurement using OCT (Doppler OCT).

[0088] The target cross sections of the first and second scans are preferably oriented in the xy plane so as to be perpendicular to the running direction of the blood vessel of interest. As shown in the fundus image D of Fig. 9, in the embodiment, for example, two cross sections C11 and C12 on which the first scan is performed and a cross section C2 of interest on which the second scan is performed are set in the vicinity of the optic disc Da so as to intersect with the blood vessel of interest Db. One of the two cross sections C11 and C12 is located upstream of the blood vessel of interest Db with respect to the cross section C2 of interest, and the other is located downstream. The distance (inter-cross section distance) of each cross section C11 and C12 with respect to the cross section C2 of interest is determined in advance.

[0089] The second scan is desirably performed for at least one cardiac cycle of the subject's heart. This allows blood flow information to be obtained in all time phases of the heart. The execution time of the second scan may be a fixed time (e.g., 2 seconds) that is set in advance, or may be a time set for each subject or each test. In one example of the latter, heart rate data of the subject obtained using an electrocardiograph can be used.

[0090] For example, the tomographic image forming unit 221 forms a tomographic image representing the shape of the cross section C11 and a tomographic image representing the shape of the cross section C12 based on the detection result of the interference light LC obtained by the first scan on the cross sections C11 and C12. At this time, one tomographic image can be formed by scanning the cross section C11 once, and one tomographic image can be formed by scanning the cross section C12 once. Alternatively, one tomographic image can be obtained based on multiple tomographic images obtained by scanning the cross section C11 multiple times, and one tomographic image can be obtained based on multiple tomographic images obtained by scanning the cross section C12 multiple times. Examples of the process of obtaining one tomographic image from multiple tomographic images include a process of averaging multiple tomographic images to improve image quality and a process of selecting an optimal one from multiple tomographic images.

[0091] Further, the tomographic image forming unit 221 forms a group of tomographic images representing a time-series change in the shape of the cross section C2 of interest based on the detection result of the interference light LC obtained by the second scan on the cross section C2 of interest. This process will be described in more detail. In the second scan, the cross section C2 of interest is repeatedly scanned as described above. The tomographic image forming unit 221 sequentially receives detection signals from the detector 125 of the OCT unit 100 in response to the second scan. The tomographic image forming unit 221 forms one tomographic image of the cross section C2 of interest based on a group of detection signals corresponding to one scan of the cross section C2 of interest. The tomographic image forming unit 221 repeats this process the number of times the second scan is repeated to form a series of tomographic images in time series. Here, these tomographic images may be divided into a plurality of groups, and the tomographic images of each group may be averaged to improve image quality.

[0092] (User Interface 240) The ophthalmic apparatus 1 is provided with a user interface 240. The user interface 240 includes a display unit 241 and an operation unit 242. The display unit 241 includes the display device 3. The operation unit 242 includes various operation devices and input devices. The user interface 240 may include a device such as a touch panel that integrates a display function and an operation function. It is also possible to construct an embodiment that does not include at least a part of the user interface 240. For example, the display device may be an external device connected to the ophthalmic apparatus.

[0093] (Data processing unit 300) The data processing unit 300 performs various data processing (image processing) and analysis processing on the image data formed by the image forming unit 220. For example, the data processing unit 300 executes correction processing such as image brightness correction and dispersion correction. In addition, the data processing unit 300 performs various image processing and analysis processing on images (anterior eye images, etc.) obtained using the CCD image sensors 35 and 38.

[0094] The data processing unit 300 can perform known image processing such as an interpolation process for interpolating pixels between tomographic images to form volume data (voxel data) of the subject's eye E. When an image based on the volume data is to be displayed, the data processing unit 300 performs a rendering process on the volume data to form a pseudo three-dimensional image as viewed from a specific line of sight.

[0095] The data processing unit 300 can form an image by performing various renderings on the acquired volume data (three-dimensional data set, stack data, etc.). The images formed by the data processing unit 300 include a B-mode image (longitudinal section image, axial section image) in an arbitrary section, a C-mode image (transverse section image, horizontal section image) in an arbitrary section, a projection image, a shadowgram, etc. Images of an arbitrary section such as a B-mode image or a C-mode image are formed by selecting pixels (voxels) on a specified section from a three-dimensional data set. A projection image is formed by projecting a three-dimensional data set in a predetermined direction (z direction, depth direction, axial direction). A shadowgram is formed by projecting a part of a three-dimensional data set (for example, partial data corresponding to a specific layer) in a predetermined direction. An image with a viewpoint on the front side of the subject's eye, such as a C-mode image, a projection image, or a shadowgram, is called an en-face image.

[0096] Furthermore, the data processing unit 300 can construct a B-mode image or a front image (vessel-enhanced image, angiogram) in which retinal blood vessels and choroidal blood vessels are emphasized based on data (e.g., B-scan image data) collected in a time series by OCT. For example, time-series OCT data can be collected by repeatedly scanning approximately the same part of the subject's eye E.

[0097] In some embodiments, the data processor 300 compares time-series B-scan images obtained by B-scanning substantially the same region, and constructs an enhanced image in which the changed portion is emphasized by converting pixel values ​​of the changed portion of the signal intensity into pixel values ​​corresponding to the changed portion. Furthermore, the data processor 300 extracts information of a predetermined thickness of a desired region from the constructed multiple enhanced images, and constructs the information as an en-face image to form an OCT angiogram.

[0098] Furthermore, the data processing unit 300 can specify a predetermined layer region in the tomographic image formed by the image forming unit 220. Examples of layer regions that can be specified include the inner limiting membrane, nerve fiber layer, ganglion cell layer, inner plexiform layer, inner nuclear layer, outer plexiform layer, outer nuclear layer, outer limiting membrane, photoreceptor layer, retinal pigment epithelium layer, choroid, sclera, and interfaces between each layer region.

[0099] The process of identifying a predetermined layer region from a tomographic image typically includes a segmentation process. The segmentation process is a known process for identifying a partial region in a tomographic image. The data processing unit 300 performs the segmentation process, for example, based on the luminance value of each pixel in the tomographic image. That is, each layer region of the fundus Ef has a characteristic reflectance, and the image regions corresponding to these layer regions also have characteristic luminance values. The data processing unit 300 can identify a target image region (layer region) by performing the segmentation process based on these characteristic luminance values. The data processing unit 300 identifies a layer region (for example, a layer region between the internal limiting membrane and the Bruch's membrane) in which at least a retinal artery or a retinal vein exists. In some embodiments, the data processing unit 300 identifies an image region in a phase image (described later) corresponding to the layer region identified in the tomographic image.

[0100] The data processing unit 300 generates blood flow information based on the image formed by the image forming unit 220, and can analyze the generated blood flow information to classify the subject as belonging to a diseased (healthy) group or not. The blood flow information includes the blood flow velocity in the fundus Ef. As shown in FIG. 5, the data processing unit 300 includes a phase image forming unit 310, a blood flow information generating unit 320, and a blood flow information analyzing unit 330.

[0101] (Phase image forming unit 310) The phase image forming section 310 forms a phase image based on data (OCT data) obtained by repeatedly performing OCT scanning on a (substantially the same) predetermined site of the fundus Ef. The phase image forming section 310 forms a phase image representing a time series change in phase difference in the scanned cross section based on data acquired in the preliminary measurement. In the preliminary measurement, multiple cross sections of the fundus Ef are repeatedly scanned. The phase image forming section 310 forms a phase image in each of these cross sections based on data acquired by the preliminary measurement.

[0102] In addition, the phase image forming unit 310 forms a phase image based on the data acquired by the second scan of the blood flow measurement. For example, the phase image forming unit 310 forms a phase image representing a time-series change in the phase difference in the cross section C2 of interest based on the detection result of the interference light LS obtained by the second scan on the cross section C2 of interest.

[0103] The data used to form the phase image may be the same as the data used by the tomographic image forming unit 221 to form the tomographic image. In this case, the tomographic image of the scanned cross section and the phase image can be easily aligned. That is, for a tomographic image and a phase image formed based on the same data, the pixels of the tomographic image and the pixels of the phase image can be naturally associated with each other.

[0104] An example of a method for forming a phase image will be described. The phase image of this example is obtained by calculating the phase difference between adjacent A-line complex signals (signals corresponding to adjacent scanning points). In other words, the phase image of this example is formed for each pixel of the tomographic image of the scanned cross section based on the time series change of the pixel value (luminance value) of the pixel. For any pixel, the phase image forming unit 310 considers a graph of the time series change of the luminance value. The phase image forming unit 310 obtains a phase difference Δφ between two time points t1 and t2 (t2=t1+Δt) that are separated by a predetermined time interval Δt in this graph. Then, this phase difference Δφ is defined as the phase difference Δφ(t1) at time point t1 (more generally, any time point between two time points t1 and t2). By performing this process for each of a large number of time points set in advance, the time series change of the phase difference at the pixel is obtained.

[0105] A phase image is an image that represents the phase difference value of each pixel at each time point. This imaging process can be realized, for example, by representing the phase difference value with a display color or brightness. In this case, a different color (e.g., red) can represent an increase in phase along the time series, and a different color (e.g., blue) can represent a decrease. The magnitude of the amount of change in phase can also be represented by the intensity of the display color. By adopting such a representation method, it becomes possible to present the direction and magnitude of blood flow with color and intensity. A phase image is formed by performing the above process for each pixel.

[0106] The time series change of the phase difference can be obtained by making the above-mentioned time interval Δt sufficiently small to ensure phase correlation. At this time, oversampling is performed by setting the time interval Δt to a value less than the time corresponding to the resolution of the tomographic image in the scanning of the measurement light LS.

