Optical coherence tomography (OCT) data processing method

Abstract

To make OCT data processing efficient.SOLUTION: A method according to the embodiment can perform imaging processing of Fourier domain method (FD method) including Fourier transformation transforming A scanning data to be a spectrum intensity distribution indicating relation between a wave number value and an intensity value into a reflection intensity distribution in a depth direction. The method prepares for a sample three-dimensional data set, applies a high-pass filter to each A scanning data of the three-dimensional data set to extract an amplitude component without applying the imaging processing of the FD method, determines single estimated intensity value (representative intensity value) on the basis of an inverse cumulative distribution function from the amplitude component, and creates a two-dimensional map on a surface orthogonal to the depth direction of a plurality of representative intensity values obtained from a plurality of A scanning data. The method determines a positional relationship between the three-dimensional data set and a sample area on the basis of the two-dimensional map, applies the imaging processing of the FD method to the three-dimensional data set, and generates OCT image data.SELECTED DRAWING: Figure 7

Description

Technical Field

[0001] The present invention relates to a method for processing optical coherence tomography (OCT) data.

Background Art

[0002] OCT is a technique capable of imaging an optical scattering medium with a resolution at the micrometer level or less, and is used in medical imaging, non-destructive inspection, etc. OCT is a technique based on the low-coherence interferometry method, and typically uses near-infrared light to ensure the depth of penetration into a sample of the optical scattering medium.

[0003] Patent Document 1 discloses a method for processing an OCT data set obtained as a function of optical depth by measurement of backscattering or backreflection in order to efficiently collect OCT data and to accurately and quickly collect OCT data from a specific region of a sample, the method including analyzing the OCT data set to identify at least a first subset of landmark region data, positioning the OCT data set based on the landmark region data, and performing processing on at least a second subset of the OCT data set based on the correspondence between the OCT data set and the landmark region data.

[0004] Further, Patent Document 2 discloses a method for monitoring the progression of a disease, including obtaining an OCT survey scan data set as a function of optical depth by measurement of backscattering or backreflection, analyzing the survey scan data set to identify a landmark region, registering a portion of the survey scan data set representing at least a part of a diseased tissue region related to the landmark region by assigning a position related to a position within the sample or a fixed position to an element of the survey scan data set, and monitoring changes in the diseased tissue region at a plurality of different time points.

Prior Art Documents

Patent Documents

[0005] [Patent Document 1] U.S. Patent No. 7,884,945 [Patent Document 2] U.S. Patent No. 8,405,834 [Summary of the Invention] [Problems to be Solved by the Invention]

[0006] An object of the present invention is to improve the efficiency of OCT data processing. [Means for Solving the Problems]

[0007] Some exemplary embodiments are methods for processing data collected by applying an optical coherence tomography (OCT) scan to a sample (OCT data processing methods), which include preparing a three-dimensional data set collected from the sample, creating a two-dimensional map based on representative intensity values of each of a plurality of A-scan data included in the three-dimensional data set, positioning the three-dimensional data set based on the two-dimensional map, and executing processing based on at least a partial data set of the positioned three-dimensional data set.

[0008] Any of the following optional embodiments can be combined with the OCT data processing method of some exemplary embodiments: the processing includes a predetermined analysis process; the processing includes a predetermined evaluation process based on data obtained by the analysis process; the processing includes setting a partial data set to which the analysis process is applied; the processing includes setting an application area of a predetermined inspection for the sample; the processing includes setting a partial data set to which a predetermined imaging process is applied; preparing inspection data obtained from the sample by a predetermined inspection different from OCT, and the processing includes a predetermined comparison process between the inspection data and at least a part of the three-dimensional data set.

[0009] Some exemplary aspects are methods for processing data collected by applying an optical coherence tomography (OCT) scan to a sample (OCT data processing methods), which include preparing a first three-dimensional data set and a second three-dimensional data set collected from the sample, creating a first two-dimensional map based on representative intensity values of a plurality of A-scan data included in the first three-dimensional data set, creating a second two-dimensional map based on representative intensity values of a plurality of A-scan data included in the second three-dimensional data set, and performing processing based on at least one of at least a first partial data set of the first three-dimensional data set and at least a second partial data set of the second three-dimensional data set based on the first two-dimensional map and the second two-dimensional map.

[0010] The OCT data processing method of some exemplary embodiments can combine any of the following optional embodiments: The processing includes registration between the at least first partial data set and the at least second partial data set through registration between the first two-dimensional map and the second two-dimensional map; The processing includes adjustment of the application area of the OCT scan for the sample through registration between the first two-dimensional map and the second two-dimensional map; The adjustment is sequentially performed by sequentially processing the three-dimensional data sets sequentially collected from the sample; The registration between the first two-dimensional map and the second two-dimensional map includes image correlation calculation; The image correlation calculation obtains a displacement amount between the first two-dimensional map and the second two-dimensional map, and performs the registration between the first two-dimensional map and the second two-dimensional map based on the displacement amount; The displacement amount includes at least one of a translation amount and a rotation amount; The first three-dimensional data set and the second three-dimensional data set are collected from different three-dimensional regions of the sample, and the processing includes synthesis of the first image data generated from the at least first partial data set and the second image data generated from the at least second partial data set through registration between the first two-dimensional map and the second two-dimensional map; The processing includes a predetermined analysis process; The processing includes a predetermined evaluation process based on the data obtained by the analysis process; The processing includes setting of the partial data set to which the analysis process is applied; Prepare a plurality of three-dimensional data sets corresponding to different time points respectively, including the first three-dimensional data set and the second three-dimensional data set, and the analysis process includes a process of obtaining a time-series change of a predetermined parameter value.

[0011] Some exemplary aspects include an OCT scanner that applies an optical coherence tomography (OCT) scan to a sample to collect a three-dimensional dataset, a map creation unit that creates a two-dimensional map based on representative intensity values of each of a plurality of A-scan data included in the three-dimensional dataset, a positioning unit that positions the three-dimensional dataset based on the two-dimensional map, and a processing execution unit that executes processing based on at least a partial dataset of the positioned three-dimensional dataset.

[0012] Some exemplary aspects include an OCT scanner that applies an optical coherence tomography (OCT) scan to a sample to collect a first three-dimensional dataset and a second three-dimensional dataset, a map creation unit that creates a first two-dimensional map based on representative intensity values of each of a plurality of A-scan data included in the first three-dimensional dataset and creates a second two-dimensional map based on representative intensity values of each of a plurality of A-scan data included in the second three-dimensional dataset, and a processing execution unit that executes processing based on at least one of at least a first partial dataset of the first three-dimensional dataset and at least a second partial dataset of the second three-dimensional dataset based on the first two-dimensional map and the second two-dimensional map.

