JP2019150345A - Image processing system, image processing method and program - Google Patents

Abstract

To support a user for determining whether a prescribed blood vessel is an artery or vein based on a motion contrast image.SOLUTION: An image processing system includes: acquisition means for acquiring a motion contrast image of an ocular fundus; and determination means for determining whether a blood vessel is an artery or vein based on a motion contrast image of an evaluation region that is a region positioned within a prescribed distance from a region equivalent to a blood vessel in the motion contrast image.SELECTED DRAWING: Figure 7

Description

  The present disclosure relates to an image processing apparatus, an image processing method, and a program.

  Patent Document 1 discloses a method for determining whether a predetermined blood vessel is an artery or a vein from a tomographic image.

Japanese Patent Application No. 2015-207572

  However, a technique for determining whether a predetermined blood vessel is an artery or a vein from a motion contrast image obtained by OCT Angiography (OCTA) has not been known.

  Disclosure of the present specification has been made in view of the above problems, and an object thereof is to support the determination of whether a predetermined blood vessel is an artery or a vein based on a motion contrast image.

  It is to be noted that the present invention is not limited to the above-described object, and is an operational effect derived from each configuration shown in an embodiment for carrying out the invention described later, and also has an operational effect that cannot be obtained by conventional techniques. It can be positioned as one of other purposes.

  The image processing apparatus disclosed in the present specification is based on an acquisition unit that acquires a motion contrast image of the fundus and a motion contrast value of an evaluation area that is an area within a predetermined distance from an area corresponding to a blood vessel in the motion contrast image. Determining means for determining whether the blood vessel is an artery or a vein.

  It is possible to assist in determining whether a predetermined blood vessel is an artery or a vein based on the motion contrast image.

It is a figure which shows an example of an apparatus structure. It is a figure which shows an example of the scan at the time of OCTA imaging. It is a figure for demonstrating an example of a motion contrast image. It is a figure which shows an example of the acquisition procedure of a motion contrast image. It is a figure which shows an example of the superimposition procedure of a motion contrast image. It is a figure for demonstrating an example of the determination method of an artery and a vein. It is a figure which shows an example of the determination procedure of an artery and a vein. It is a figure which shows an example of a FAZ detection procedure. It is a figure for demonstrating an example of the blood vessel classification. It is a figure for demonstrating an example of the determination method of an artery and a vein. It is a figure which shows an example of the determination procedure of an artery and a vein.

  An apparatus configuration for acquiring an OCTA image will be described. Next, the entire method for acquiring the MC image from the OCT information will be described, and the detailed flow will be sequentially described.

[Hardware configuration]
FIG. 1A is a diagram illustrating an example of a device configuration. A light source 001 is an SLD light source, and a coupler 002 branches light into measurement light and reference light under a desired branching ratio. The measurement light passes through the coupler 002 and is emitted from the collimator 021 to the sample optical system 102. The sample optical system 102 includes a focus lens 022, an X galvanometric mirror 023 and a Y galvanometric mirror 024 that are variable in angle, a lens 025 that forms an objective lens system, and a lens 026. A beam spot by measuring light is formed on the fundus of the eye. Here, the beam spot guided onto the fundus is scanned two-dimensionally on the fundus by driving the X galvanometric mirror 023 and the Y galvanometric mirror 024. The measurement light reflected and scattered by the fundus of the subject eye 027 is guided to the coupler 002 after passing through the sample optical system 102. The reference light is guided to the reference optical system 103, becomes collimated light by the collimator lens 031, passes through the ND filter 032 and is attenuated to a predetermined light amount. Thereafter, the reference light is reflected by the mirror 033 which can move in the optical axis direction and can correct the optical path length difference with the sample optical system 102 while maintaining the collimated state, and is folded back to the same optical path. The folded reference light is guided to the coupler 002 after passing through the ND filter 032 and the collimator lens 031.

  The measurement light and the reference light that have returned to the coupler 002 are combined by the coupler 002 and guided to the detection system (or spectroscope) 104. The combined light is emitted from the collimator 042, dispersed by the diffraction grating 043, received by the line sensor 045 through the lens 044, and output as an interference signal. The line sensor 045 is disposed so that each pixel receives light corresponding to the wavelength component of light dispersed by the diffraction grating 043.

  FIG. 1B is a diagram illustrating an example of an apparatus configuration including an image processing apparatus. In the OCT optical system, a focus driving unit 061 for moving the focus lens 022, a galvanometric mirror driving unit 062 for driving the X galvanometric mirror 023 and the Y galvanometric mirror 024, and the mirror 033 in the optical axis direction. Mirror drive means 063 for moving each is provided. Each drive unit, light source 001, line sensor 045, sampling unit 051, memory 052, signal processing unit 053, operation input unit 056, monitor 055 and the like are connected to the control unit 054 to control the movement of the entire apparatus. Here, the image processing apparatus includes a signal processing unit 053 and a control unit 054. Note that the image processing apparatus may include at least one of the sampling unit 051, the memory 052, the monitor 055, and the operation input unit 056. The signal processing unit 053 and the control unit 054 are realized by a processor such as a CPU provided in the image processing apparatus executing a program stored in the memory. Note that a processor such as a CPU may function as the sampling unit 051 by executing a program stored in a memory.

  The output signal from the line sensor 045 is output as an interference signal by the sampling unit 051 for any galvanometric mirror driving position driven by the galvanometric mirror driving means 062. Subsequently, the galvanometric mirror driving means 062 offsets the galvanometric mirror driving position and outputs an interference signal at that position. Thereafter, interference signals are generated one after another by repeating this process. The interference signal generated by the sampling unit 051 is stored in the memory 052 together with the galvanometric mirror driving position. The interference signal stored in the memory 052 is frequency-analyzed by the signal processing unit 053, becomes a cross-sectional image of the fundus of the eye 027, and is displayed on the monitor 055. A three-dimensional fundus volume image can be generated by the galvanometric mirror drive position information and displayed on the monitor 055.