[0107] (Blood flow information generation unit 320) The blood flow information generating unit 320 generates blood flow information of a specific layer region (e.g., a layer region between the internal limiting membrane and Bruch's membrane) identified as described above in the tomographic image formed by the image forming unit 220 (specifically, the phase image formed by the phase image forming unit 310).

[0108] The blood flow information generating unit 320 processes data acquired by blood flow measurement of a region set based on the preliminary measurement. In this example, the blood flow information generating unit 320 obtains the inclination of a blood vessel of interest in a cross section of interest based on data acquired by a first scan for one blood vessel. Furthermore, the blood flow information generating unit 320 obtains blood flow information related to the blood vessel of interest based on the inclination of the blood vessel of interest obtained based on the first scan and data acquired by a second scan. The blood flow information includes, for example, blood flow velocity and blood flow amount. As shown in FIG. 6, the blood flow information generating unit 320 includes a blood vessel region specifying unit 321, an inclination calculating unit 322, a blood flow velocity calculating unit 323, a blood vessel diameter calculating unit 324, and a blood flow amount calculating unit 325.

[0109] (Vascular region identification unit 321) The vascular region identifying unit 321 identifies a vascular region corresponding to a blood vessel of interest in a tomographic image formed by the tomographic image forming unit 221. Furthermore, the vascular region identifying unit 321 identifies a vascular region corresponding to a blood vessel of interest in a phase image formed by the phase image forming unit 310. The vascular region is identified by analyzing pixel values ​​of each image (e.g., threshold processing). In some embodiments, the vascular region identifying unit 321 identifies a vascular region by performing known image processing such as threshold processing, edge detection, binarization, thinning, and region growing on the tomographic image or phase image.

[0110] In some embodiments, when the shape of a vascular region depicted in a phase image is approximately circular, the vascular region identifying unit 321 identifies the vascular region by identifying an approximately circular region in which there is a large change in brightness difference or phase difference.

[0111] In this embodiment, it is possible to form a tomographic image and a phase image from the same data. As a result, the vascular region specifying unit 321 can specify a vascular region in a tomographic image in which the cross-sectional morphology is depicted relatively clearly, and specify a region in a phase image corresponding to the specified vascular region as a vascular region. Even if a tomographic image and a phase image are created from different data, if their registration is possible directly or indirectly, the result of specifying the vascular region in one image can be used to specify the vascular region in the other image.

[0112] In some embodiments, the vascular region specifying part 321 specifies a specified vascular region based on the operation content by the user on the operation part 242. The user can specify a vascular region corresponding to a desired blood vessel of interest by performing an operation on the operation part 242 while viewing a front image or a cross-sectional phase image of the fundus Ef displayed on the display part 241 by the main control part 211.

[0113] (Slope calculation unit 322) The gradient calculation unit 322 calculates the gradient of the blood vessel Db of interest in the cross section C2 of interest based on the data acquired by the first scan. At this time, it is also possible to further use data acquired by the second scan. The gradient calculation unit 322 calculates the gradient of the blood vessel Db of interest in the cross section C2 of interest based on the distance between the cross sections and the result of identifying the vascular region. The distance between the cross sections may include the distance between the cross sections C11 and C12. The distance between the cross sections may also include the distance between the cross section C11 and the cross section C2 of interest, and the distance between the cross section C12 and the cross section C2 of interest.

[0114] An example of a method for calculating the inclination of the blood vessel of interest Db will be described with reference to FIG. 10. The tomographic images G11 and G12 are tomographic images representing the cross section C11 to which the first scan is applied, and the cross section C12, respectively. The tomographic image G2 is tomographic images representing the cross section C2 to which the second scan is applied. The symbols V11, V12, and V2 indicate the blood vessel region in the tomographic image G11, the blood vessel region in the tomographic image G12, and the blood vessel region in the tomographic image G2, respectively. These blood vessel regions correspond to the cross section of the blood vessel of interest Db. In FIG. 10, the z coordinate axis is oriented downward, which is substantially the same as the irradiation direction of the measurement light LS (the direction of the optical axis of the optical path of the measurement light LS, the axial direction, and the A-line direction). The interval between adjacent tomographic images (cross sections) is L.

[0115] In one example, the gradient calculation unit 322 calculates the gradient U of the blood vessel of interest Db in the cross section of interest C2 based on the positional relationship of the three blood vessel regions V11, V12, and V2. This positional relationship is obtained, for example, by connecting the three blood vessel regions V11, V12, and V2. Specifically, the gradient calculation unit 322 identifies characteristic points of each of the three blood vessel regions V11, V12, and V2, and connects these characteristic points. These characteristic points include the center position, the center of gravity position, the top (the position with the smallest z coordinate value), and the bottom (the position with the largest z coordinate value). In addition, the method of connecting these characteristic points includes a method of connecting them with a line segment, a method of connecting them with an approximation curve (such as a spline curve or a Bezier curve), and the like.

[0116] Furthermore, the slope calculation unit 322 calculates the slope U based on a line connecting these feature points. When a line segment is used, for example, the slope U is calculated based on the slope of a first line segment connecting a feature point of the vascular region V2 in the target cross section C2 and a feature point of the vascular region V11 in the cross section C11, and the slope of a second line segment connecting the feature point of the vascular region V2 and a feature point of the vascular region V12 in the cross section C12. As an example of this calculation process, the average value of the slopes of the two line segments can be calculated. Also, as an example of connecting by an approximation curve, the slope of the approximation curve at the intersection position of the approximation curve and the target cross section C2 can be calculated. Note that the inter-section distance L is used when embedding these tomographic images G11, G12, and G2 in the xyz coordinate system in the process of calculating the line segment and the approximation curve.

[0117] In this example, the vascular regions in three cross sections are considered, but it is also possible to obtain the inclination by considering the vascular regions in two cross sections. As a specific example, it is possible to obtain the inclination U of the blood vessel Db of interest in the cross section C2 of interest based on the vascular region V11 in the cross section C11 and the vascular region V12 in the cross section C12. Alternatively, it is also possible to use the inclination U of the first line segment or the second line segment.

[0118] The method of calculating the inclination of the blood vessel of interest in the cross section of interest is not limited to the above. For example, the following method can be applied. First, an OCT scan is performed on a cross section that intersects with the cross section of interest and runs along the blood vessel of interest. Next, a tomographic image is formed based on data acquired by the OCT scan, and an image region (layer region) corresponding to a predetermined tissue is specified by performing segmentation of the tomographic image. The specified layer region is, for example, a layer region (ILM region) corresponding to the internal limiting membrane. The internal limiting membrane is a retinal tissue that defines the boundary between the retina and the vitreous body, and is depicted relatively clearly. Furthermore, a line segment that approximates the shape of the specified layer region is obtained, and the inclination of the line segment is obtained. The inclination of the approximated line segment is expressed, for example, as an angle with respect to the z coordinate axis or an angle with respect to the xy plane (i.e., a plane perpendicular to the z coordinate axis).

[0119] (Blood velocity calculation unit 323) The blood flow velocity calculation unit 323 calculates the blood flow velocity of blood flowing through the blood vessel of interest Db in the cross section of interest C2 based on the time series change of the phase difference obtained as a phase image. The calculation target may be the blood flow velocity at a certain time point, or the time series change of this blood flow velocity (blood flow velocity change information). In the former case, for example, it is possible to selectively obtain the blood flow velocity in a predetermined time phase of the electrocardiogram (for example, the time phase of the R wave). In the latter case, the time range is the entire time during which the cross section of interest C2 is scanned, or any part of it.

[0120] When the blood flow velocity change information is obtained, the blood flow velocity calculation unit 323 can calculate the statistical values ​​of the blood flow velocity in the time range. The statistical values ​​include the average value, standard deviation, variance, median value, maximum value, minimum value, maximum value, minimum value, etc. Also, a histogram of the blood flow velocity values ​​can be created.

[0121] The blood flow velocity calculation unit 323 calculates the blood flow velocity using the Doppler OCT technique as described above. At this time, the gradient U of the blood vessel Db of interest in the cross section C2 of interest calculated by the gradient calculation unit 322 is taken into consideration. Specifically, the gradient calculation unit 322 uses the following equation.

[0122]

number

[0123] In formula (1), Δf represents the Doppler shift that the scattered light of the measurement light LS undergoes, n represents the refractive index of the medium (blood), v represents the flow velocity of the medium (blood flow velocity), θ represents the angle between the incident direction of the measurement light LS and the flow direction of the medium (inclination U), and λ represents the central wavelength of the measurement light LS.

[0124] In the embodiment, n and λ are known, Δf is obtained from the time series change of the phase difference, and θ is obtained from the slope U (or θ is obtained as the slope U). By substituting these values ​​into equation (1), the blood flow velocity v is calculated.

[0125] (Blood vessel diameter calculation unit 324) The blood vessel diameter calculation unit 324 calculates the diameter of the blood vessel Db of interest in the cross section C2 of interest by analyzing the fundus image or OCT image. When a fundus image is used, an image of a portion of the fundus Ef including the position of the cross section C2 of interest is captured, and the diameter of the blood vessel Db of interest in the cross section C2 of interest, that is, the diameter of the blood vessel region V2, is calculated based on the fundus image (e.g., a color fundus image, a red-free image, etc.) obtained thereby. When an OCT image is used, the OCT image is, for example, a tomographic image formed based on the second scan, or an image formed based on a preliminary measurement. Such calculation of the blood vessel diameter is performed in the same manner as in the conventional method.

[0126] (Blood flow calculation unit 325) The blood flow rate calculation unit 325 calculates the flow rate of blood flowing through the interested blood vessel Db based on the calculation results of the blood flow velocity and the blood vessel diameter. An example of this process will be described below.

[0127] Assuming that blood flow in a blood vessel is Hagen-Poiseuille flow, and assuming that the diameter of the blood vessel is w and the maximum velocity of the blood flow is Vm, the blood flow rate Q is expressed by the following equation.