[0013] A method of controlling an OCT apparatus including an OCT scanner that applies an optical coherence tomography (OCT) scan to a sample and a processor, the method comprising controlling the OCT scanner to collect a three-dimensional dataset from the sample, controlling the processor to create a two-dimensional map based on representative intensity values of each of a plurality of A-scan data included in the three-dimensional dataset, controlling the processor to position the three-dimensional dataset based on the two-dimensional map, and controlling the processor to execute processing based on at least a partial dataset of the positioned three-dimensional dataset.

[0014] Some exemplary aspects are methods for controlling an OCT apparatus including an OCT scanner and a processor that applies an optical coherence tomography (OCT) scan to a sample, the method comprising controlling the OCT scanner to collect a first three-dimensional dataset and a second three-dimensional dataset from the sample, creating a first two-dimensional map based on respective representative intensity values of a plurality of A-scan data included in the first three-dimensional dataset, and controlling the processor to create a second two-dimensional map based on respective representative intensity values of a plurality of A-scan data included in the second three-dimensional dataset, and controlling the processor to execute processing based on at least one of at least a first partial dataset of the first three-dimensional dataset and at least a second partial dataset of the second three-dimensional dataset based on the first two-dimensional map and the second two-dimensional map.

[0015] Some exemplary aspects are an OCT data processing apparatus including a reception unit that receives a three-dimensional dataset collected by applying an optical coherence tomography (OCT) scan to a sample, a map creation unit that creates a two-dimensional map based on respective representative intensity values of a plurality of A-scan data included in the three-dimensional dataset, a positioning unit that positions the three-dimensional dataset based on the two-dimensional map, and a processing execution unit that executes processing based on at least a partial dataset of the positioned three-dimensional dataset.

[0016] Some exemplary aspects include a receiving unit that receives a first three-dimensional dataset and a second three-dimensional dataset collected by applying an optical coherence tomography (OCT) scan to a sample, a mapping unit that creates a first two-dimensional map based on representative intensity values of respective ones of a plurality of A-scan data included in the first three-dimensional dataset and creates a second two-dimensional map based on representative intensity values of respective ones of a plurality of A-scan data included in the second three-dimensional dataset, and a processing execution unit that executes processing based on at least one of at least a first partial dataset of the first three-dimensional dataset and at least a second partial dataset of the second three-dimensional dataset based on the first two-dimensional map and the second two-dimensional map. An OCT data processing apparatus.

[0017] Some exemplary aspects are methods of controlling an optical coherence tomography (OCT) data processing apparatus including a processor, controlling the processor to receive a three-dimensional dataset collected by applying an OCT scan to a sample, controlling the processor to create a two-dimensional map based on representative intensity values of respective ones of a plurality of A-scan data included in the three-dimensional dataset, controlling the processor to position the three-dimensional dataset based on the two-dimensional map, and controlling the processor to execute processing based on at least a partial dataset of the three-dimensional dataset for which the positioning has been performed.

[0018] Some exemplary embodiments are methods of controlling an optical coherence tomography (OCT) data processing apparatus including a processor, the method comprising controlling the processor to receive a first three-dimensional dataset and a second three-dimensional dataset collected by applying an optical coherence tomography (OCT) scan to a sample, creating a first two-dimensional map based on respective representative intensity values of a plurality of A-scan data included in the first three-dimensional dataset, and creating a second two-dimensional map based on respective representative intensity values of a plurality of A-scan data included in the second three-dimensional dataset, and controlling the processor to perform processing based on at least one of at least a first partial dataset of the first three-dimensional dataset and at least a second partial dataset of the second three-dimensional dataset based on the first two-dimensional map and the second two-dimensional map.

[0019] Some exemplary embodiments are programs for causing a computer to execute the method of any of the embodiments.

[0020] Some exemplary embodiments are computer-readable non-transitory recording media having recorded thereon the program of any of the embodiments. [[Effect of the Invention]]

[0021] According to the exemplary embodiments, it is possible to improve the efficiency of OCT data processing. [[Brief Description of the Drawings]]

[0022]

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Best Mode for Carrying Out the Invention

[0023] Some exemplary aspects of the embodiments will be described below. It is possible to incorporate matters disclosed in the documents cited in this specification and matters related to any known techniques into the exemplary aspects.

[0024] Some exemplary aspects relate to a technique (OCT data processing method) for processing a three-dimensional dataset collected by applying an OCT scan to a three-dimensional region of a sample, and include a process of creating a two-dimensional map from a plurality of A-scan data included in the three-dimensional dataset.

[0025] Furthermore, based on this two-dimensional map, the three-dimensional dataset is positioned. This positioning typically includes a process of determining the correspondence (positional relationship) between the three-dimensional dataset and the region of the sample, and / or a process of determining the correspondence (positional relationship) between the three-dimensional dataset and another three-dimensional dataset.

[0026] In addition, a process based on part or all of the positioned three-dimensional dataset is executed. This process is, for example, executed on one or two or more three-dimensional datasets and may include any of the following: analysis; setting of an analysis area; evaluation of analysis data; setting of an evaluation area; setting of an inspection area; setting of an imaging area; comparison with other inspection data (for example, data collected by applying an OCT scan to the same sample, data obtained by applying an inspection different from the OCT scan to the same sample, data collected by applying an OCT scan to one or more other samples, and any one or more of the data obtained by applying an inspection different from the OCT scan to one or more other samples).

[0027] Some other exemplary aspects relate to a technique (OCT data processing method) for processing a first 3D dataset and a second 3D dataset collected by applying OCT scans to a first 3D region and a second 3D region of a sample respectively, including a process of creating a first 2D map from a plurality of A-scan data included in the first 3D dataset and a process of creating a second 2D map from a plurality of A-scan data included in the second 3D dataset.

[0028] Further, based on the first 2D map and the second 2D map, a process based on at least a part of the first 3D dataset and / or at least a part of the second 3D dataset is executed. This process may include, for example, any of the following: registration; tracking; panoramic OCT imaging (mosaic OCT imaging, montage OCT imaging); analysis (e.g., static analysis or dynamic analysis (time-series analysis, analysis of changes over time)); setting of an analysis area; evaluation of analysis data; setting of an evaluation area; setting of an inspection area; setting of an imaging area; comparison with other inspection data.