  The control unit 054 acquires background data at an arbitrary timing during shooting. The background data refers to a signal in a state where the measurement light is not incident on the subject, that is, a signal of only the reference light. For example, the galvanometric mirror metric mirror is driven by the galvanometric mirror driving means 062, and the measurement light from the sample optical system The background data is acquired by performing signal acquisition in a state in which the position of the measurement light is adjusted so as not to return.

[Scan example]
Next, an example of the scan pattern of this embodiment will be described with reference to FIG. In addition, the numerical value illustrated below is an example and can be changed into another numerical value. 2A is a diagram showing a scan pattern for an arbitrary scan, and FIG. 2B is a diagram showing a scan pattern reflecting a numerical value specifically executed in this embodiment. In OCTA, since the time change of the OCT interference signal due to blood flow is measured, multiple measurements are required at the same place. In the present embodiment, the OCT apparatus performs a scan that moves to n y positions while repeating the B scan at the same location m times. A specific scan pattern is shown in FIG. B scans are repeated m times for n positions y1 to yn on the fundus plane. If m is large, the number of times of measurement at the same place increases, so that blood flow detection accuracy is improved. On the other hand, the scan time becomes long, and there arises a problem that motion artifacts are generated in the image due to eye movement (fixed eye movement) during scanning and a burden on the subject increases. In the present embodiment, m = 4 (FIG. 2B) was implemented in consideration of the balance between the two.

  Note that the repetition number m may be changed according to the A-scan speed of the OCT apparatus and the motion analysis of the fundus surface image of the subject 027. In FIG. 2, p indicates the number of samplings of A scan in one B scan. That is, the plane image size is determined by p × n. When p × n is large, a wide range can be scanned at the same measurement pitch, but the scan time becomes long, and the above-mentioned motion artifact and patient burden problem occur. In the present embodiment, n = p = 300 was implemented in consideration of the balance between the two. The above n and p can be freely changed as appropriate. In FIG. 2A, Δx is an interval between adjacent x positions (x pitch), and Δy is an interval between adjacent y positions (y pitch). In this embodiment, the x pitch and the y pitch are determined as ½ of the beam spot diameter of the irradiation light on the fundus, and in this embodiment, 10 μm (FIG. 2B). An image to be generated can be formed with high definition by setting the x pitch and the y pitch to ½ of the beam spot diameter on the fundus. Even if the x pitch and the y pitch are smaller than ½ of the fundus beam spot diameter, the effect of further increasing the definition of the generated image is small. Conversely, if the x pitch and y pitch are made larger than ½ of the fundus beam spot diameter, the definition deteriorates, but a wide range of images can be acquired with a small data capacity. The x pitch and y pitch may be freely changed according to clinical requirements. The scan range of the present embodiment is p × Δx = 3 mm in the x direction and n × Δy = 3 mm in the y direction (see FIG. 2B).

[OCTA image acquisition]
Next, a method for generating the OCTA image 301 will be described with reference to FIG. In step S401, the signal processing unit 053 may extract the repeated B-scan interfering signal at the position _{y k} (m sheets). In step S402, the signal processing unit 053 extracts the j-th tomographic data. In step S403, the signal processing unit 053 subtracts the acquired background data from the interference signal. In step S404, the signal processing unit 053 performs a Fourier transform on the interference signal obtained by subtracting the background by performing a conversion process on the wave number function. In this embodiment, a fast Fourier transform (FFT) is applied. Note that zero padding processing may be performed before Fourier transform to increase the interference signal. By performing the zero padding process, the gradation after Fourier transform is increased, and the alignment accuracy can be improved in step 409 described later. In step S405, the signal processing unit 053 calculates the absolute value of the complex signal obtained by the Fourier transform executed in step S404. This value becomes the intensity of the tomographic image of the scan. In step S406, the signal processing unit 053 determines whether the index j has reached a predetermined number (m). That is, it is determined whether the intensity calculation of the tomographic image at the position y _k has been repeated m times. If the predetermined number is not reached, the process returns to S402, and the intensity calculation of the tomographic image at the same Y position is repeated. When the predetermined number is reached, the process proceeds to the next step.

In step S407, the signal processing unit 053 calculates image similarity in m frames of the same tomographic image at a certain y _k position. Specifically, the signal processing unit 053 selects any one of the m-frame tomographic images as a template, and calculates a correlation value with the remaining m−1 frame images. The correlation value calculation method may be another method. In step S408, the signal processing unit 053 selects a highly correlated image that is equal to or greater than a certain threshold value from the correlation values calculated in step S407. The threshold value can be arbitrarily set, and is set so as to exclude frames in which the correlation as an image has decreased due to blinking of the subject or slight eye movement. As described above, OCTA is a technique for distinguishing a contrast between a flowable tissue (for example, blood) and a nonflowable tissue among subject tissues based on a correlation value between images. In other words, tissue with no flow is extracted on the premise that there is a high correlation between images, so if the correlation is low as an image, it will be erroneously detected when calculating MC (motion contrast), as if the image It is judged as if the whole is a flowing organization. In this step, in order to avoid such erroneous detection, tomographic images with low correlation are excluded in advance as images, and only images with high correlation are selected. As a result of image selection, images of m frames acquired at the same position y _k are appropriately selected and become q frame images. Here, a possible value of q is 1 ≦ q ≦ m.