[0128]

number

[0129] The blood flow calculation unit 325 calculates the blood flow Q by substituting the blood vessel diameter calculation result w by the blood vessel diameter calculation unit 324 and the maximum value Vm based on the blood flow velocity calculation result by the blood flow velocity calculation unit 323 into equation (2).

[0130] (Blood flow information analysis unit 330) The blood flow information analysis unit 330 executes an analysis process using the blood flow information generated by the blood flow information generation unit 320. In this example, the blood flow information generation unit 320 generates the blood flow velocity of one retinal artery or retinal vein as blood flow information. The blood flow information analysis unit 330 generates time series data of the blood flow information (i.e., data representing the change over time of the blood flow velocity) representing the change over time of the blood flow information generated by the blood flow information generation unit 320. The blood flow information analysis unit 330 acquires the feature amount of the blood flow information by performing a time-frequency analysis on the generated time series data of the blood flow information, and classifies the subject into one of a plurality of groups including a healthy subject group and a diseased subject group based on the acquired feature amount.

[0131] Such a blood flow information analysis unit 330 includes an extraction unit 340, an analysis unit 350, a classification unit 360, and a learning unit 370, as shown in FIG.

[0132] (Extraction part 340) The extraction unit 340 extracts blood flow information (time series data of blood flow velocity) for one pulse period from the blood flow information (time series data of blood flow velocity) generated by the blood flow information generation unit 320. The extraction unit 340 includes a cardiac cycle determination unit 341, an interpolation unit 342, and a normalization unit 343.

[0133] (Cardiac cycle identification unit 341) The cardiac cycle determination unit 341 determines a cardiac cycle (one pulsation period) from time series data of blood flow velocity. The blood flow velocity changes due to the pulsation of the heart. The cardiac cycle determination unit 341 determines the cardiac cycle, for example, by determining a change point of the blood flow velocity corresponding to the pulsation.

[0134] Fig. 11 is a diagram illustrating the operation of the extraction unit 340. Fig. 11 shows an example of a blood flow velocity waveform, which is time-series data of the blood flow velocity. In Fig. 11, the horizontal axis represents time, and the vertical axis represents the blood flow velocity.

[0135] The cardiac cycle determination unit 341 determines the period between two timings when the blood flow velocity reaches a predetermined value from the time series data of the blood flow velocity as the cardiac cycle (one heartbeat period) T required for one heartbeat. HRThe predetermined value may be a minimum value, a maximum value, a minimum value, a maximum value, etc. In the embodiment, the cardiac cycle determination unit 341 determines the period between two timings at which the blood flow velocity reaches the minimum value as the cardiac cycle T HR The cardiac cycle determination unit 341 determines the cardiac cycle T HR The statistical values ​​may be the mean, standard deviation, variance, median, maximum, minimum, local maximum, local minimum, etc.

[0136] (Interpolation unit 342) The interpolation unit 342 interpolates the cardiac cycle T HR The time series data of the blood flow velocity during the period is normalized in the time axis direction. Normalization in the time axis direction corresponds to normalization of the cardiac cycle. Specifically, the interpolation unit 342 performs an interpolation process on the time series data of the blood flow velocity whose cardiac cycle has been identified by the cardiac cycle identification unit 341 so that the number of measurement samples in a predetermined period becomes a predetermined number of samples. The predetermined period can be, for example, one beat period, one second, two seconds, etc. The predetermined number of samples can be, for example, two beat periods, one second, two seconds, etc. k (k is a natural number, for example, k=7) points. In this embodiment, the interpolation unit 342 performs the interpolation such that the number of sampling points in one pulsation period is 2 7 Perform interpolation to get (=128) points (dimensions).

[0137] If the number of measurement samples in a predetermined period is less than the predetermined number of samples, the interpolation unit 342 can add blood flow velocities at one or more sampling points by interpolation. In some embodiments, the interpolation unit 342 adds one or more sampling points using a known interpolation method such as linear interpolation, Lagrange interpolation, or spline interpolation.

[0138] When the number of measurement samples in a given period exceeds a given number of samples, the interpolation unit 342 can thin out the blood flow velocities at one or more sampling points.

[0139] By normalizing the time series data of blood flow velocity in the time axis direction in this manner, it becomes possible to improve the accuracy of analysis of the time series data of multiple subjects with different heart rates (cardiac cycles).

[0140] (Normalization unit 343) The normalization unit 343 normalizes the time series data of the blood flow velocity in the direction of the blood flow velocity axis, to which the sampling points have been added (or thinned out) by the interpolation unit 342. The normalization in the direction of the blood flow velocity axis corresponds to the normalization of the blood flow velocity (amplitude). Specifically, the normalization unit 343 normalizes the time series data of the blood flow velocity in the direction of the blood flow velocity axis, as shown in FIG. 11 . HR For example, the normalization unit 343 normalizes the blood flow velocity in the cardiac cycle T HR The difference between the maximum and minimum values ​​of blood flow velocity in HR The blood flow velocity is normalized so that the maximum value is "1" and the minimum value is "0".

[0141] By normalizing the time series data of blood flow velocity in this manner in the blood flow velocity axis direction, it is possible to improve the accuracy of analysis of time series data of blood flow velocity of multiple subjects with different maximum values, minimum values, or fluctuation ranges of blood flow velocity.

[0142] In some embodiments, the normalization process by the normalization unit 343 is omitted for the time series data of blood flow velocity that has been subjected to the interpolation process by the interpolation unit 342. In some embodiments, the interpolation unit 342 is configured to perform the above-mentioned interpolation process on the time series data of blood flow velocity normalized by the normalization unit 343.

[0143] (Analysis Department 350) The analysis unit 350 performs time-frequency analysis on the time series data of blood flow velocity extracted by the extraction unit 340, and obtains the feature amount of the acquired analysis data. In this embodiment, the analysis unit 350 performs continuous wavelet transform on the time series data of blood flow velocity in one pulsation period, and obtains the scale value and shift value of the mother wavelet at the feature position of the acquired wavelet transformed data. Hereinafter, the continuous wavelet transform is simply referred to as "wavelet transform".

[0144] Such an analysis section 350 includes a wavelet transformation section 351, a feature position search section 352, and a feature amount specification section 353, as shown in FIG.

[0145] (Wavelet transform unit 351) The wavelet transform unit 351 performs wavelet transform on the time series data of the blood flow velocity in one pulsation period.

[0146] For example, the wavelet function Ψ a,b (t) (t is time) is defined as in equation (3). In equation (3), "a" represents a scale value that converts the time axis by "1 / a" times, and "b" represents a shift value that moves the time axis by "b".

[0147]

number

[0148] In addition, when a Mexican Hat wavelet is adopted as the mother wavelet, for example, as shown in the following formula (4), the wavelet function Ψ a,b (t) is expressed as equation (5).

[0149]

number

[0150]

number

[0151] Here, the time series data of the blood flow velocity in one pulsation period extracted by the extraction unit 340 is taken as the signal to be analyzed x(t). The wavelet transformation unit 351 performs a wavelet transformation on the signal to be analyzed x(t) according to equation (6) to obtain transformed data W ΨFind [x(t)].

[0152]

number

[0153] (Feature position search unit 352) The feature position search unit 352 searches the transformed data W obtained by the wavelet transform unit 351. Ψ The feature position search unit 352 searches for the feature position of [x(t)]. Ψ Search for feature positions based on [x(t)]. As an example of feature positions, Ψ The position showing the maximum of [x(t)], the transformation data W Ψ The position of the minimum of [x(t)], the transformation data W Ψ In this embodiment, the feature position search unit 352 searches the transformed data W Ψ The position where [x(t)] is maximum is searched for as the feature position.

[0154] (Feature identification unit 353) The feature quantity specification unit 353 is a wavelet transform unit 351. Ψ Specifically, the feature quantity specifying unit 353 specifies the feature quantity of the transformed data W at the feature position searched for by the feature position search unit 352. Ψ The feature quantity of [x(t)] is identified. The feature quantity includes a scale value "a" and a shift value "b". In this embodiment, the feature quantity identifying unit 353 identifies the feature quantity of the transformed data W Ψ The scale value "a" and shift value "b" at the position where [x(t)] is maximum are identified as features.

[0155] 12 is a diagram illustrating the operation of the analysis unit 350. In FIG. 12, when the vertical axis is the scale value "a" and the horizontal axis is the shift value "b", the converted data W calculated using the formula (6) is Ψ 12 is a schematic diagram of a plot of [x(t)]. Note that in Fig. 12, the vertical and horizontal axes are each normalized to the maximum value.

[0156] That is, the wavelet transform unit 351 performs a wavelet transform on the signal under analysis x(t) according to equation (6). As a result, the transformed data W Ψ [x(t)] is distributed as shown in FIG. 12. In FIG. 12, the transformed data W Ψ The value of [x(t)] is expressed as shades of gray (the larger the value, the darker it is).

[0157] The feature position search unit 352 uses the transformed data W calculated by the wavelet transform unit 351. Ψ The position showing the maximum of [x(t)] is searched for, and the feature position CP is identified. The feature amount identifying unit 353 identifies the scale value a0 and the shift value b0 at the feature position CP identified by the feature position searching unit 352 as the feature amount.

[0158] (Classification Department 360) The classification unit 360 classifies the converted data W obtained by the analysis unit 350. Ψ Based on the scale value and shift value of the mother wavelet at the feature position of [x(t)], the subject is classified into one of a plurality of groups including a healthy subject group and a diseased subject group. As described above, the healthy subject group is a group that has been diagnosed as healthy by a doctor in advance, and the diseased subject group is a group that has been diagnosed as diseased by a doctor in advance.

[0159] In some embodiments, the classifier 360 classifies the subject into either a healthy group or a diseased group based on the scale and shift values.