[0029] Some exemplary aspects are modality devices (including at least an OCT device) capable of implementing any of the above-described exemplary OCT data processing methods, having a function of applying an OCT scan to a sample to collect a 3D dataset. Also, some exemplary aspects are methods for controlling such an OCT device.

[0030] Some exemplary aspects are information processing devices (OCT data processing devices) capable of implementing any of the above-described exemplary OCT data processing methods, having a function of receiving OCT data (3D dataset) collected from a sample. Also, some exemplary aspects are methods for controlling such an OCT data processing device.

[0031] Some exemplary embodiments are programs executed by a computer (or a modality device including at least a computer and an OCT device) for any of the above-described exemplary methods (OCT data processing method, control method). Also, some exemplary embodiments are computer-readable non-transitory recording media on which such programs are recorded.

[0032] In some exemplary embodiments, a "processor" is, for example, a circuit such as a CPU (Central Processing Unit), GPU (Graphics Processing Unit), ASIC (Application Specific Integrated Circuit), programmable logic device (e.g., SPLD (Simple Programmable Logic Device), CPLD (Complex Programmable Logic Device), FPGA (Field Programmable Gate Array)). The processor provides some examples for realizing the target function, for example, by reading and executing a program stored in a storage circuit or a storage device.

[0033] The type of OCT applicable to some exemplary embodiments is arbitrary, typically swept source OCT or spectral domain OCT, but other types may also be used.

[0034] Swept source OCT is a technique that divides light from a wavelength-variable light source into measurement light and reference light, overlaps the return light of the measurement light from the sample with the reference light to generate interference light, detects this interference light with a photodetector, and performs Fourier transform or the like on the detection data collected according to the wavelength sweep and the scan of the measurement light to construct an image.

[0035] Spectral domain OCT is a method that splits light from a low-coherence light source (broadband light source) into measurement light and reference light, overlaps the return light of the measurement light from the sample with the reference light to generate interference light, detects the spectral distribution of this interference light with a spectrometer, and constructs an image by performing a Fourier transform or the like on the detected spectral distribution.

[0036] That is, swept-source OCT is an OCT method that acquires the spectral distribution of interference light in a time-division manner, and spectral domain OCT is an OCT method that acquires the spectral distribution of interference light in a space-division manner.

[0037] As types other than such Fourier domain OCT, there are time domain OCT that mechanically and sequentially performs axial (Z-direction) scans, en-face OCT or full field OCT that two-dimensionally images the XY plane orthogonal to the Z direction, and the like.

[0038] The exemplary embodiments described below are used for eye imaging, analysis, measurement, evaluation, etc. in the field of ophthalmology. Note that some exemplary embodiments may be used in other fields. Examples of other fields include medical fields other than ophthalmology (dermatology, dentistry, surgery, etc.) and industrial fields (non-destructive inspection, etc.).

[0039] <First exemplary embodiment> FIG. 1 and FIG. 2 show the configuration of an OCT device (ophthalmic device) 100 according to one exemplary embodiment. The ophthalmic device 100 provides OCT data processing in addition to OCT imaging.

[0040] More specifically, the ophthalmic apparatus 100 is configured to collect a three-dimensional dataset by applying an OCT scan targeting a three-dimensional region of a sample (eye 120). Here, the three-dimensional region is set to an arbitrary range. Also, considering eye movement and the like, the region where the OCT scan targeting the three-dimensional region is actually applied does not have to match this three-dimensional region, but by using fixation or tracking, etc., a region substantially matching the three-dimensional region can be scanned.

[0041] Furthermore, the ophthalmic apparatus 100 is configured to create a two-dimensional map based on the respective representative intensity values of a plurality of A-scan data included in the three-dimensional dataset collected from the sample. Here, the three-dimensional dataset is data before imaging processing (e.g., Fourier transform) is performed. The three-dimensional dataset typically consists of a plurality of A-scan data two-dimensionally arranged in the XY plane, and each A-scan data is a spectral intensity distribution (e.g., distribution data representing the relationship between wavenumber values and intensity values). Note that by applying Fourier transform or the like to the A-scan data, A-scan image data representing the reflection intensity distribution (backscattering intensity distribution) along the Z direction is generated. The process of creating a two-dimensional map from the three-dimensional dataset may include, for example, the process disclosed in Japanese Patent No. 6230023 (U.S. Patent Application Publication No. 2014 / 0293289).

[0042] Furthermore, the ophthalmic apparatus 100 is configured to perform positioning of the three-dimensional dataset based on the two-dimensional map. This positioning may include, for example, any one of a process of determining the correspondence (positional relationship) between the three-dimensional dataset and the region of the sample, a process of determining the correspondence (positional relationship) between the three-dimensional dataset and another three-dimensional dataset, and a process of determining the correspondence (positional relationship) between the three-dimensional dataset and inspection data. The determination of the positional relationship may be, for example, any one of the association of the coordinates of the three-dimensional dataset with a predetermined identifier (such as the name of a part), the association of the coordinates with each other, the association of the coordinate systems defined by each other, and the representation of both in a common coordinate system.

[0043] In one example, the positional relationship between a specific part of the eye 120 and the three-dimensional data set can be determined. The specific part of the eye 120 can be, for example, a lesion, a blood vessel, the optic disc, the macula, a sub-tissue of the fundus (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, Bruch's membrane, choroid, sclera, etc.), a sub-tissue of the cornea (corneal epithelium, Bowman's membrane, stroma, Dua's layer, Descemet's membrane, corneal endothelium, etc.), the iris, the lens, the zonular fibers, the ciliary body, the vitreous body, and any of other eye tissues. As a typical example, the part of the three-dimensional data set corresponding to the optic disc is determined, or the part of the three-dimensional data set corresponding to the retinal pigment epithelium layer is determined.

[0044] In other examples, the positional relationship between the three-dimensional data set of the eye 120 and another three-dimensional data set (OCT data collected from the eye 120 or another eye) can be determined. As an example, the positional relationship between the two three-dimensional data sets can be determined such that the regions corresponding to the common specific part match. For example, the positional relationship between these three-dimensional data sets can be determined such that the region of the three-dimensional data set corresponding to the optic disc matches the region of another three-dimensional data set also corresponding to the optic disc. Alternatively, the positional relationship between the two three-dimensional data sets can be determined such that the regions corresponding to different specific parts match the positional relationship between these specific parts. For example, the positional relationship between these three-dimensional data sets can be determined such that the region of the three-dimensional data set corresponding to the optic disc and the region of another three-dimensional data set corresponding to the macula match the positional relationship between the optic disc and the macula.