  In step S409, the signal processing unit 053 performs alignment of the tomographic image of the q frame selected in step S408. For the frames to be selected as the alignment template, correlation may be calculated for all combinations, a sum of correlation coefficients may be obtained for each frame, and a frame having the maximum sum may be selected. Next, the position deviation amounts (δX, δY, δθ) are obtained by matching each frame with a template. Specifically, Normalized Cross-Correlation (NCC), which is an index representing the degree of similarity while changing the position and angle of the template image, is calculated, and the difference between the image positions when this value is maximized is obtained as the amount of positional deviation. In the present invention, the index representing the degree of similarity can be changed in various ways as long as it is a scale representing the similarity between the template and the image feature in the frame. For example, Sum of Absence Difference (SAD), Sum of Squared Difference (SSD), Zero-means Normalized Cross-Correlation (ZNCC), Pr, and Phase Only Correlation (POC), R

  Next, the signal processing unit 053 applies position correction to (q−1) frames other than the template in accordance with the positional deviation amounts (δX, δY, δθ), and performs frame alignment. If q is 1, this step is not executed.

In step S410, the signal processing unit 053 calculates MC. MC is a value indicating the decorrelation of luminance between tomographic images, for example. In the present embodiment, a variance value is calculated for each pixel at the same position between q-frame Intensity images selected in step S408 and aligned in step S409, and the variance value is defined as MC. There are various ways of obtaining MC, and in the present invention, MC can be applied as long as it is an index representing a change in luminance value of each pixel of a plurality of tomographic images at the same Y position. When q = 1, that is, when the correlation is low as an image due to the effects of blinking or fixation micromotion, and MC cannot be calculated at the same position y _k , different processing is performed. For example, the feature may be set to 0 and the step may be terminated, or when MCs in images of y _k −1 and y _k +1 before and after are obtained, values may be interpolated from the previous and next variance values. In this case, the abnormality may be notified that the feature quantity that could not be calculated correctly is a complementary value. Alternatively, the Y position where the feature amount could not be calculated may be stored, and rescanning may be performed automatically. Alternatively, a warning prompting remeasurement may be issued without performing automatic rescanning.

  In step S411, the signal processing unit 053 averages the intensity image subjected to the alignment in step S409, and generates an intensity averaged image.

  In step S412, the signal processing unit 053 performs threshold processing for the MC output in step S410. The threshold value is calculated by extracting an area where only random noise is displayed on the noise floor from the intensity averaged image output by the signal processing unit 053 in step S411, calculating the standard deviation σ, and calculating the average luminance of the noise floor (intensity). ) Set the value + 2σ. The signal processing unit 053 sets the MC value corresponding to the region where each intensity is equal to or less than the threshold value to 0. By this threshold processing, noise can be reduced by removing MC derived from random noise. As the threshold value is smaller, the MC detection sensitivity increases, but the noise component also increases. Also, the larger the noise, the less noise, but the MC detection sensitivity decreases. In this embodiment, the threshold value is set as the average luminance value (intensity) value of the noise floor + 2σ, but the threshold value is not limited to this.

  In step S413, the signal processing unit 053 determines whether the index k has reached a predetermined number (n). That is, it is determined whether image correlation calculation, image selection, alignment, intensity image averaging calculation, MC calculation, and threshold processing have been performed at all n Y positions. When the predetermined number is not reached, the process returns to S401, and when the predetermined number is reached, the process proceeds to the next step S414. When step S413 is completed, the intensity average image and the MC three-dimensional volume data (three-dimensional OCTA data) in the tomographic images at all the Y positions are generated. A three-dimensional MC image is an example of MC three-dimensional volume data. In step S414, the signal processing unit 053 generates an MC front image (MC EnFace image) integrated in the depth direction with respect to the generated three-dimensional OCTA data. That is, the signal processing unit 053 corresponds to an example of an acquisition unit that acquires a fundus motion contrast image. FIG. 3A is an example of the MC front image. At this time, when generating the MC front image, the image depth range to be integrated may be arbitrarily set. For example, the signal processing unit 053 extracts a layer boundary of the fundus retina based on the intensity averaged image generated in step S411, and generates an MC front image so as to include a desired layer. After generating the MC front image, the signal processing unit 053 ends the signal processing flow. By using the apparatus configuration, imaging method, and signal processing procedure described above, an MC image (OCTA image) can be acquired in a desired region. The MC image includes a three-dimensional MC image and a two-dimensional MC image such as an EnFace image. In this embodiment, the OCTA image is acquired under the condition of m = 4.

  As shown in FIG. 3B, the depth range to be integrated with respect to the three-dimensional OCTA data obtained by imaging the macula is limited to several layers on the retina surface layer side (from the inner boundary film 302 to the INL central portion 303). An MC front image of the surface layer of the retina (Superficial Capillary) such as 3 (a) is obtained. The signal processing unit 053 can extract the fundus blood vessel 301 at the center of the macula from the MC front image. The depth range for generating the MC front image is not limited to the above example.

[MC image overlay image acquisition]
Next, a method for acquiring a plurality of MC images will be described with reference to FIG. In step S501, the control unit 054 displays a GUI for prompting the user to input the MC acquisition number F on the monitor 055. In step S502, the user inputs F (F is an integer equal to or greater than 1). Note that the MC acquisition number F may be a predetermined value regardless of user input. In step S503, the control unit 054 controls the OCT optical system to acquire the MC image acquisition number i sequentially from one, and acquires MC images up to F designated by the user as shown in steps S504, 505, and 506. . In step S505, the process of FIG. 4 is executed. The MC image generation may be executed in step S505, may be performed after the scan for the MC acquisition number F is completed, or may be performed in parallel with the scan for the MC acquisition number F. It is good.

  After obtaining the desired number of MC images, in step S507, the signal processing unit 053 stores data (data of a plurality of MC images) in the memory 052. In step S508, the signal processing unit 053 uses the plurality of stored MC images, acquires an MC superimposed image, and stores it in the memory 052 as other data (step S509). In this embodiment, ten MC images are acquired. By this processing, a plurality of MC superimposed images can be acquired. The MC image to be superimposed may be a three-dimensional MC image or a two-dimensional MC image (MC EnFace image). In addition, in the determination of the arteriovenous which will be described later, the superposition of the MC image is not an essential process, and the determination of the arterial and vein may be performed from the MC image which is not superposed.