[0160] In some embodiments, the multiple groups include a healthy subject group, a diseased subject group, and a group in an intermediate region between the healthy subject and the diseased subject. In this case, the classification unit 360 classifies the subject into one of the healthy subject group, the diseased subject group, and the intermediate region group based on the scale value and the shift value. By classifying the subject into the intermediate region group, it is possible to prompt the subject, the examiner, the doctor, or the like to particularly carefully examine the test results of the detailed test on the subject.

[0161] In some embodiments, the classifier 360 classifies subjects into an intermediate group and a non-intermediate group based on the scale and shift values.

[0162] In some embodiments, the classifier 360 classifies the subject as described above by comparing at least one of the scale and shift values ​​to a predetermined threshold.

[0163] In some embodiments, the classification unit 360 classifies the subjects as described above based on the scale value and shift value using a classification model obtained by analyzing the time series data of the blood flow velocity of multiple subjects in advance. A known analysis method such as regression analysis (simple regression analysis, multiple regression analysis) and machine learning is used for the advance analysis of the time series data of the blood flow velocity of multiple subjects. The analysis method by machine learning includes learning of classification models such as a linear support vector machine, a nonlinear support vector machine, a decision tree method, a random forest method, and a k-nearest neighbor method. In this case, the classification unit 360 can classify the subjects as described above based on the scale value and shift value using a regression line or regression curve obtained by regression analysis. Alternatively, the classification unit 360 can classify the subjects as described above based on the scale value and shift value using a classification model (trained model) obtained by machine learning. In this embodiment, the classification unit 360 classifies the subjects as described above based on the scale value and shift value using a classification model that functions as a nonlinear support vector machine.

[0164] (Learning Section 370) The learning unit 370 generates a classification model used by the classification unit 360 to classify subjects. The learning unit 370 generates the classification model by performing a known supervised machine learning method using a combination of scale values ​​and shift values ​​obtained from the time series data of the blood flow velocity of the subject as training data and the classification result of the training data as teacher data. The classification result is determined in advance by a doctor or the like as to which of the above multiple groups the subject is classified into.

[0165] The above-described functions of the data processing unit 300 are realized by one or more processors. In some embodiments, the functions of the data processing unit 300 are realized by a plurality of processors that execute the functions of each unit of the data processing unit 300.

[0166] The ophthalmic apparatus 1 or the blood flow information analysis unit 330 is an example of a "blood flow analysis apparatus" according to the embodiment. The Doppler shift Δf is an example of a "Doppler signal" according to the embodiment. In FIG. 1, the optical system from the objective lens 22 to the OCT unit 100 is an example of an "optical system" according to the embodiment.

[0167] [Operation] The operation of the ophthalmologic apparatus 1 according to the embodiment will be described.

[0168] An example of the operation of the ophthalmic apparatus 1 is shown in Figs. 13 to 15. Fig. 13 shows a flow diagram of an example of the operation of the ophthalmic apparatus 1. Fig. 14 shows a flow diagram of a processing example of step S1 in Fig. 13. Fig. 15 shows a flow diagram of a processing example of step S5 in Fig. 13. The storage unit 212 stores a computer program for realizing the processing shown in Figs. 13 to 15. The main control unit 211 operates according to this computer program to execute the processing shown in Figs. 13 to 15. It is assumed that general preparation processing such as alignment, focus adjustment, interference sensitivity adjustment, and z position adjustment has already been completed.

[0169] (S1: Generate blood flow information) First, the control unit 210 (main control unit 211) causes the blood flow information generating unit 320 to generate blood flow information including the blood flow velocity of one retinal artery or retinal vein of the subject's eye E. Next, the control unit 210 controls the blood flow information analyzing unit 330 to generate time series data of the blood flow velocity generated by the blood flow information generating unit 320.

[0170] Specifically, the control unit 210 controls the optical system of the OCT unit 100 and the like to perform OCT measurement, and causes the blood flow information generating unit 320 to generate blood flow information using the acquired measurement results. The blood flow information analyzing unit 330 generates time series data of blood flow information (time series data of blood flow velocity) by chronologically arranging the blood flow information generated by the blood flow information generating unit 320. Details of step S1 will be described later.

[0171] (S2: Identify the cardiac cycle) Next, the control unit 210 causes the cardiac cycle determination unit 341 to determine the cardiac cycle from the time-series data generated in step S1 as described above.

[0172] (S3: Interpolation) Next, the control unit 210 causes the interpolation unit 342 to interpolate the time series data of the blood flow velocity in the cardiac cycle identified in step S2 as described above.

[0173] (S4: Normalization) Next, the control unit 210 causes the normalization unit 343 to normalize the time series data of the blood flow velocity interpolated in step S3 as described above.

[0174] (S5: Analysis) Next, the control unit 210 causes the analysis unit 350 to perform a time-frequency analysis on the time series data of the blood flow velocity normalized in step S4. The details of step S5 will be described later.

[0175] (S6:Classification) Next, the control unit 210 classifies the subjects into either a healthy subject group or a diseased subject group based on the feature amount obtained by the time-frequency analysis executed in step S5, where the feature amount is the scale value and shift value of the mother wavelet at the feature position of the wavelet transform data.

[0176] This is the end of the operation of the ophthalmologic apparatus 1 (END).

[0177] The process of step S1 in FIG. 13 is performed as shown in FIG.

[0178] (S11: Specify the measurement position) First, the user or the control unit 210 sets a scan range for preliminary measurement. In setting the scan range, for example, an infrared observation image of the fundus Ef acquired in real time or data of the fundus Ef acquired in the past (fundus image, OCT image, SLO image, phase image, etc.) is referred to.

[0179] The control unit 210 controls the OCT unit 100 and the like to perform preliminary measurement on a set scan range (multiple cross sections). As an example, the set scan range is a three-dimensional area, and raster scanning of this three-dimensional area is repeatedly performed. The phase image forming unit 310 forms a phase image for each of the multiple cross sections based on data acquired by the preliminary measurement.

[0180] (S12: Set the cross section near the measurement position) Next, the control unit 210 displays a front image of the fundus oculi Ef. The front image is, for example, an infrared observation image acquired in real time, or an image acquired in the past. The user refers to the displayed information and sets a cross section (cross section of interest C2) that is to be the target of blood flow measurement. For example, the user designates a desired arterial region (blood vessel of interest Db) in this front image by referring to setting support information displayed together with the front image of the fundus oculi Ef.

[0181] In this example, the cross section C2 of interest is set manually, but is not limited to this. For example, the blood vessel Db of interest and the cross section C2 of interest can be set automatically based on the infrared observation image of the fundus Ef and the measurement site information. The blood vessel Db of interest is set, for example, by selecting any one of the arterial regions (for example, the thickest one). In addition, the cross section C2 of interest is set, for example, at a position where the orientation of the blood vessel Db of interest is within a predetermined allowable range, so as to be perpendicular to the running direction of the blood vessel Db of interest.

[0182] Furthermore, the user or the control unit 210 (or the data processing unit 300) sets the cross sections C11 and C12 to be subjected to the first scan.

[0183] (S13: OCT measurement) Next, the control unit 210 executes OCT scans of the cross sections C11 and C12 set in step S12 (first scan). Furthermore, the control unit 210 executes repetitive OCT scans of the cross section of interest C2 set in step S12 (second scan).

[0184] (S14: Forming an image) The control unit 210 causes the tomographic image forming unit 221 to form tomographic images G11 and G12 corresponding to the cross sections C11 and C12 based on the data acquired by the first scan in step S13. The control unit 210 also causes the phase image forming unit 310 to form a phase image of the cross section of interest C2 based on the data acquired by the second scan in step S13. The tomographic image forming unit 221 also forms a tomographic image of the cross section of interest C2 based on the data.

[0185] (S15: Identify vascular area) The control unit 210 specifies the blood vessel of interest Db in the tomographic image formed in step S14. At this time, the control unit 210 specifies, as the blood vessel region, an image region corresponding to the blood vessel of interest Db in a predetermined layer region (for example, a layer region between the internal limiting membrane and Bruch's membrane) specified by the segmentation process by the data processing unit 300.

[0186] (S16: Calculate the inclination of the blood vessel) Next, the control unit 210 causes the gradient calculation unit 322 to calculate the gradient U of the blood vessel of interest Db in the cross section of interest C2 as described above.

[0187] (S17: Calculate blood flow velocity) Next, the control unit 210 causes the blood flow velocity calculation unit 323 to calculate the blood flow velocity in the cross section of interest C2 based on the tilt U calculated based on the first scan in step S13 and the phase image acquired by the second scan in step S13.

[0188] (S18: Calculate blood vessel diameter) Next, the control unit 210 causes the blood vessel diameter calculation unit 324 to calculate the diameter of the blood vessel Db of interest in the cross section C2 of interest.

[0189] (S19: Calculate blood flow) Next, the control unit 210 causes the blood flow calculation unit 325 to calculate the flow rate of blood flowing through the interested blood vessel Db based on the blood flow velocity calculated in step S17 and the blood vessel diameter calculated in step S18.

[0190] The control unit 210 can cause the blood flow information generating unit 320 to generate blood flow information including at least one of the blood flow velocity, blood vessel diameter, and blood flow volume calculated as described above. In this embodiment, the control unit 210 causes the blood flow information generating unit 320 to generate blood flow information including at least the blood flow velocity. Furthermore, the control unit 210 controls the blood flow information analyzing unit 330 to generate blood flow information including time series data of blood flow velocity over a period longer than at least one cardiac cycle, generated by the blood flow information generating unit 320.

[0191] With this, the process of step S1 in FIG. 13 is completed (END).

[0192] The process of step S5 in FIG. 13 is performed as shown in FIG.