[0045] In still other examples, the positional relationship between the three-dimensional data set of the eye 120 and inspection data (data obtained by applying a predetermined inspection to the eye 120 or another eye) can be determined. For example, the positional relationship between the three-dimensional data set of the fundus of the eye 120 and the sensitivity distribution data obtained by a visual field test of the eye 120 (or, for example, electroretinogram (EGR) obtained by an electrophysiological test) can be determined. Alternatively, the positional relationship between the three-dimensional data set of the fundus of the eye 120 and the standard data (normative data) of the retinal layer thickness distribution can be determined.

[0046] In addition, the ophthalmic apparatus 100 is configured to execute processing based on part or all of the positioned three-dimensional data set. This processing is, for example, executed on one or two or more three-dimensional data sets and may include any of the following: analysis; setting of an analysis area; evaluation of analysis data; setting of an evaluation area; setting of an inspection area; setting of an imaging area; comparison with inspection data. A part of the three-dimensional data set is called a partial data set. Also, for convenience of explanation, the partial data set may be the entire three-dimensional data set.

[0047] The data that can be used in the processing of the partial data set may be a three-dimensional region of the eye 120, a three-dimensional data set, a two-dimensional map, or data generated from any of these, a combination of any two or more of these, or data generated from a combination of any two or more of these.

[0048] As shown in FIG. 1, the ophthalmic apparatus 100 includes a light source 102 (for example, a broadband light source or a wavelength-variable light source) for generating a light beam. A beam splitter (BS) 104 splits the light beam from the light source 102 into a sample light beam (measurement light) and a reference light beam (reference light). In other words, the beam splitter 104 guides a part of the light beam from the light source 102 to the sample arm 106 and another part to the reference arm 108.

[0049] The reference arm 108 includes a polarization controller 110 for adjusting the reference light beam (e.g., for maximizing the interference efficiency) and a collimator 112 for outputting the reference light beam as a parallel light beam. The reference light beam output from the collimator 112 is converged by the lens 114 and projected onto the mirror 115. The reference light beam reflected by the mirror 115 returns to the beam splitter 104 through the reference arm 108. The lens 114 and the mirror 115 are integrally movable, thereby changing the distance from the collimator 112 (in other words, changing the length of the path of the reference light beam).

[0050] The sample arm 106 projects the sample light beam onto the eye 120 as a sample through the collimator 117, the two-dimensional scanner 116, and one or more objective lenses 118. The two-dimensional scanner 116 is, for example, a galvanometer mirror scanner or a MEMS scanner. The return light of the sample light beam projected onto the eye 120 returns to the beam splitter 104 through the sample arm 106. The two-dimensional scanner 116 enables an OCT scan of a three-dimensional region of the eye 120.

[0051] The beam splitter 104 overlaps the return light of the reference light beam and the return light of the sample light beam to generate an interference light beam. The interference light beam is guided to the detector 122 and detected. Thereby, the optical echo time delay is measured from the interference spectrum.

[0052] The detection unit 122 generates a plurality of output sets based on the combined light (i.e., interferogram data) of the return light of the sample light beam supplied from the sample arm 106 and the return light of the reference light beam supplied from the reference arm 108. For example, each of the plurality of output sets generated by the detection unit 122 may correspond to the light intensity received at different wavelengths output from the light source 102. When the two-dimensional scanner 116 sequentially projects the sample light beam to a plurality of XY positions, the detected light intensity includes information regarding the reflection intensity distribution (backscattering intensity distribution) inside the eye 120 in the depth direction (Z direction) at each XY position.

[0053] In this way, a three-dimensional data set is obtained. The three-dimensional data set includes a plurality of A-scan data respectively corresponding to a plurality of XY positions. Each A-scan data represents the spectral intensity distribution at the corresponding XY position. The three-dimensional data set collected by the detection unit 122 is sent to the processing device 124.

[0054] The processing device 124 is configured to execute, for example, creation of a two-dimensional map based on the three-dimensional data set, positioning of the three-dimensional data set based on the two-dimensional map, and processing based on a partial data set of the positioned three-dimensional data set. The processing device 124 includes a processor that operates according to a processing program. A specific example of the processing device 124 will be described later.

[0055] The control device 126 controls each part of the ophthalmic device 100. For example, the control device 126 executes various controls for applying an OCT scan to a preset region of the eye 120. The control device 126 includes a processor that operates according to a control program. A specific example of the control device 126 will be described later.

[0056] Although illustration is omitted, the ophthalmic device 100 may further include a display device, an operation device, a communication device, and the like.

[0057] With reference to FIG. 2, the processing device 124 and the control device 126 will be further described. The processing device 124 includes a map creation unit 202, a positioning unit 204, and a processing execution unit 206. The control device 126 includes a scan control unit 210.

[0058] The OCT scanner 220 shown in FIG. 2 applies an OCT scan to a sample (eye 120). The OCT scanner 220 of this embodiment includes, for example, the optical element group shown in FIG. 1, that is, a light source 102, a beam splitter 104, a sample arm 106 (including a collimator 117, a two-dimensional scanner 116, an objective lens 118, etc.), a reference arm 108 (including a collimator 112, a lens 114, a mirror 115, etc.), and a detection unit 122. In some exemplary embodiments, the OCT scanner may have other configurations.

[0059] The control device 126 controls each part of the ophthalmic device 100. The control regarding the OCT scan among various controls is executed by the scan control unit 210. The scan control unit 210 of this embodiment is configured to execute the control of the OCT scanner 220, for example, control of the light source 102, control of the two-dimensional scanner 116, movement control of the lens 114 and the mirror 115, etc. The scan control unit 210 includes a processor that operates according to a scan control program.

[0060] The processing device 124 executes various data processes (calculation, analysis, measurement, image processing, etc.). The three processes described above, that is, creation of a two-dimensional map based on a three-dimensional data set, positioning of a three-dimensional data set based on the two-dimensional map, and processing based on a partial data set of the positioned three-dimensional data set are executed by the map creation unit 202, the positioning unit 204, and the processing execution unit 206, respectively.

[0061] The map creation unit 202 includes a processor that operates according to a map creation program. The positioning unit 204 includes a processor that operates according to a positioning program. The processing execution unit 206 includes a processor that operates according to a processing execution program.