(Example 1: First arteriovenous determination method)
In the present embodiment, an example of performing arteriovenous separation using the superimposed MC images obtained as described above will be described with reference to FIG.

  First, the signal processing unit 053 acquires the MC superimposed image 601 shown in FIG. The signal processing unit 053 acquires the MC image 602 shown in FIG. 6B from the image 601 excluding the inner boundary membrane and the nerve fiber layer (RNFL). By removing the inner boundary membrane and nerve fiber layer from the MC front image, the MC front image can be easily determined for arteriovenous. Note that the MC image 062 may be generated by removing from the inner boundary membrane to the upper layer of the inner granular layer. That is, the MC image 602 is an MC front image of a blood vessel layer located in the lower layer of the inner granular layer. The MC image 602 may or may not include the choroid side layer from the lower layer of the inner granular layer. Moreover, it is good also as producing | generating the MC front image of only an upper layer among inner granule layers. Since a part of the MC front image of the inner granule layer is less affected by blood vessels in the surface layer, the determination accuracy of the arteriovenous can be improved. For example, the signal processing unit 053 generates the MC image 602 from a three-dimensional MC image. The MC front image generated for the arteriovenous determination is not limited to the above example. For example, the MC front image generated for arteriovenous determination may be an image including the ganglion cell layer without including the inner boundary membrane and the nerve fiber layer, or the inner boundary membrane, nerve fiber layer, ganglion cell The image may include a part of the inner granular layer without including the layer and the inner network layer. For example, the MC front image generated for the arteriovenous determination may not include a layer on the choroid side from a part of the inner granular layer. Here, the part of the inner granule layer is, for example, one of the cases where the inner granule layer is divided into upper and lower parts on the vitreous side and the choroid side.

  The signal processing unit 053 binarizes the MC image 602 and acquires an image 603 shown in FIG. The signal processing unit 053 obtains the erosion image 604 by performing erosion on the image 603 (or any other method as long as it is a noise removal method). The signal processing unit 053 acquires main blood vessels (606 to 610) from the image 604. The acquisition of blood vessels may be performed based on the user's designation with respect to the MC image 602, or may be performed automatically regardless of the user's designation. In addition, blood vessel acquisition may be performed based on a three-dimensional MC image, or may be performed based on a two-dimensional MC image. For example, when acquiring a blood vessel based on a three-dimensional MC image, it is difficult to be affected by the intersection of blood vessels when viewed from the front direction, so it is possible to acquire a blood vessel that intersects in the depth direction as a separate blood vessel. It becomes. In the case of automatically acquiring a blood vessel, a blood vessel having a blood vessel length of a predetermined value or more may be extracted as a main blood vessel.

  The signal processing unit 053 acquires an image 605 obtained by adapting the extracted blood vessel to the original image 602. Here, the signal processing unit 053 identifies the positions of the main blood vessels 606 to 610 in the MC image 602 by associating the positional relationship between the MC image 602 and the image 605, for example. Here, since the blood vessel 609 is a blood vessel in which the blood vessel 606 is branched, the signal processing unit 053 treats the blood vessel 606 and the blood vessel 609 as the same blood vessel. Therefore, the blood vessels independent in this processing are 606, 607, 608, and 610. The signal processing unit 053 measures the luminance around the blood vessels (2 pixels along the blood vessel wall perpendicular line: about 20 μm) of the independent blood vessels (606, 607, 608, 610). The signal processing unit 053 calculates the luminance per unit pixel (Iave: luminance value per unit pixel) around the blood vessel as a representative value of the motion contrast value. Here, the area where the luminance is measured is an example of an evaluation area. This evaluation region may be a region adjacent to the blood vessel or may not be adjacent to the blood vessel. For example, the evaluation region may be a region located within 2 pixels from the blood vessel even if it is not adjacent to the blood vessel. The signal processing unit 053 determines whether the blood vessel is an artery or a vein based on the evaluation region that satisfies such a condition. That is, the signal processing unit 053 is an example of a determination unit that determines whether a blood vessel is an artery or a vein based on a motion contrast value of an evaluation region that is an area located within a predetermined distance from a region corresponding to a blood vessel in a motion contrast image. Equivalent to.

  Further, the evaluation region is not limited to the region of 2 pixels from the blood vessel, and may be larger than 2 pixels or less than 2 pixels. For example, the evaluation region may be determined based on the blood vessel diameter. For example, the evaluation region may be closer to the blood vessel as the blood vessel diameter is shorter. That is, the predetermined distance from the region corresponding to the blood vessel, which is the distance defining the location where the evaluation region is located, may be determined based on the thickness of the region corresponding to the blood vessel. Specifically, the predetermined distance may be shorter as the thickness of the region corresponding to the blood vessel is smaller.

  Moreover, it is good also as shortening the width | variety of the direction which cross | intersects the blood vessel running direction of an evaluation area, so that a blood vessel diameter is short. That is, the size of the evaluation region in the direction intersecting the traveling direction of the blood vessel may be determined based on the thickness of the region corresponding to the blood vessel.

  It should be noted that the size of the evaluation region in the running direction of the blood vessel is one of important parameters in the arteriovenous determination. Therefore, the size of the evaluation region in the blood vessel traveling direction may be larger than a predetermined value. For example, the signal processing unit 053 may set an evaluation region for the entire blood vessel. Further, the ratio of the evaluation region to the travel distance of the blood vessel in the travel direction of the blood vessel may be larger than a predetermined value. The evaluation area may be set to a two-dimensional MC image (MC front image) or may be set to a three-dimensional MC image.

  When the calculated luminance average value (Iave) of each blood vessel exceeds a certain threshold value (Is), the signal processing unit 053 determines that the main blood vessel is a vein, and if not, the main blood vessel is determined as an artery. Is determined. That is, the signal processing unit 053 determines that the blood vessel is a vein when the representative value is larger than the threshold value, and determines the blood vessel as an artery when the representative value is less than the threshold value.