[0193] (S21: Wavelet transform) In step S5 of FIG. 13, the control unit 210 causes the wavelet transform unit 351 to perform wavelet transform on the time series data of the blood flow velocity normalized in step S4.

[0194] As described above, the wavelet transform unit 351 performs a wavelet transform on the time series data of the blood flow velocity as the signal to be analyzed x(t) according to equation (6), and generates transformed data W Ψ Find [x(t)].

[0195] (S22: Search for the maximum position) Next, the control unit 210 converts the conversion data W obtained in step S21 into Ψ The feature position search unit 352 searches for the feature position of [x(t)].

[0196] The feature position search unit 352 searches the converted data W Ψ The position where [x(t)] is maximum is searched for as the feature position (for example, feature position CP in FIG. 12).

[0197] (S23: Identify scale and shift values) Next, the control unit 210 causes the feature amount specifying unit 353 to specify, as a feature amount, the scale value and the shift value at the feature position specified in step S22 (for example, the scale value a0 and the shift value b0 in FIG. 12).

[0198] With this, the process of step S5 in FIG. 13 is completed (END).

[0199] Here, an example will be described in which time series data of blood flow velocity is applied to the first embodiment for 36 subjects previously classified by a doctor into a healthy group and 8 subjects previously classified by a doctor into a diseased group. The subjects classified into the diseased group are subjects diagnosed with any of the following diseases: chronic limb-threatening ischemia (CLTI), abdominal aortic aneurysm (AAA), acute aortic dissection, and acute coronary syndrome (ACS).

[0200] 16 shows an example of time series data of blood flow velocity of a subject classified into a healthy subject group and a subject classified into a diseased subject group. In FIG. 16, each time series data is divided into two by the interpolation unit 342 for one pulsation period. 7 The sampled values ​​are interpolated to have the same number of samples, and are normalized in the same manner as in the normalization process performed by the normalization unit 343.

[0201] As shown in FIG. 16, it is very difficult to clearly distinguish the difference in blood flow velocity changes between the healthy and diseased groups.

[0202] Fig. 17 shows an example of distribution of feature amounts at feature positions of wavelet transform data calculated from the time series data of blood flow velocity shown in Fig. 16. In Fig. 17, the horizontal axis represents the shift value, and the vertical axis represents the scale value.

[0203] As shown in FIG. 17, the feature amount calculated by performing a wavelet transform on the time series data of blood flow velocity is included in either the range GR0 or GR1. The range GR0 is the range in which subjects classified into the healthy subject group are distributed. The range GR1 is the range in which subjects classified into the diseased subject group are distributed. For example, the classification unit 360 classifies the subject by determining whether the feature amount calculated by performing a wavelet transform on the time series data of the blood flow velocity of the subject to be analyzed is included in the range GR0 or GR1. This makes it possible to classify the subject to be analyzed into either the healthy subject group or the diseased subject group with high accuracy.

[0204] As described above, the classification unit 360 can classify the subjects being analyzed into either a healthy subject group or a diseased subject group with high accuracy using a regression line or regression curve obtained by regression analysis, or a classification model (trained model) obtained by machine learning.

[0205] The range GR2 where the ranges GR0 and GR1 overlap is a range where subjects who are intermediate between healthy and diseased are distributed. Therefore, by determining whether the subject to be analyzed is included in the range GR2 or not, it is possible to urge the subject, examiner, doctor, etc. to carefully examine the test results of the detailed examination for the subject to be analyzed.

[0206] As described above, according to the first embodiment, the feature amount of the time series data of the blood flow velocity in one pulsation period at the fundus of the subject's eye is obtained by performing a wavelet transform on the time series data of the blood flow velocity. By classifying the subject into one of a plurality of groups including a healthy subject group and a diseased subject group based on the obtained feature amount, the vascular condition of the subject can be evaluated with high accuracy.

[0207] <Second embodiment> In the first embodiment, the case where wavelet transform is performed as time-frequency analysis has been described, but the configuration according to the embodiment is not limited to this. In the second embodiment, multi-resolution analysis is performed as time-frequency analysis.

[0208] Hereinafter, the second embodiment will be described focusing on the differences from the first embodiment.

[0209] The difference in the configuration of the ophthalmic apparatus according to the second embodiment from the configuration of the ophthalmic apparatus 1 according to the first embodiment is that an analysis unit 350a is provided instead of the analysis unit 350.

[0210] (Analysis unit 350a) FIG. 18 shows an example of a functional block diagram of the analysis unit 350a.

[0211] The analysis unit 350a decomposes the time-series data of the blood flow velocity in 2 k (k is a natural number of 2 or more) dimensions at the first depth into an approximation term and a micro term, and repeats the decomposition of the approximation term at the mth (m is a natural number of 2 or more) depth into an approximation term and a micro term at the (m + 1)th depth up to the nth (n is a natural number, n < k) depth. It performs multi-resolution analysis, and calculates the sum of the squares of the 2 k-n terms of the approximation term at the nth depth and the sum of the squares of the 2 k-(n-1) terms of the micro term at the nth depth as feature amounts. Such an analysis unit 350a includes a multi-resolution analysis unit 351a and a feature amount calculation unit 352a.

[0212] (Multi-resolution analysis unit 351a) The multi-resolution analysis unit 351a performs multi-resolution analysis on the time-series data of the blood flow velocity in 2 k dimensions (2 k sampling points) during one pulsation period.

[0213] Here, the time-series data of the blood flow velocity is denoted as f (0) (x), and the approximation term (approximation) which is a scaling function is denoted as f (j)(x) (j is the depth (order)), and the detail coefficient, which is a wavelet function, is g (j) The multi-resolution analysis unit 351a calculates f according to the following equation (7): (0) Decompose (x) (when n=1 in equation (7)).

[0214]

number

[0215] That is, the multi-resolution analysis unit 351a performs f (0) (x) is decomposed as shown in equation (8).

[0216]

number

[0217] In equation (8), the scaling function (father wavelet) and the wavelet function (mother wavelet) are defined as follows: (j) (x) is expressed as equation (9) and equation (10), and g (j) (x) is expressed as equations (11) and (12).

[0218]

number

[0219]

number

[0220] 19A and 19B are diagrams for explaining the operation of the multi-resolution analysis unit 351a. (0)19A shows a schematic diagram of the repeated decomposition of (x)(original) into approximation terms and infinitesimal terms. In addition, in FIG. 19A, the waveform (time series data) reproduced by the approximation terms and infinitesimal terms at each depth is shown. FIG. 19B shows a schematic diagram of the number of approximation terms and the number of infinitesimal terms at each depth.

[0221] As shown in FIG. 19A, the multi-resolution analysis unit 351a analyzes the time series data f (0) (x) with the approximation term f (1) (x) and the infinitesimal term g (1) (x) and (depth = 1, first depth), f (1) (x) with the approximation term f (2) (x) and the infinitesimal term g (2) (x) and (depth=2, second depth), , f (4) (x) with the approximation term f (5) (x) and the infinitesimal term g (5) (x) and (depth = 5, 5th depth). Subsequent decompositions can be done in the same way.

[0222] That is, the multi-resolution analysis unit 351a performs a multi-resolution analysis in which the time series data of blood flow velocity during one pulsation period is decomposed into an approximation term and an infinitesimal term at the first depth, and the approximation term at the mth depth is decomposed into an approximation term and an infinitesimal term at the (m+1)th depth, and the decomposition is repeated up to the nth depth (a specified depth).

[0223] As shown in FIG. 19B, at the first depth, k (e.g., k=7) terms k The original time series data (original) has 2 dimensions k-1 Approximation terms of dimensions and 2 k At the second depth, the two terms at the first depth are decomposed into k-1 The approximation of the dimension is 2 k-2 Approximation terms of dimensions and 2 k-1 At the nth depth, the n-th dimension is decomposed into 2 k-(n-1) The approximation of the dimension is 2 k-n Approximation terms of dimensions and 2 k-(n-1)In other words, the number of dimensions (number of terms) decreases as the depth increases. The relationship between the number of approximate terms and the number of infinitesimal terms is derived from equations (7) and (8).

[0224] In addition, it is possible to determine the number of repetitions (order, depth) of decomposition into approximation terms and infinitesimal terms based on the reproduced waveform at each depth shown in Fig. 19A. Alternatively, it is possible to determine the number of repetitions of decomposition into approximation terms and infinitesimal terms based on the difference between the reproduced waveform at each depth and the reproduced waveform at the immediately previous depth.

[0225] (Feature amount calculation unit 352a) The feature amount calculation unit 352a calculates feature amounts based on the approximation terms and infinitesimal terms decomposed to a predetermined depth by the multi-resolution analysis unit 351a.

[0226] The feature amount calculation unit 352a calculates the feature amount by multiplying the approximation term at the n-th depth obtained by the multi-resolution analysis unit 351a as shown in Equation (13) and Equation (14). k-n Sum of squares of the number of (dimension) terms A n and 2 of the infinitesimal terms at the nth depth k-(n-1) Sum of squares of the number of (dimension) terms D n In formula (13), a q n represents the q-th term of the approximation term at the n-th depth. In equation (14), d q n represents the qth term of the infinitesimal terms at the nth depth, e.g., n=5, k=7.

[0227]

number

[0228] The operation of the ophthalmic apparatus according to the second embodiment is substantially the same as the operation of the ophthalmic apparatus 1 according to the first embodiment. The process of step S5 in FIG.

[0229] Fig. 20 shows a flow diagram of a processing example of step S5 in Fig. 13. The storage unit 212 stores a computer program for realizing the processing shown in Fig. 13, Fig. 14, and Fig. 20. The main control unit 211 operates according to this computer program to execute the processing shown in Fig. 13, Fig. 14, and Fig. 20.

[0230] (S31:Multi-resolution analysis) In step S5 of FIG. 13, the control unit 210 causes the multi-resolution analysis unit 351a to perform a multi-resolution analysis on the time-series data of the blood flow velocity normalized in step S4.