[0062] Three-dimensional data collected from the eye 120 by an OCT scan is input from the OCT scanner 220 to the map creation unit 202. This OCT scan is performed by the OCT scanner 220 under the control of the scan control unit 210, targeting a preset three-dimensional region of the eye 120. Thereby, a three-dimensional data set is collected and supplied to the map creation unit 202.

[0063] The map creation unit 202 creates a two-dimensional map based on the representative intensity value of each of the plurality of A-scan data included in the three-dimensional data set. The three-dimensional data set is, for example, data before being subjected to imaging processing (such as Fourier transform) by the processing execution unit 206 or another imaging processor. The A-scan data is a spectral intensity distribution.

[0064] The process executed by the map creation unit 202 may be a process based on the method disclosed in the specification of the aforementioned Patent No. 6230023. Briefly described, this method includes a step of applying a high-pass filter to the A-scan data representing the spectral intensity distribution corresponding to a specific XY position to extract an amplitude component, and a step of determining a single estimated intensity value (representative intensity value) based on the inverse cumulative distribution function (inverse CDF) from the extracted amplitude component.

[0065] More specifically, as described in FIG. 5 and its description in the specification of Patent No. 6230023, in some exemplary embodiments, the map creation unit 202 may be configured to perform a process of applying high-pass filtering to the A-scan data, a process of downsampling (or truncating) the filtered A-scan data, a process of squaring (or taking the absolute value of) the downsampled A-scan data, a process of sorting the results (or selecting quantiles), a process of performing an operation by the inverse CDF method, and a process of determining a single estimated intensity value (representative intensity value) from the results.

[0066] In some other exemplary embodiments, the map creation unit 202 may be configured to perform a process of applying high-pass filtering to the A-scan data, a process of downsampling (or truncating) the filtered A-scan data, a process of squaring (or taking the absolute value of) the downsampled A-scan data, a process of selecting the maximum percentile value in the results, and a process of determining a single estimated intensity value (representative intensity value) from the selected maximum percentile value.

[0067] In yet some other exemplary embodiments, the map creation unit 202 may be configured to perform a process of applying high-pass filtering to the A-scan data, a process of downsampling (or truncating) the filtered A-scan data, a process of selecting the minimum percentile value and the maximum percentile value from the downsampled A-scan data, a process of squaring (or taking the absolute value of) the selected minimum percentile value and the maximum percentile value respectively, and a process of combining them (e.g., calculating an average value, or selecting a predetermined percentile value using the inverse CDF method).

[0068] For details of the map creation method exemplified above, refer to the specification of Patent No. 6230023. Also, the applicable map creation method is not limited to the above exemplification, and any method within the scope of the invention described in the specification of Patent No. 6230023 or any modification thereof can be applied.

[0069] By applying such a series of steps to each of the plurality of A-scan data included in the three-dimensional data set, a plurality of representative intensity values corresponding to a plurality of XY positions can be obtained. By mapping the correspondence between the obtained plurality of XY positions and the plurality of representative intensity values, a two-dimensional map representing the distribution of the representative intensity values on the XY plane is obtained.

[0070] The two-dimensional map created by the map creation unit 202 is input to the positioning unit 204. The positioning unit 204 positions the three-dimensional data set based on the two-dimensional map.

[0071] For example, the positioning unit 204 determines the correspondence (positional relationship) between the three-dimensional data set and the sample region by analyzing the two-dimensional map to detect an image of a predetermined location of the eye 120. Alternatively, the positioning unit 204 analyzes a two-dimensional map based on a three-dimensional data set to detect an image of a predetermined location, analyzes a two-dimensional map based on another three-dimensional data set to detect an image of the predetermined location, and determines the correspondence (positional relationship) between the two three-dimensional data sets based on the two detected images. Alternatively, the positioning unit 204 analyzes a two-dimensional map based on a three-dimensional data set to detect an image of a predetermined location, analyzes a two-dimensional map based on another three-dimensional data set to detect an image of another predetermined location, and determines the correspondence (positional relationship) between the two three-dimensional data sets based on the two detected images. Alternatively, the positioning unit 204 analyzes a two-dimensional map based on a three-dimensional data set to detect an image of a predetermined location, and determines the correspondence (positional relationship) between the three-dimensional data set and the inspection data by associating the detected image with a specific part of the inspection data.

[0072] The predetermined location to be the object of image detection may be, for example, a lesion, a blood vessel, the optic disc, the macula, sub - tissues of the fundus (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 cell layer, retinal pigment epithelium layer, Bruch's membrane, choroid, sclera, etc.), sub - tissues of the cornea (corneal epithelium, Bowman's membrane, stroma, Dua's layer, Descemet's membrane, corneal endothelium, etc.), iris, lens, zonular fibers, ciliary body, vitreous body, and any of other eye tissues.

[0073] For the detection of the image of the predetermined location, any image - processing technique can be applied. For example, an image classification method, an image detection method, an image recognition method, an image segmentation method, deep learning, etc. can be applied. As an example, the positioning unit 204 can analyze a two - dimensional map based on a three - dimensional data set collected by applying an OCT scan to the fundus to detect an image of the optic disc, and perform positioning of the three - dimensional data set based on the detected optic disc image.

[0074] In another example, the control device 126 causes a display device (not shown) to display a two - dimensional map. The user uses an operation device (not shown) to specify a desired region within the displayed two - dimensional map. The positioning unit 204 can perform positioning of the three - dimensional data set based on the region specified by the user in the displayed two - dimensional map.

[0075] Note that the data that can be used for the positioning of the three - dimensional data set is not limited to the two - dimensional map. For example, data generated from the two - dimensional map, data used in the process before the creation of the two - dimensional map (three - dimensional region, three - dimensional data set, etc.) and / or data generated from this data may be referred to for the positioning of the three - dimensional data set.

[0076] The three - dimensional data set positioned by the positioning unit 204 is input to the processing execution unit 206. The processing execution unit 206 executes processing based on a partial data set of this three - dimensional data set.

[0077] Examples of various processes executable by the process execution unit 206, including analysis, evaluation, setting of regions of interest (regions to be analyzed, regions to be evaluated), setting of inspection areas, setting of imaging areas, and comparison with inspection data, will be described below. Note that any two or more of these examples can be combined. The process execution unit 206 includes elements shown in two or more of the combined examples. For example, the process execution unit 206 may include some or all of the elements shown in FIGS. 3A to 6.