  Actually, the signal processing unit 053 can acquire data as shown in FIG. The signal processing unit 053 can determine that the blood vessel 606 is an artery because the average luminance value Iave of the blood vessel 606 is lower than Is. Further, since the luminance average value Iave of the blood vessels 607, 608, and 610 is higher than Is, the signal processing unit 053 can determine that the blood vessels 607, 608, and 610 are veins. Note that the threshold value Is may be fixed or variable. For example, the signal processing unit 053 may change the threshold Is according to the blood vessel diameter. The signal processing unit 053 may increase the threshold Is as the blood vessel diameter is shorter. That is, the threshold value Is may be a constant value regardless of the blood vessel, or may be a value that is adaptively set for each blood vessel.

  The above process will be described with reference to the flowchart of FIG.

  In step S701, the signal processing unit 053 reads from the memory 052 the superimposed MC image (three-dimensional MC image) obtained by the processing of FIG. In step S702, the signal processing unit 053 acquires an EnFace image from the three-dimensional MC image. In this embodiment, the signal processing means 053 includes the layer boundary of GCL (ganglion cell layer) / IPL (inner reticulated layer) and IPL / INL (inner granule layer) (−15 μm: the negative sign indicates the choroid side) An EnFace image having a depth range from the boundary of the position of was acquired. In step S703, the signal processing unit 053 performs image processing on the image obtained in step S702 to extract main blood vessels. In this embodiment, the signal processing unit 053 binarizes the MC image and then performs erosion processing once (see FIG. 6D). The signal processing unit 053 extracts a set (line) of dots (pixels) that continue for a certain distance in the image. In this embodiment, the signal processing means 053 extracts a line of 80 pixels or more (about 1.0 mm or more) as a main blood vessel. Note that the numerical value for blood vessel extraction is not limited to 80 pixels.

  In step S704, the signal processing unit 053 numbers the main blood vessels obtained in step S703. In step S705, the signal processing unit 053 adapts the numbered blood vessel to the MC image acquired in S702 (see FIG. 6E). In step S706, the signal processing unit 053 measures the luminance (motion contrast value) around each blood vessel using the image 605 obtained in step S705. In this embodiment, the luminance around the blood vessel (2 pixels: about 20 μm area) is measured, and the average luminance value (Iave) is calculated for each blood vessel. Note that the signal processing means 053 may be a median luminance instead of an average luminance. In step S707, the signal processing unit 053 compares the luminance average (Iave) of each blood vessel obtained in step S706 with a threshold value (Is). In step S708, when Iave is lower than Is, the signal processing unit 053 determines that the artery is an artery. In step S709, when Iave is higher than Is, it is determined as a vein.

  According to the above example, it is possible to determine the arteriovenous from the motion contrast image. The median, standard deviation, maximum value, etc. may be used as the representative value of the evaluation area instead of Iave.

(Example 2: Second arteriovenous determination method)
In the first embodiment, the arteriovenous determination is performed based on the luminance at a position of 20 μm from the vascular wall perpendicular line. However, in this embodiment, the arteriovenous is determined by measuring the luminance profile from the blood vessel center as follows.

  Specifically, as in the first embodiment, the signal processing unit 053 extracts a blood vessel from the MC image (FIG. 10A). As shown in FIG. 10A, the signal processing unit 053 defines areas 1011, 1012, 1013 of 500 μm perpendicular to the traveling direction of the extracted blood vessels 1010, 1014. The areas 1011, 1012, and 1013 may be defined based on user input, or may be defined regardless of user input. In the present specification, the term “vertical” is a concept that includes a case of being completely vertical and a case of being substantially vertical. Further, the size of the areas 1011, 1012, and 1013 is not limited to 500 μm, and may be other values. For example, the above area may be an individual size for each blood vessel. The size of the area may be changed according to the blood vessel diameter. For example, the area size may be reduced as the blood vessel diameter is shorter. The signal processing unit 053 measures a luminance profile (motion contrast value profile) for each of the areas 1011, 1012, 1013. The luminance profile of the area 1011 was measured as shown in FIG. 10B. Similarly, the area 1012 was measured as shown in FIG. 10C, and the area 1013 was measured as shown in FIG. The signal processing unit 053 performs determination of the arteriovenous from the luminance profile shown in FIG. For example, the signal processing unit 053 sets Is (luminance threshold value) as a luminance value (motion contrast value) of a region where a blood vessel does not exist (a level equivalent to background noise). The signal processing unit 053 can divide the profile into a profile region 1001 having a higher luminance than Is, a profile region 1002 having a lower luminance than Is, a profile region 1003 having a higher luminance in the profile, and a profile region 1004 having a lower luminance than Is. . Comparing the area 1011 in FIG. 10A and the luminance profile in FIG. 10B, the profile region 1001 has a capillary network, the profile region 1002 has an avascular region, and the profile region 1003 has several tens of μm. It can be confirmed that an avascular region exists in the thick blood vessel and profile region 1004. Furthermore, the avascular region of the profile region 1002 and the profile region 1004 indicates that an avascular region exists at a plurality of points (pixels), that is, a region of several tens of μm or more. A blood vessel 1010 that has an avascular region around the vascular structure of the fundus can be determined as an artery.