[0231] As described above, the multi-resolution analysis unit 351a performs multi-resolution analysis on the time series data of blood flow velocity according to the formulas (7) to (12). As described above, for example, n=5 and k=7.

[0232] (S32: Calculate the sum of squares of approximate terms and the sum of squares of infinitesimal terms) Next, the control unit 210 causes the feature calculation unit 352a to calculate the feature by using the approximation term and the infinitesimal term obtained in step S31.

[0233] As described above, the feature amount calculation unit 352a calculates the 2nd approximation term at the fifth depth obtained in step S31. 7-5 (=2 2 ) terms and the sum of the squares of the small terms at the fifth depth 7-4 (=2 3 ) terms are calculated as features.

[0234] With this, the process of step S5 in FIG. 13 is completed (END).

[0235] Here, as in the first embodiment, we will explain an example in which time series data of blood flow velocity is applied to the second embodiment for 36 subjects who were previously classified into a healthy group and 8 subjects who were previously classified into a diseased group (see Figure 16).

[0236] Figure 21 shows the feature amount (the 2nd approximation term at the 5th depth) calculated by multi-resolution analysis of the time series data of blood flow velocity shown in Figure 16. 2 2 terms and 5th depth infinitesimal terms 3 In FIG. 21, the horizontal axis shows the distribution of the square sum of the approximation terms at the fifth depth. 2 The vertical axis represents the square sum of the small terms at the fifth depth. 3 Represents the sum of squares of terms.

[0237] As shown in FIG. 21, the feature amount calculated by performing a multi-resolution analysis on the time series data of blood flow velocity is included in either of the ranges GR10 and GR11. The range GR10 is the range in which subjects classified into the healthy subject group are distributed. The range GR11 is the range in which subjects classified into the diseased subject group are distributed. For example, the classification unit 360 classifies the subject by determining whether the feature amount calculated by performing a multi-resolution analysis on the time series data of the blood flow velocity of the subject to be analyzed is included in the ranges GR10 and GR11. This makes it possible to classify the subject to be analyzed into either the healthy subject group or the diseased subject group with high accuracy.

[0238] As described above, the classification unit 360 can classify the subjects being analyzed into either a healthy subject group or a diseased subject group with high accuracy using a regression line or regression curve obtained by regression analysis, or a classification model (trained model) obtained by machine learning.

[0239] In the second embodiment, the learning unit 370 calculates the approximation term at the fifth depth obtained from the time series data of the blood flow velocity of the subject. k-n The sum of squares of the terms and the sum of squares of the small terms at the fifth depth k-(n-1) A classification model that realizes the function of the classification unit 360 is generated by performing known supervised machine learning using a combination of the sums of squares of the terms as training data and the classification results of the training data as teacher data. Here, the classification results are determined in advance by a doctor or the like as to which of the above multiple groups the data falls into.

[0240] The range GR12 where the ranges GR10 and GR11 overlap is a range where subjects who are intermediate between healthy and diseased are distributed. Therefore, by determining whether the subject to be analyzed is included in the range GR12 or not, it is possible to urge the subject, examiner, doctor, etc. to carefully examine the test results of the detailed examination for the subject to be analyzed.

[0241] As described above, according to the second embodiment, by performing multi-resolution analysis on the time series data of the blood flow velocity in one pulsation period at the fundus of the subject's eye, it is possible to obtain feature quantities of the time series data of the blood flow velocity. By classifying the subject into one of a plurality of groups including a healthy subject group and a diseased subject group based on the obtained feature quantities, it becomes possible to evaluate the vascular condition of the subject with high accuracy.

[0242] <Third embodiment> In the above embodiment, the case where wavelet transform or multi-resolution analysis is performed as the time-frequency analysis has been described, but the configuration of the embodiment is not limited to this. The ophthalmologic apparatus according to the third embodiment is configured to classify subjects into either a healthy subject group or a diseased subject group by combining wavelet transform and multi-resolution analysis as the time-frequency analysis.

[0243] The third embodiment will be described below, focusing on the differences from the first or second embodiment.

[0244] The configuration of the ophthalmic apparatus according to the third embodiment differs from the ophthalmic apparatus 1 according to the first embodiment or the ophthalmic apparatus according to the second embodiment in that an analysis unit 350b is provided instead of the analysis unit 350 or the analysis unit 350a.

[0245] (Analysis section 350b) An example of a functional block diagram of the analysis unit 350b is shown in Fig. 22. In Fig. 22, the same parts as those in Fig. 8 or 18 are given the same reference numerals, and the description thereof will be omitted as appropriate.

[0246] The analysis unit 350b includes a first analysis unit 361b and a second analysis unit 362b. The first analysis unit 361b performs a wavelet transform on the time series data of the blood flow velocity, as in the first embodiment, to obtain feature quantities of the time series data of the blood flow velocity. The second analysis unit 362b performs a multi-resolution analysis on the time series data of the blood flow velocity, as in the second embodiment, to obtain feature quantities of the time series data of the blood flow velocity. That is, the first analysis unit 361b includes a wavelet transform unit 351, a feature position search unit 352, and a feature quantity identification unit 353. The second analysis unit 362b includes a multi-resolution analysis unit 351a and a feature quantity calculation unit 352a.

[0247] Specifically, the first analysis unit 361b performs a wavelet transform on the time series data of the blood flow velocity in one pulse period of the subject, and obtains the scale value and shift value of the mother wavelet at the feature position of the acquired wavelet transformed data as feature quantities. The second analysis unit 362b performs a multi-resolution analysis on the time series data of the blood flow velocity that is the analysis target of the first analysis unit 361b, and obtains the 2 of the approximation term at the n-th depth. k-n The sum of squares of the terms and the square of the infinitesimal terms at the nth depth k-(n-1) The sum of squares of the terms is calculated as the feature.

[0248] In the third embodiment, the classification unit 360 classifies the converted data W obtained by the first analysis unit 361b. Ψ The scale value and shift value of the mother wavelet at the feature position of [x(t)] and the 2 of the approximation term at the nth depth obtained by the second analysis unit 362b are k-n The sum of squares of the terms and the square of the infinitesimal terms at the nth depth k-(n-1) Based on the sum of squares of these terms, the subjects are classified into one of a number of groups, including a healthy group and a diseased group.

[0249] In some embodiments, the classification unit 360 classifies a subject into a healthy subject group when the subject is included in the range GR0 based on the feature amount obtained by the first analysis unit 361b and the subject is included in the range GR10 based on the feature amount obtained by the second analysis unit 362b. The classification unit 360 classifies a subject into a diseased subject group when the subject is included in the range GR1 based on the feature amount obtained by the first analysis unit 361b and the subject is included in the range GR11 based on the feature amount obtained by the second analysis unit 362b.

[0250] Furthermore, the classification unit 360 classifies the subject into an intermediate area group when the subject is within range GR1 based on the features obtained by the first analysis unit 361b and within range GR10 based on the features obtained by the second analysis unit 362b, or when the subject is within range GR0 based on the features obtained by the first analysis unit 361b and within range GR11 based on the features obtained by the second analysis unit 362b.

[0251] The learning unit 370 calculates the scale value and the shift value obtained from the time series data of the blood flow velocity of the subject and the approximation term at the n-th depth. k-n The sum of squares of the terms and the 2 of the infinitesimal terms at the nth depth k-(n-1) It is possible to generate a classification model that realizes the function of the classification unit 360 by performing known supervised machine learning using a combination of the sum of squares of the terms as training data and the classification result of the training data as teacher data. Here, the classification result is determined in advance by a doctor or the like as to which of the above multiple groups the item falls into.

[0252] As described above, according to the third embodiment, a plurality of feature quantities of the time series data of the blood flow velocity in one pulsation period at the fundus of the subject's eye are obtained by performing wavelet transform and multi-resolution analysis on the time series data of the blood flow velocity. The obtained plurality of feature quantities are combined to classify the subject into one of a plurality of groups including a healthy subject group and a diseased subject group, so that the vascular condition of the subject can be evaluated with high accuracy.

[0253] <Fourth embodiment> In the first to third embodiments, the case where the time-frequency analysis is performed on the time series data of the blood flow velocity is described, but the configuration of the embodiments is not limited to this. The ophthalmologic apparatus according to the fourth embodiment is configured to classify subjects into either a healthy subject group or a diseased subject group by performing regression analysis on the time series data of the blood flow velocity.

[0254] The fourth embodiment will be described below, focusing on the differences from the first embodiment.

[0255] The configuration of the ophthalmologic apparatus according to the fourth embodiment differs from the ophthalmologic apparatus 1 according to the first embodiment in that a blood flow information analysis unit 330c is provided instead of the blood flow information analysis unit 330.

[0256] (Blood flow information analysis unit 330c) An example of a functional block diagram of the blood flow information analysis unit 330c is shown in Fig. 23. In Fig. 23, the same parts as in Fig. 7 are given the same reference numerals, and the description will be omitted as appropriate.

[0257] The blood flow information analysis unit 330c includes an extraction unit 340, a standardization unit 380c, a classification unit 360c, and a learning unit 370c. The extraction unit 340 includes a cardiac cycle determination unit 341, an interpolation unit 342, and a normalization unit 343. The standardization unit 380c standardizes the time series data of blood flow velocity extracted by the extraction unit 340. The classification unit 360c classifies subjects into one of a plurality of groups including a healthy subject group and a diseased subject group based on the time series data of blood flow velocity standardized by the standardization unit 380c. The learning unit 370c generates a classification model that realizes the function of the classification unit 360c.

[0258] (Standardization Department 380c) The standardization unit 380c converts the distribution of the time series data of the blood flow velocity into a standard normal distribution. If each data constituting the time series data is x, the average value of the time series data is μ, and the standard deviation of the time series data is σ, the standardization unit 380c converts the distribution of the time series data of the blood flow velocity into a standard normal distribution of the standardized variable z converted according to Equation (15).