[0078] The process execution unit 206A shown in FIG. 3A includes an analysis unit 2061 configured to execute a predetermined analysis process based on a partial data set of a three-dimensional data set.

[0079] For example, the analysis unit 2061 performs layer thickness analysis. The site to be subjected to layer thickness analysis may be any eye tissue such as, for example, the retina, one sub-tissue of the retina, a combination of two or more sub-tissues of the retina, the choroid, one sub-tissue of the choroid, a combination of two or more sub-tissues of the choroid, the cornea, one sub-tissue of the cornea, a combination of two or more sub-tissues of the cornea, the lens, etc. Layer thickness analysis includes, for example, segmentation for identifying a region (partial data set) of a three-dimensional data set corresponding to such a target site, and measurement of the thickness at at least one location of the identified partial data set. Further, the analysis unit 2061 can determine the position in the eye 120 corresponding to each layer thickness measurement position based on the result of the positioning process executed by the positioning unit 204. This makes it possible to grasp which part of the eye 120 the layer thickness has been measured.

[0080] The analysis process executable by the analysis unit 2061 is not limited to layer thickness analysis. As one example, there is dimension analysis for measuring the dimensions of tissue. The tissue to be subjected to dimension analysis may be, for example, the optic nerve head (cup diameter, disc diameter, rim diameter, depth, etc.), a lesion (area, volume, length, etc.), a blood vessel (thickness, length, etc.), and the like. Dimension analysis includes, for example, segmentation for identifying a region (partial dataset) of a three-dimensional dataset corresponding to such a target tissue, and measurement of the dimensions of the identified partial dataset. Further, the analysis unit 2061 can determine the position in the eye 120 corresponding to the dimension measurement position (target tissue) based on the result of the positioning process executed by the positioning unit 204. Thereby, it becomes possible to grasp at which location in the eye 120 the dimension was measured.

[0081] As another example of the analysis process, there is shape analysis for measuring the shape of tissue. The tissue to be subjected to shape analysis may be, for example, the optic nerve head, a lesion, a blood vessel, and the like. Shape analysis includes, for example, segmentation for identifying a region (partial dataset) of a three-dimensional dataset corresponding to such a target tissue, and identification of the shape of the identified partial dataset. Identification of the shape includes, for example, extraction of the contour of the partial dataset and processing for obtaining the shape of the contour (circularity, roundness, ellipticity, cylindricity, etc.). Further, the analysis unit 2061 can determine the position in the eye 120 corresponding to the shape measurement position (target tissue) based on the result of the positioning process executed by the positioning unit 204. Thereby, it becomes possible to grasp at which location in the eye 120 the shape was measured.

[0082] In addition to such shape measurement, orientation analysis for measuring the orientation of the target tissue can be performed. Orientation analysis includes, for example, processing for obtaining a figure (e.g., an approximate ellipse) that approximates the shape of the contour of the partial dataset, and processing for obtaining the orientation of this approximate figure (e.g., the orientation of the major axis of the approximate ellipse). In other examples, orientation analysis includes processing for obtaining a specific parameter (e.g., the maximum diameter) of the partial dataset, and processing for obtaining the orientation based on this parameter (e.g., the orientation of the line segment indicating the maximum diameter).

[0083] The processing execution unit 206B shown in FIG. 3B includes an analysis unit 2061 configured to execute a predetermined analysis process based on a partial data set of the three-dimensional data set, and an evaluation unit 2062 configured to execute a predetermined evaluation process based on the data obtained by this analysis process. The analysis unit 2061 may be the same as the analysis unit 2061 in FIG. 3A.

[0084] The evaluation unit 2062 can, for example, evaluate whether the data of the eye 120 is normal (whether there is a suspicion of a disease), determine the degree of the disease, or determine the degree of suspicion of the disease by comparing the data obtained by the analysis unit 2061 with the normative data.

[0085] The evaluation process is not limited to comparison with normative data, and may include any evaluation process using statistics, any evaluation process using calculations, and the like.

[0086] The processing execution unit 206C shown in FIG. 3C includes a region of interest setting unit 2063 configured to set a partial data set (region of interest which is at least a part of the three-dimensional data set) to which the analysis process is applied, and an analysis unit 2061 configured to execute a predetermined analysis process based on the set partial data set. The analysis unit 2061 may be the same as the analysis unit 2061 in FIG. 3A.

[0087] The region of interest setting unit 2063 sets the region of interest, for example, by analyzing the three-dimensional data set. The setting of the region of interest includes, for example, segmentation for identifying the region of interest in the three-dimensional data set.

[0088] In other examples, the control device 126 causes a display device (not shown) to display a two-dimensional map (or any image based on a three-dimensional dataset). The user uses an operation device (not shown) to specify a desired region within the displayed two-dimensional map (or image). The region of interest setting unit 2063 can set a region of interest in the three-dimensional dataset based on the region specified by the user in the displayed two-dimensional map (or image).

[0089] The eye region corresponding to the region of interest may include, for example, a lesion, blood vessels, optic disc, macula, sub-tissues of the fundus (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, Bruch's membrane, choroid, sclera, etc.), sub-tissues of the cornea (corneal epithelium, Bowman's membrane, stroma, Dua's layer, Descemet's membrane, corneal endothelium, etc.), iris, lens, zonular fibers, ciliary body, vitreous body, and any of other eye tissues.

[0090] The evaluation unit 2062 can be combined with the processing execution unit 206C shown in FIG. 3C. The evaluation unit 2062 in this example executes a predetermined evaluation process based on the data obtained by the analysis process executed by the analysis unit 2061 based on the partial dataset set by the region of interest setting unit 2063. The evaluation unit 2062 in this example may be the same as the evaluation unit 2062 in FIG. 3B.

[0091] The processing execution unit 206D shown in FIG. 3D includes an analysis unit 2061 configured to execute a predetermined analysis process based on a partial dataset of a three-dimensional dataset, a region of interest setting unit 2063 configured to set partial data (region of interest which is at least a part of the analysis data) to which an evaluation process is applied in the data obtained by this analysis process, and an evaluation unit 2062 configured to execute a predetermined evaluation process based on the set region of interest. The analysis unit 2061 may be the same as the analysis unit 2061 in FIG. 3A. The evaluation unit 2062 may be the same as the evaluation unit 2062 in FIG. 3B.

[0092] The region of interest setting unit 2063 identifies a partial data set by analyzing, for example, a three-dimensional data set, and sets partial data of the analysis data corresponding to this partial data set as the region of interest. The setting of the partial data set includes, for example, segmentation.