  Similarly, the luminance profile of the area 1012 will be described with reference to FIG. . Similarly to the area 1011, the signal processing unit 053 has a profile area 1005 having a higher brightness than Is, a profile area 1006 having a lower brightness than Is, and a profile area 1007 having a higher brightness in the profile, and a brightness lower than Is. The profile shown in FIG. 10C can be divided into the profile area 1008. Comparing the luminance profile in FIG. 10C with the area 1012, the profile region 1005 is a capillary network, the profile region 1006 is a non-blood vessel region, and the profile region 1007 is a thick blood vessel of several tens of μm (than the blood vessel 1010. It is shown that the avascular region exists in the profile region 1008. Furthermore, it can be seen that the avascular region of the profile region 1006 and the profile region 1008 is present at a plurality of points, that is, regions of several tens of μm or more. The signal processing unit 053 can determine that the region 1012 is an artery from the MC image because the region 1012 also has the avascular region around the presence of the avascular region. The blood vessel in the region 1012 is a natural result because the blood vessel 1010 is branched. In this embodiment, a plurality of areas 1011 and 1012 are set for one blood vessel 1010. However, an arteriovenous determination is made based on the profile of one area, and the determination result is reflected on the connected blood vessels. Also good. Further, in order to increase the accuracy of the determination, the determination target blood vessel may not be determined as an artery only when it is determined as an artery (or vein) in a plurality of areas.

  Similarly, the luminance profile of the region 1013 will be described with reference to FIG. Similarly to the area 1011, the signal processing unit 053 can divide the profile into a profile area 1009 having a higher luminance than Is, a profile area 1015 having a lower luminance than Is, and a profile area 1015 having a higher luminance in the profile. In the profile regions 1009 and 1016 whose luminance is higher than Is, some blood vessels such as capillaries exist. Further, it can be seen that the profile region 1015 having a low luminance is a single point and is located between the blood vessel and the blood vessel rather than the avascular region. The signal processing unit 053 can determine that the blood vessel 1014 is a vein without an avascular region (or because the avascular region is equal to or less than a threshold value).

  That is, the signal processing unit 053 can determine the arteriovenous from the size of the avascular region obtained from the profile of the motion contrast value.

  The above determination, that is, the procedure of the second arteriovenous determination method will be described with reference to FIG. In step S1101, the signal processing unit 053 reads the superimposed MC image obtained by the processing of FIG. In step S1102, the signal processing unit 053 extracts a blood vessel from the MC image. In step S1103, the signal processing unit 053 sets a straight line of 200 μm in the vertical direction with respect to the direction of blood vessel travel, and measures the luminance (motion contrast value) on the straight line. In step S1104, the signal processing unit 053 reads the threshold value Is from the memory 052. In step S1105, the signal processing unit 053 measures the number (number of pixels) n of regions where the luminance is lower than the threshold Is. The threshold value Is corresponds to an example of a first threshold value. In this embodiment, only the number of pixels is counted. In steps S1106 and 1107, if the number of pixels measured in step S1105 is n> 4, the signal processing unit 053 determines that the artery is an artery because there are more avascular regions than the threshold. In steps S1106 and 1108, if n ≦ 4, the signal processing unit 053 determines that the avascular region is equal to or less than the threshold value, so that it is a vein. That is, it can be said that the signal processing unit 053 determines whether the blood vessel is an artery or a vein based on the size of the region where the motion contrast value is less than the first threshold in the evaluation region. The above “4” corresponds to an example of a second threshold value. That is, it can be said that the signal processing unit 053 determines that the blood vessel is an artery when the size of the region where the motion contrast value is less than the first threshold in the value region is greater than the second threshold. Further, it can be said that the signal processing unit 053 determines that the blood vessel is a vein when the size of the region whose motion contrast value is less than the first threshold in the evaluation region is smaller than the second threshold.

  In step S1110, the control unit 054 causes the monitor 055 to display the result. For example, the control unit 054 causes the monitor 055 to display an MC image in which arteries and veins are displayed in different colors.

  As described above, the arteriovenous can be determined by measuring the luminance around the blood vessel using the MC image. In the present embodiment, the profile region is a straight region, but a two-dimensional region, a three-dimensional region, a region of a set of points, or the like may be used. Similarly, the size may be other than the embodiment. For luminance measurement, luminance average, standard deviation, maximum value, and the like may be used as representative values of the evaluation area.

  The threshold value Is can also be determined with high accuracy by changing it according to the installation environment, setting by the user, and race. Although described in the flow of FIG. 11, the flow of extracting blood vessels from the image may be manual or automatic.

  In this embodiment, MC image superposition is used, but the determination of the arteriovenous can also be made using a single MC image.

(Modification 1)
In step 702, when an MC image is acquired, if an MC front image is acquired based on INL (inner granule layer), the arteriovenous determination becomes more stable. Specifically, as shown in FIG. 3 (c), the signal processing unit 053 converts the line 307 (inner boundary membrane) to the line 308 (lower nerve fiber layer) and the line 308 (lower nerve fiber layer) to line 309 (nerve). Node node layer lower part), line 309 (lower ganglion cell layer lower part) to line 306 (INL center) and line 306 (INL center) to line 310 (outer reticulated layer lower part). The signal processing unit 053 separates the four layers as follows using this division information, and the data is stabilized when the above-described arteriovenous determination is performed in the second layer. The first layer is defined as a layer between the line 307 and the line 308 about 5 μm below (choroidal direction). The second layer is defined as a layer between 5 μm or more below line 308 and about 5 μm above line 309. The third layer is defined as a layer between the line 306 and the line 306 from about 5 μm above the line 309. The fourth layer is defined as the layer between line 306 and line 310. By using the second layer as the MC image (MC front image), the determination of the arteriovenous can be obtained with higher accuracy. The MC image using the second layer corresponds to an image including the ganglion cell layer without including the inner boundary membrane and the nerve fiber layer. In FIG. 3C, the line 304 indicates the upper end of the INL, and the line 305 indicates the lower end of the INL. The third layer and the fourth layer may be used for the arteriovenous determination. The MC image using the fourth layer does not include the inner boundary membrane, the nerve fiber layer, and the ganglion cell layer, and corresponds to an image including only a part of the entire inner granule layer.