[0259]

number

[0260] (Classification Department 360c) The classification unit 360c classifies the subject into one of a plurality of groups including a healthy subject group and a diseased subject group, based on the time series data of the blood flow velocity converted into a standard normal distribution by the standardization unit 380c.

[0261] In some embodiments, the plurality of groups includes a healthy subject group, a diseased subject group, and a group in an intermediate region between the healthy subject and the diseased subject. In this case, the classification unit 360c classifies the subject into any of the healthy subject group, the diseased subject group, and the intermediate region group based on the scale value and the shift value.

[0262] In some embodiments, the classification unit 360c classifies the subject into either a healthy subject group or a diseased subject group based on the time series data of blood flow velocity converted into a standard normal distribution by the standardization unit 380c.

[0263] In some embodiments, the classifier 360c classifies subjects into an intermediate group and other groups based on the time series data of blood flow velocity converted to a standard normal distribution by the standardizer 380c.

[0264] In some embodiments, the classification unit 360c classifies the subjects as described above using a classification model obtained by analyzing the time series data of blood flow velocities of multiple subjects in advance. A known analysis method such as machine learning, such as a linear support vector machine, a nonlinear support vector machine, a decision tree method, a random forest method, or a k-nearest neighbor method, is used for the advance analysis of the time series data of blood flow velocities of multiple subjects. In this case, the classification unit 360c can classify the subjects as described above using a classification model (trained model) obtained by machine learning.

[0265] (Learning section 370c) The learning unit 370c generates a classification model used by the classification unit 360c to classify subjects. The learning unit 370c generates the classification model by performing a known supervised machine learning method using time series data of the blood flow velocity of the subject as training data and the classification result of the training data as teacher data. The classification result is determined in advance by a doctor or the like as to which of the above multiple groups the subject is classified into.

[0266] The above-mentioned functions of the blood flow information analysis unit 330c are realized by one or more processors. In some embodiments, the functions of the blood flow information analysis unit 330c are realized by a plurality of processors that execute the functions of each unit.

[0267] As described above, according to the fourth embodiment, the subject is classified into one of a plurality of groups including a healthy subject group and a diseased subject group by machine learning for time series data of blood flow velocity in one pulsation period at the fundus of the subject's eye. In particular, even if the effect of machine learning for the classification unit 360c is reduced due to differences in distribution of absolute values ​​between different explanatory variables (inputs), the distribution of absolute values ​​between the explanatory variables can be made uniform, thereby improving the accuracy of machine learning for the classification unit 360c.

[0268] <Modification> In the above embodiment, the mother wavelet shown in formula (4) is used as an example, but the configuration according to the embodiment is not limited to this. For example, in the above embodiment, any of the mother wavelets shown in formulas (16) to (22) may be used.

[0269] Equation (16) represents an example of a Mexican hat mother wavelet (mexh) that is different from equation (4).

[0270]

number

[0271] Equation (17) represents an example of the Morlet mother wavelet (morl).

[0272]

number

[0273] Equation (18) represents an example of a Complex Morlet mother wavelet (cmorB-C), where B represents the bandwidth and C represents the center frequency.

[0274]

number

[0275] Equation (19) represents an example of the Gaussian Derivative mother wavelet (gausP). In equation (19), C represents a constant.

[0276]

number

[0277] Equation (20) represents an example of the complex Gaussian derivative mother wavelet (cgausP). In equation (20), C represents a constant.

[0278]

number

[0279] Equation (21) represents an example of a Shannon wavelet (shanB-C), where B represents the bandwidth and C represents the center frequency.

[0280]

number

[0281] Equation (22) represents an example of a Frequency B-Spline wavelet (fbsp): In equation (22), M represents the order of the spline, B represents the bandwidth, and C represents the center frequency.

[0282]

number

[0283] The ophthalmologic apparatus according to the third embodiment may be configured to classify subjects into the above-mentioned multiple groups based on two or more feature amounts obtained by wavelet transform to which two or more different mother wavelets are applied, or may be configured to classify subjects into the above-mentioned multiple groups based on two or more feature amounts obtained by wavelet transform to which two or more different mother wavelets are applied and feature amounts obtained by multi-resolution analysis.

[0284] [Effect] A blood flow analysis apparatus, an ophthalmic apparatus, a blood flow analysis method, and a program according to an embodiment will be described.

[0285] A blood flow analysis device (blood flow information analysis unit 330, ophthalmologic apparatus 1) according to some embodiments includes an analysis unit (350, 350a) and a classification unit (360). The analysis unit performs time-frequency analysis on time-series data of blood flow velocity in the fundus (Ef) of the subject. The classification unit classifies the subject into one of a plurality of groups including a healthy subject group and a diseased subject group based on the feature amount of the analysis data obtained by the analysis unit.

[0286] According to this aspect, features are obtained by performing time-frequency analysis on the time-series data of blood flow velocity, and the subjects are classified based on the obtained features, making it possible to correctly evaluate the vascular condition of the subjects with high accuracy.

[0287] In some embodiments, the analysis unit performs a wavelet transform on the time series data for one beat period, and the classification unit classifies the subject into one of a plurality of groups based on the scale value and shift value of the mother wavelet at the characteristic position (CP) of the wavelet transformed data obtained by the analysis unit.

[0288] According to this aspect, the scale value and shift value of the mother wavelet at the characteristic position of the wavelet transformed data obtained by the wavelet transform are obtained as the characteristic quantities, so that it is possible to easily obtain the characteristic quantities that are useful for highly accurate evaluation of the vascular condition.

[0289] In some embodiments, the analysis unit performs multi-resolution analysis in which the time series data for one beat period is decomposed into approximate terms and infinitesimal terms at a first depth, and the decomposition of the approximate terms at the mth depth (m is a natural number greater than or equal to 2) into approximate terms and infinitesimal terms at the (m+1)th depth is repeated up to the nth depth (n is a natural number), and the classification unit classifies the subject into one of a plurality of groups based on the sum of squares of the approximate terms at the nth depth and the sum of squares of the infinitesimal terms at the nth depth obtained by the analysis unit.

[0290] According to such an aspect, the sum of squares of the approximation terms at the n-th depth obtained by multi-resolution analysis (for example, the sum of squares of 2 k-n terms of the approximation terms, n < k, where k is a natural number of 2 or more) and the sum of squares of the fine terms at the n-th depth (for example, the sum of squares of 2 k-(n-1) terms of the fine terms) are obtained as feature amounts, so that it becomes possible to easily acquire feature amounts useful for highly accurate evaluation of the vascular state.

[0291] In some embodiments, the analysis unit includes a first analysis unit (361b) that performs wavelet transform on the time-series data in one heartbeat period, and decomposes the time-series data in one heartbeat period into an approximation term and a fine term at the first depth, and further decomposes the approximation term at the m-th (where m is a natural number of 2 or more) depth into an approximation term and a fine term at the (m + 1)-th depth, and repeats this up to the n-th (where n is a natural number) depth by performing multi-resolution analysis. The classification unit classifies the subject into one of a plurality of groups based on the scale value and shift value of the mother wavelet at the characteristic position of the wavelet transform data obtained by the first analysis unit, and the sum of squares of the approximation terms at the n-th depth and the sum of squares of the fine terms at the n-th depth obtained by the second analysis unit.

[0292] According to such an aspect, the scale value and shift value of the mother wavelet at the characteristic position of the wavelet transform data obtained by wavelet transform, and the sum of squares of the approximation terms at the n-th depth obtained by multi-resolution analysis (for example, the sum of squares of 2 k-n terms of the approximation terms, n < k, where k is a natural number of 2 or more) and the sum of squares of the fine terms at the n-th depth (for example, the sum of squares of 2 k-(n-1) terms of the fine terms) are obtained as feature amounts, so that it becomes possible to easily acquire feature amounts useful for highly accurate evaluation of the vascular state.

[0293] In some embodiments, the characteristic position is the position where the wavelet transform data is maximum.

[0294] According to this aspect, it becomes easier to search for the feature position, and it becomes possible to obtain the feature amount more simply.

[0295] In some embodiments, the blood flow velocity is obtained based on a Doppler signal due to blood flow obtained by performing Doppler OCT on the fundus.

[0296] According to this embodiment, it becomes possible to evaluate the state of blood vessels in the examinee's eye with high accuracy using an existing ophthalmic device capable of measuring blood flow velocity by OCT.

[0297] Some embodiments are an ophthalmologic device that includes an optical system (the optical system from the objective lens 22 to the OCT unit 100 in FIG. 1) that performs OCT at different timings on measurement sites on the fundus, a blood flow information generating unit (320) that calculates blood flow velocity at the fundus based on multiple signals obtained by the optical system, and the above-mentioned blood flow analysis device.

[0298] According to this aspect, it is possible to provide an ophthalmologic apparatus capable of correctly and highly accurately evaluating the blood vessel condition of a subject.

[0299] Some embodiments are a blood flow analysis method including an analysis step of performing a time-frequency analysis on time series data of blood flow velocity in the fundus (Ef) of a subject, and a classification step of classifying the subject into one of a plurality of groups including a healthy subject group and a diseased subject group based on features of the analysis data obtained in the analysis step.

[0300] According to this aspect, features are obtained by performing time-frequency analysis on the time-series data of blood flow velocity, and the subjects are classified based on the obtained features, making it possible to correctly evaluate the vascular condition of the subjects with high accuracy.

[0301] In some embodiments, the analysis step performs a wavelet transform on the time-series data in one cardiac cycle, and the classification step classifies the subject into one of a plurality of groups based on the scale value and the shift value of the mother wavelet at the characteristic position of the wavelet transform data obtained in the analysis step.