[0093] In another example, the region of interest setting unit 2063 sets the region of interest by analyzing the analysis data obtained by the analysis unit 2061. As an example, the region of interest setting unit 2063 executes a process of detecting characteristic partial data in the analysis data and a process of setting the region of interest based on the detected partial data.

[0094] In still another example, the control device 126 causes a display device (not shown) to display a two-dimensional map (or any image based on a three-dimensional data set, or analysis data). The user designates a desired region within the displayed two-dimensional map (or image or analysis data) using an operation device (not shown). The region of interest setting unit 2063 can set the region of interest in the analysis data based on the region designated by the user in the displayed two-dimensional map (or image or analysis data).

[0095] The process execution unit 206E shown in FIG. 4 includes an inspection area setting unit 2064 configured to execute setting of an application area (inspection area) of a predetermined inspection for the eye 120. The predetermined inspection may be any inspection, and examples include OCT scan, visual field test, microperimetry, electrophysiological test, and the like.

[0096] For example, the inspection area setting unit 2064 executes a process of analyzing any one or more of a two-dimensional map, a three-dimensional data set, and data generated based on at least one of these to determine a region of interest of the eye 120, and a process of setting the inspection area based on the determined region of interest. The region of interest is, for example, a lesion, a specific site, a specific tissue, or the like. Typically, the inspection area is set to include at least a part of the region of interest.

[0097] In one example, the inspection area setting unit 2064 includes a segmentation for a two-dimensional map (or a three-dimensional data set), a process of converting an area in the two-dimensional map identified thereby into a region of interest in the eye 120 based on the result of the positioning process, and a process of setting an inspection area based on this region of interest.

[0098] When the inspection area set by the inspection area setting unit 2064 is an OCT scan application area, information indicating this inspection area can be provided to the scan control unit 210. The scan control unit 210 controls the OCT scanner 220 to apply an OCT scan to this inspection area.

[0099] When the inspection area set by the inspection area setting unit 2064 is an OCT scan application area, information indicating this inspection area can be provided to another OCT device (inspection device 130) via the communication device described above.

[0100] When the inspection area set by the inspection area setting unit 2064 is an application area for a certain inspection, information indicating this inspection area can be provided to the inspection device 130 corresponding to this inspection via the communication device described above.

[0101] The processing execution unit 206F shown in FIG. 5 includes an imaging area setting unit 2065 configured to set a partial data set (an imaging area that is at least a part of a three-dimensional data set) to which imaging processing is applied, and an image data generation unit 2066 configured to generate image data of the set imaging area.

[0102] The imaging processing includes at least a Fourier transform. Examples of the imaging processing include general OCT image construction, motion contrast (such as OCT angiography), phase image construction, polarization image construction, and the like.

[0103] For example, the imaging area setting unit 2065 analyzes any one or more of a two-dimensional map, a three-dimensional dataset, and data generated based on at least one of these, to determine the attention area of the eye 120, and executes a process of setting an imaging area based on the determined attention area. The attention area is, for example, a lesion, a specific part, a specific tissue, etc. Typically, the imaging area is set to include at least a part of the attention area.

[0104] In one example, the imaging area setting unit 2065 includes segmentation of a two-dimensional map (or a three-dimensional dataset), a process of converting the region in the two-dimensional map identified thereby into an attention area in the eye 120 based on the result of the positioning process, and a process of setting an imaging area based on this attention area.

[0105] The image data generation unit 2066 is configured to generate image data based on the data collected by the OCT scanner 220. For example, the image data generation unit 2066 forms image data of a tomographic image of the eye 120 based on the output from the OCT scanner 220 (sampling data, interference signal data). This image data generation process includes filter processing, fast Fourier transform (FFT), etc., similar to conventional (swept source or spectral domain) OCT. Through such processing, the reflection intensity profile (reflection intensity profile along the Z direction) in the A-line (scan path of the measurement light beam in the eye 120) corresponding to each XY position is obtained, and the image data of this A-line (A-scan image data) is formed by imaging this reflection intensity profile.

[0106] Furthermore, the image data generation unit 2066 can form a plurality of A-scan image data according to the mode of the OCT scan (deflection of the measurement light beam, movement of the A-scan position), and construct two-dimensional image data or three-dimensional image data by arranging these A-scan image data.

[0107] When a plurality of tomographic image data are obtained by raster scanning or the like, the image data generation unit 2066 can construct stack data by embedding these tomographic image data in a single three-dimensional coordinate system, and apply voxelization processing to this stack data to construct voxel data (volume data).

[0108] The image data generation unit 2066 can render the stack data or volume data. The rendering method is arbitrary and may be, for example, volume rendering, multi-planar reformation (MPR), surface rendering, etc. Also, the image data generation unit 2066 can construct a planar image (for example, a front image, an en face image) from the stack data or volume data. For example, the image data generation unit 2066 can construct a projection image by integrating the stack data or volume data along each A-line.

[0109] In this example, the image data generation unit 2066 applies imaging processing to the OCT data set (partial data set of the three-dimensional data set) included in the imaging area set by the imaging area setting unit 2065 to generate image data.

[0110] In another example, the image data generation unit 2066 applies imaging processing to the three-dimensional data set to generate image data. Further, the processing execution unit 206F extracts partial image data corresponding to the imaging area set by the imaging area setting unit 2065 from this image data. Extraction of the partial image data includes, for example, clipping, cropping, or trimming and the like.

[0111] The processing execution unit 206F shown in FIG. 5 has an image construction function (image data generation unit 2066), but an OCT device (ophthalmic device) in other exemplary embodiments may not have an image construction function. In this case, information indicating the imaging area set by the imaging area setting unit 2065 can be provided to an external device (including an imaging processor) via a communication device (not shown).

[0112] In the example shown in FIG. 6, a reception unit 140 is provided that receives data (inspection data) obtained from the eye 120 by a predetermined inspection different from OCT. Further, the processing execution unit 206G in this example includes a comparison unit 2067. The comparison unit 2067 is configured to execute a predetermined comparison process between at least a part of the inspection data received by the reception unit 140 and at least a part of the three-dimensional data set collected by the OCT scanner 220.

[0113] The reception unit 140 receives inspection data obtained from the eye 120 from the outside (for example, an ophthalmic device, an image archiving system, a recording medium). The reception unit 140 may include, for example, a communication device or a drive device.