  Although the line 306 is the center of the INL, the present invention is not limited to this, and the line 306 may be a position where the line 306 is separated into two upper and lower layers based on the blood vessel arrangement in the INL. Since the blood vessels in the INL are linearly arranged in the X direction (direction orthogonal to the depth direction) (for example, the upper and lower two-stage blood vessels are linearly arranged), the signal processing means 053 is arranged in the blood vessel array. Based on this, the position where the INL is separated into two layers can be determined. Specifically, the signal processing unit 053 obtains a line connecting the blood vessels in the INL in the X direction (or Y direction) for the two-stage blood vessels in the INL, and obtains a midpoint in the depth direction of the obtained lines. A line connecting the midpoints obtained in the X direction may be a line 306. That is, the signal processing unit 053 corresponds to an example of a determining unit that determines a part of the inner granule layer (the upper layer or the lower layer of the inner granule layer) based on the arrangement of blood vessels included in the inner granule layer. . The signal processing unit 053 generates the third layer and fourth layer MC images using the line 306. That is, it corresponds to an example of a generation unit that generates a motion contrast image based on a part of the inner granular layer.

  Further, an MC image of only a part (upper layer or lower layer) of the inner granular layer defined by the line 306 may be generated. This modification can also be applied to the first, third, and fourth embodiments.

(Modification 2)
In this example, a clear MC image can be acquired by separating blood vessels, and an example in which the analysis of Examples 1 and 2 is further improved will be described.

  An image 901 in FIG. 9A is an image displaying only the MC signal in the XZ section. A black point 902 in FIG. 9A is an area (point) having a temporal change in the OCT image. A black dot 902 is a portion having a signal having a motion contrast value of a predetermined value or more, that is, a blood vessel portion. The same applies to the black point 903. Black dots 902 indicate blood vessels in the surface layer of the retina, and black dots 903 indicate blood vessels in the choroid area.

  In the image of FIG. 9A, when luminance (motion contrast value) is added in the x direction at the arrow positions a, b, c, and d, the result is as shown in FIG. 9B. The positions of the arrows a, b, c, and d can be specified from the peak position of the profile in the A scan direction of the luminance tomographic image. The A scan for specifying the positions of the arrows a, b, c, and d may use an integrated value of a plurality of A scans. It has been found that blood vessels existing in the superficial layer and the deep layer can be classified into four blood vessels a to d as shown in FIG. 9A. In particular, the blood vessel layer a has a low luminance, and it is considered that the blood vessel layer a belonged to the first layer in the above-described embodiment. Similarly, the blood vessel layer b is considered to belong to the second layer, the blood vessel layer c to the third layer, and the blood vessel layer d to the fourth layer. For example, the signal processing unit 053 changes the angle of the line integrated in the x direction so that the integrated value of the luminance of the blood vessel layer b corresponding to the second layer becomes high, and the integrated value of the luminance of the blood vessel layer b is maximized. The MC front image for determining the arteriovenous may be generated with reference to the line of the angle. For example, the signal processing unit 053 generates the MC front image using a region having a predetermined width in a direction orthogonal to the line having an angle at which the integrated value of the luminance of the blood vessel layer b is maximized. The predetermined width may be an arbitrary value determined in advance, or a variance value or a standard deviation value of the motion contrast value of the blood vessel layer may be used. That is, the signal processing unit 053 generates the MC front image based on the blood vessel arrangement.

  The position of the blood vessel layer b may be specified by the user, or may be determined based on the peak position obtained by sequentially performing luminance integration in the x direction by the signal processing unit 053. is there. For example, the signal processing unit 053 can identify the second peak position from the choroid side as the blood vessel layer b.

  As in this example, an MC image suitable for arteriovenous determination can be acquired by generating an MC front image using blood vessels. In addition to the arteriovenous determination, an MC front image of a desired blood vessel is generated, which is suitable for diagnosis. This modification can also be applied to the first, third, and fourth embodiments.

(Example 3: Display of information on arteriovenous determination)
In the first and second embodiments, it has been described that the determination of the arteriovenous based on the MC image is automatically performed based on the motion contrast value in the evaluation region or the size of the avascular region. An example of displaying supporting information will be described.

  In this embodiment, the control unit 054 causes the monitor 055 to display at least one of Iave obtained in step S706 in FIG. 7 and the number n of pixels indicating the avascular region obtained in step S1105 in FIG. In this embodiment, automatic determination as to whether a blood vessel is an artery or a vein is not performed. Iave or number n corresponds to an example of information indicating whether a blood vessel is an artery or a vein. The control unit 054 corresponds to an example of a display control unit that displays information indicating whether an artery or a vein is displayed on the display unit.

  The control unit 054 causes the monitor 055 to display at least one of Iave and the number n regarding the blood vessel selected by the user or the blood vessel automatically selected by the device. The control unit 054 may further display a threshold value serving as a reference for determining at least one of Iave and number n on the monitor 055. Further, the MC image may be further displayed on the monitor 055. For example, when a blood vessel on the MC image is designated by the user, at least one of Iave and number n related to the blood vessel and a threshold value used as a reference for determining at least one of Iave and number n are displayed on the monitor 055. .

  In this way, the user can grasp information necessary for the arteriovenous determination.

(Example 4: FAZ detection)
In the present embodiment, an analysis apparatus capable of providing FAZ analysis more stably will be described.

  FAZ is an avascular region in the fovea, and is a region 311 in FIG. In the conventional superficial capillary image using the MC signal, the FAZ measurement was not stable. The reason is that the dividing boundary between the shallow layer (Supercapillary Capillary) and the deep layer (Deep Capillary) is ambiguous and depends on the layer segmentation definition.

  The flow of this embodiment will be described with reference to FIG.

  In step S <b> 801, the signal processing unit 053 acquires the MC image after superposition from the memory 052. In step S802, the image acquired in step S801 is separated into the first layer, the second layer, the third layer, and the fourth layer described above. In step S803, the signal processing unit 053 extracts the third-layer MC image divided in step S802. For example, the signal processing unit 053 generates a third layer MC image (MC front image) based on the three-dimensional MC image after superposition acquired in step S801. In step S804, the signal processing unit 053 measures FAZ using the MC image of the third layer acquired in step S803. In this example, FAZ was measured by binarizing the third layer MC image.