[0302] According to such an aspect, since the scale value and the shift value of the mother wavelet at the characteristic position of the wavelet transform data obtained by the wavelet transform are obtained as feature amounts, it is possible to easily acquire feature amounts useful for highly accurate evaluation of the vascular state.

[0303] In some embodiments, the analysis step performs a multi-resolution analysis in which the time-series data in one cardiac cycle is decomposed into an approximation term and a detail term at a first depth, and the approximation term at the m-th (where m is a natural number of 2 or more) depth is decomposed into an approximation term and a detail term at the (m + 1)-th depth, which is repeated up to the n-th (where n is a natural number) depth, and the classification step classifies the subject into one of a plurality of groups based on the sum of squares of the approximation terms at the n-th depth and the sum of squares of the detail terms at the n-th depth obtained in the analysis step.

[0304] According to such an aspect, since the sum of squares of the approximation terms at the n-th depth (for example, the sum of squares of 2 k-n terms of the approximation terms, n < k, where k is a natural number of 2 or more) and the sum of squares of the detail terms at the n-th depth (for example, the sum of squares of 2 k-(n-1) terms of the detail terms) obtained by the multi-resolution analysis are obtained as feature amounts, it is possible to easily acquire feature amounts useful for highly accurate evaluation of the vascular state.

[0305] In some embodiments, the analysis step includes a first analysis step of performing a wavelet transform on the time series data in one cardiac cycle, and a second analysis step of decomposing the time series data in one cardiac cycle into an approximation term and a detail term at a first depth, and repeatedly decomposing the approximation term at the m-th (where m is a natural number of 2 or more) depth into an approximation term and a detail term at the (m + 1)-th depth until the n-th (where n is a natural number) depth. The classification step classifies the subject into one of a plurality of groups based on the scale value and shift value of the mother wavelet at the characteristic position of the wavelet transform data obtained in the first analysis step, and the sum of squares of the approximation terms and the sum of squares of the detail terms at the n-th depth obtained in the second analysis step.

[0306] According to such an aspect, since the scale value and shift value of the mother wavelet at the characteristic position of the wavelet transform data obtained by wavelet transform, and the sum of squares of the approximation terms (for example, the sum of squares of 2 k-n terms of the approximation terms, n < k, k is a natural number of 2 or more) and the sum of squares of the detail terms (for example, the sum of squares of 2 k-(n-1) terms of the detail terms) at the n-th depth obtained by multi-resolution analysis are obtained as feature amounts, it becomes possible to simply acquire feature amounts useful for highly accurate evaluation of the vascular state.

[0307] In some embodiments, the characteristic position is the position where the wavelet transform data is maximum.

[0308] According to such an aspect, the search for the characteristic position becomes easy, and it becomes possible to obtain the feature amount more simply.

[0309] In some embodiments, the blood flow velocity is acquired based on the Doppler signal due to the blood flow obtained by performing Doppler OCT on the fundus.

[0310] According to this embodiment, it becomes possible to evaluate the state of blood vessels in the examinee's eye with high accuracy using an existing ophthalmic device capable of measuring blood flow velocity by OCT.

[0311] Some embodiments are programs for causing a computer to execute each step of any of the blood flow analysis methods described above.

[0312] According to this aspect, features are obtained by performing time-frequency analysis on time-series data of blood flow velocity, and subjects are classified based on the obtained features, making it possible to provide a program that can correctly and accurately evaluate the vascular condition of a subject.

[0313] It is possible to create a computer-readable non-transitory recording medium having a program according to any of the embodiments recorded thereon. The non-transitory recording medium may be in any form, including, for example, a magnetic disk, an optical disk, a magneto-optical disk, and a semiconductor memory.

[0314] The embodiment described above is merely one example of the present invention. Those who wish to implement the present invention may make any modifications (omissions, substitutions, additions, etc.) within the scope of the gist of the present invention. [Explanation of symbols]

[0315] 1 Ophthalmology equipment 100 OCT units 210 Control section 211 Main control unit 300 Data Processing Unit 310 Phase image formation unit 320 Blood flow information generation section 330, 330c Blood flow information analysis department 340 Extraction part 350, 350a, 350b analysis section 351 Wavelet Transformation Section 351a Multi-resolution analysis section 352 Feature Location Search Unit 352a Feature Calculation Unit 353 Feature quantity specifying unit 360, 360c Classification unit 361b First analysis unit 362b Second analysis unit 370, 370c Learning unit 380c Standardization unit E Eye to be examined Ef Fundus

Claims

1. an analysis unit that performs a time-frequency analysis on time series data of blood flow velocity in the fundus of the subject; a classification unit that classifies the subject into one of a plurality of groups including a healthy subject group and a diseased subject group based on a feature amount of the analysis data obtained by the analysis unit; Including, The analysis unit performs a time-frequency analysis on the time series data normalized in the time axis direction and the blood flow velocity axis direction.

2. The analysis unit performs a wavelet transform on the time series data for one heartbeat period, The classification unit classifies the subject into one of the plurality of groups based on a scale value and a shift value of a mother wavelet at a characteristic position of the wavelet transformation data obtained by the analysis unit.

2. The blood flow analysis apparatus according to claim 1 .

3. the analysis unit performs a multi-resolution analysis in which the time series data in one pulsation period is decomposed into an approximation term and an infinitesimal term at a first depth, and the approximation term at an m-th depth (m is a natural number of 2 or more) is decomposed into an approximation term and an infinitesimal term at an (m+1)-th depth, and the decomposition is repeated up to an n-th depth (n is a natural number); The classification unit classifies the subject into one of the plurality of groups based on the sum of squares of the approximation terms at the n depth and the sum of squares of the infinitesimal terms at the n depth obtained by the analysis unit.

2. The blood flow analysis apparatus according to claim 1 .

4. The analysis unit is A first analysis unit that performs a wavelet transform on the time series data in one heartbeat period; a second analysis unit that performs a multi-resolution analysis by decomposing the time series data in one pulsation period into an approximation term and an infinitesimal term at a first depth, and repeating the decomposition of the approximation term at an m-th depth (m is a natural number of 2 or more) into an approximation term and an infinitesimal term at an (m+1)-th depth up to an n-th depth (n is a natural number); Including, The classification unit classifies the subject into one of the plurality of groups based on a scale value and a shift value of a mother wavelet at a feature position of the wavelet transform data obtained by the first analysis unit, and a sum of squares of an approximation term at the nth depth and a sum of squares of an infinitesimal term at the nth depth obtained by the second analysis unit.

2. The blood flow analysis apparatus according to claim 1 .

5. The characteristic position is the position where the wavelet transform data is maximum.

5. The blood flow analysis apparatus according to claim 2 or 4.

6. The blood flow velocity is acquired based on a Doppler signal due to blood flow obtained by performing Doppler OCT on the fundus. The blood flow analysis device according to any one of claims 1 to 5.

7. an optical system that performs OCT on a measurement site on the fundus at different timings; a blood flow information generating unit that calculates a blood flow velocity at the fundus based on a plurality of signals obtained by the optical system; The blood flow analysis device according to claim 6 , 13. An ophthalmic device comprising:

8. An analysis step in which one or more processors perform a time-frequency analysis on time series data of blood flow velocity in the fundus of the subject; a classification step in which the one or more processors classify the subject into one of a plurality of groups including a healthy subject group and a diseased subject group based on a feature amount of the analysis data obtained in the analysis step; Including, A method for operating a blood flow analysis apparatus, wherein the analysis step comprises the one or more processors performing a time-frequency analysis on the time series data normalized in the time axis direction and the blood flow velocity axis direction.

9. The analyzing step performs a wavelet transform on the time series data for one heartbeat period, The classification step classifies the subject into one of the plurality of groups based on a scale value and a shift value of a mother wavelet at a feature position of the wavelet transform data obtained in the analysis step.

9. A method for operating a blood flow analysis apparatus according to claim 8.

10. The analysis step includes performing a multi-resolution analysis in which the time series data in one pulsation period is decomposed into an approximation term and an infinitesimal term at a first depth, and the approximation term at an m-th depth (m is a natural number of 2 or more) is decomposed into an approximation term and an infinitesimal term at an (m+1)-th depth, and the decomposition is repeated up to an n-th depth (n is a natural number); The classification step classifies the subject into one of the plurality of groups based on the sum of squares of the approximation terms at the nth depth and the sum of squares of the infinitesimal terms at the nth depth obtained in the analysis step.

9. A method for operating a blood flow analysis apparatus according to claim 8.

11. The analyzing step includes: a first analysis step of performing a wavelet transform on the time series data in one heartbeat period; a second analysis step of performing a multi-resolution analysis in which the time series data in one pulsation period is decomposed into an approximation term and an infinitesimal term at a first depth, and the approximation term at an m-th depth (m is a natural number of 2 or more) is decomposed into an approximation term and an infinitesimal term at an (m+1)-th depth, and the decomposition is repeated up to an n-th depth (n is a natural number); Including, The classification step classifies the subject into one of the plurality of groups based on a scale value and a shift value of a mother wavelet at a feature position of the wavelet transform data obtained in the first analysis step, and a sum of squares of an approximation term at the nth depth and a sum of squares of an infinitesimal term at the nth depth obtained in the second analysis step.

9. A method for operating a blood flow analysis apparatus according to claim 8.

12. The characteristic position is the position where the wavelet transform data is maximum. A method for operating the blood flow analysis apparatus according to claim 9 or 11.

13. The blood flow velocity is acquired based on a Doppler signal due to blood flow obtained by performing Doppler OCT on the fundus. A method for operating the blood flow analysis apparatus according to any one of claims 8 to 12.

14. A program for causing a computer to execute each step of the method for operating a blood flow analysis apparatus according to any one of claims 8 to 13.

Images