  When the FAZ was measured by the flow as described above, the repetition accuracy was improved as compared with the case where the measurement was performed using a conventional superficial layer. Moreover, in the diseased eye, the FAZ value was further stabilized by performing the same treatment.

  The processing of the present embodiment is particularly effective when measuring changes over time because it is possible to accurately extract slight changes when acquiring change information over time in the same subject.

(Other embodiments)
Although the embodiment has been described in detail above, the disclosed technology can take an embodiment as a system, apparatus, method, program, recording medium (storage medium), or the like. Specifically, the present invention may be applied to a system composed of a plurality of devices (for example, a host computer, an interface device, an imaging device, a web application, etc.), or may be applied to a device composed of a single device. good.

  Needless to say, the object of the present invention can be achieved as follows. That is, a recording medium (or storage medium) that records a program code (computer program) of software that implements the functions of the above-described embodiments is supplied to the system or apparatus. Needless to say, such a storage medium is a computer-readable storage medium. Then, the computer (or CPU or MPU) of the system or apparatus reads and executes the program code stored in the recording medium. In this case, the program code itself read from the recording medium realizes the functions of the above-described embodiment, and the recording medium on which the program code is recorded constitutes the present invention.

  In addition, you may implement combining the Example and modification which were mentioned above suitably.

053 Signal processing means 054 Control means 055 Monitor

Claims (25)

Obtaining means for obtaining a motion contrast image of the fundus;
Determining means for determining whether the blood vessel is an artery or a vein based on a motion contrast value of an evaluation region that is an area located within a predetermined distance from a region corresponding to the blood vessel in the motion contrast image;
An image processing apparatus comprising:   The image processing apparatus according to claim 1, wherein the determination unit determines whether the blood vessel is an artery or a vein based on a representative value of a motion contrast value in the evaluation region.   The image processing apparatus according to claim 2, wherein the determination unit determines that the blood vessel is an artery when the representative value is less than a threshold value.   The image processing apparatus according to claim 2, wherein the determination unit determines that the blood vessel is a vein when the representative value is larger than the threshold value.   The image processing apparatus according to claim 2, wherein the representative value is an average value, a median value, or a maximum value of motion contrast values in the evaluation region.   The image processing apparatus according to claim 1, wherein the determination unit determines whether the blood vessel is an artery or a vein based on a size of a region having a motion contrast value less than a first threshold in the evaluation region.   The image according to claim 6, wherein the determination unit determines that the blood vessel is an artery when a size of a region having a motion contrast value less than the first threshold in the evaluation region is greater than a second threshold. Processing equipment.   7. The determination unit according to claim 6, wherein the blood vessel is determined to be a vein when a size of a region having a motion contrast value less than the first threshold in the evaluation region is smaller than the second threshold. Item 8. The image processing apparatus according to Item 7.   The image processing apparatus according to claim 1, wherein the evaluation area is an area adjacent to an area corresponding to the blood vessel.   The image processing apparatus according to claim 1, wherein a size of the evaluation region in the traveling direction of the blood vessel is larger than a predetermined value.   10. The image processing apparatus according to claim 1, wherein a ratio of the evaluation region to a travel distance of the blood vessel in the travel direction of the blood vessel is larger than a predetermined value.   The size of the evaluation region in a direction intersecting the traveling direction of the blood vessel is determined based on the thickness of the region corresponding to the blood vessel. Image processing apparatus.   The image processing apparatus according to claim 1, wherein the predetermined distance is determined based on a thickness of a region corresponding to the blood vessel.   The image processing apparatus according to claim 13, wherein the predetermined distance is shorter as a thickness of a region corresponding to the blood vessel is smaller.   The image processing apparatus according to claim 1, wherein the motion contrast image is an EnFace image.   The image processing apparatus according to claim 15, wherein the motion contrast image is an image including a ganglion cell layer without including an inner limiting membrane and a nerve fiber layer.   The image processing apparatus according to claim 15, wherein the motion contrast image does not include an inner boundary membrane, a nerve fiber layer, and a ganglion cell layer but includes only a part of the entire area of the inner granule layer. Determining means for determining a part of the inner granule layer based on an arrangement of blood vessels contained in the inner granule layer;
The image processing apparatus according to claim 17, further comprising: a generation unit that generates the motion contrast image based on a part of the inner granular layer determined by the determination unit.   19. The image processing apparatus according to claim 18, wherein the determining unit determines a part of the inner granule layer based on an array of blood vessels in a direction orthogonal to the depth direction.   The image processing apparatus according to claim 17, wherein the motion contrast image does not include a layer that is closer to the choroid than a part of the inner granular layer. Obtaining means for obtaining a motion contrast image of the fundus;
Determining means for determining whether the blood vessel is an artery or a vein based on the size of an avascular region adjacent to the region corresponding to the blood vessel in the motion contrast image;
An image processing apparatus comprising: Obtaining means for obtaining a motion contrast image of the fundus;
Determination means for determining whether a blood vessel included in the motion contrast image is an artery or a vein based on a motion contrast value in the motion contrast image;
An image processing apparatus comprising: Acquisition means for acquiring information indicating whether the blood vessel is an artery or a vein based on a motion contrast value of an evaluation region which is a region located within a predetermined distance from a region corresponding to a blood vessel in a motion contrast image of the fundus;
Display control means for displaying on the display section information indicating whether the artery or vein;
An image processing apparatus comprising: An acquisition step of acquiring a motion contrast image of the fundus;
A determination step of determining whether the blood vessel is an artery or a vein based on a motion contrast value of an evaluation region that is a region located within a predetermined distance from a region corresponding to the blood vessel in the motion contrast image;
An image processing method comprising:   A program causing a computer to execute the image processing method according to claim 24.