[0114] The inspection data may be data obtained by any modality or inspection. Examples of the inspection data include sensitivity distribution data obtained by a visual field test of the eye 120, electroretinogram (EGR) obtained by an electrophysiological test, and tear distribution data obtained by tear imaging (anterior eye imaging).

[0115] The comparison unit 2067 may be configured to execute, for example, registration between a two-dimensional map based on the result of the positioning process executed by the positioning unit 204 and the inspection data, registration between the three-dimensional data set and the inspection data based on the result of this registration, and a predetermined comparison process based on the registered three-dimensional data set (at least a part thereof) and the inspection data.

[0116] This comparison process may include any one or more of, for example, comparison between a three-dimensional data set and inspection data, comparison between a two-dimensional map based on the three-dimensional data set and inspection data, comparison between image data based on the three-dimensional data set and inspection data, comparison between analysis data of the two-dimensional map and inspection data, comparison between analysis data of the image data and inspection data, comparison between the three-dimensional data set and processed data of the inspection data, comparison between a two-dimensional map based on the three-dimensional data set and processed data of the inspection data, comparison between image data based on the three-dimensional data set and processed data of the inspection data, comparison between analysis data of the two-dimensional map and processed data of the inspection data, and comparison between analysis data of the image data and processed data of the inspection data.

[0117] In addition to the data processing described above, the processing device 124 may be capable of performing various data processes. The processing device 124 can process data (OCT data) acquired using an OCT scan. The OCT data is, for example, interference signal data (e.g., at least a part of a three-dimensional data set), a reflection intensity profile, or image data.

[0118] The processing device 124 may be capable of processing data other than OCT data. For example, when the ophthalmic device 100 has a data acquisition device other than the OCT scanner 220, the processing device 124 can process the data acquired by this data acquisition device. The ophthalmic device employed as the data acquisition device may be, for example, an ophthalmic imaging device (ophthalmic imaging apparatus) such as a fundus camera, a scanning laser ophthalmoscope (SLO), a surgical microscope, a slit lamp microscope, etc. As another example, there are ophthalmic measurement devices such as a refractometer, a keratometer, a tonometer, an axial length measuring device, a specular microscope, a wavefront analyzer, a perimeter, etc. Further, when the OCT device is an arbitrary medical device (that is, when the OCT device is a device used in an arbitrary medical department), the medical device employed as the data acquisition device may be, for example, an arbitrary medical imaging device and / or an arbitrary medical examination device. Also, for an OCT device used in a field other than medicine, a data acquisition device corresponding to that field can be adopted.

[0119] Some examples of the operations that the ophthalmic device 100 having the configuration exemplified above can perform will be described.

[0120] Referring to FIG. 7. In this example, first, the scan control unit 210 controls the OCT scanner 220 so as to apply an OCT scan to the eye 120 and collect a three-dimensional data set (S1).

[0121] Next, the map creation unit 202 creates a two-dimensional map based on the respective representative intensity values of a plurality of A-scan data included in the three-dimensional data set collected in step S1 (S2).

[0122] Next, the positioning unit 204 performs positioning of the three-dimensional data set collected in step S1 based on the two-dimensional map created in step S2 (S3).

[0123] Next, the process execution unit 206 executes a process based on the partial data set of the three-dimensional data set positioned in step S3 (S4).

[0124] The process in step S4 may include, for example, any one or more of a predetermined analysis process, a predetermined evaluation process, setting of an area of interest, setting of an inspection area, setting of an imaging area, generation of image data, and a predetermined comparison process with inspection data. In other words, the process execution unit 206 may include any one or more of an analysis unit 2061, an evaluation unit 2062, an area of interest setting unit 2063, an inspection area setting unit 2064, an imaging area setting unit 2065, an image data generation unit 2066, and a comparison unit 2067.

[0125] In one example, when the process in step S4 includes setting of an OCT scan application area (setting of an inspection area), information indicating the set inspection area is provided to the scan control unit 210. The scan control unit 210 controls the OCT scanner 220 to apply an OCT scan targeting the set inspection area and collect a data set. The process execution unit 206 (image data generation unit 2066) generates image data from the collected data set. The control device 126 can display the generated image data on a display device (not shown). This display device may be, for example, any one of an element of the ophthalmic device 100, peripheral equipment of the ophthalmic device 100, and a device connectable to the ophthalmic device 100 via a communication line (such as a telemedicine device). Further, the control device 126 can store the generated image data in a storage device (not shown). This storage device may be, for example, any one of an element of the ophthalmic device 100, peripheral equipment of the ophthalmic device 100, a device connectable to the ophthalmic device 100 via a communication line, and a portable recording medium.

[0126] Some effects of the ophthalmic device (OCT device) 100 of this aspect will be described.

[0127] The ophthalmic device 100 of this aspect includes an OCT scanner 220, a map creation unit 202, a positioning unit 204, and a process execution unit 206. The OCT scanner 220 applies an OCT scan to a sample (eye 120) to collect a three-dimensional data set. The map creation unit 202 creates a two-dimensional map based on the representative intensity value of each of a plurality of A-scan data included in this three-dimensional data set. The positioning unit 204 positions the three-dimensional data set based on this two-dimensional map. The process execution unit 206 executes a process based on at least a partial data set of the positioned three-dimensional data set.

[0128] According to such an ophthalmic device 100, based on a two-dimensional map created from a three-dimensional data set collected by an OCT scan, the three-dimensional data set can be positioned, and a process based on this positioned three-dimensional data set can be performed. Therefore, without performing three-dimensional image construction or landmarking as in the inventions described in Patent Document 1 (U.S. Patent No. 7,884,945) and Patent Document 2 (U.S. Patent No. 8,405,834), it is possible to execute a process related to a desired region of the sample. Accordingly, it is possible to improve the efficiency of resources required for the process and shorten the processing time, and it is possible to improve the efficiency of OCT data processing. Thereby, for example, it is also possible to suitably perform real-time processing.

[0129] In the ophthalmic device 100 of this aspect, the process execution unit 206 may include an analysis unit 2061 configured to execute a predetermined analysis process based on at least a partial data set of the positioned three-dimensional data set.

[0130] According to such a configuration, without performing three-dimensional image construction or landmarking as in the prior art, it is possible to execute an analysis related to a desired region of the sample. Therefore, it is possible to improve the efficiency of resources required for the analysis and shorten the processing time, and it is possible to improve the efficiency of the analysis. Thereby, for example, it is also possible to suitably perform real-time analysis.

[0131] In the ophthalmic apparatus 100 of this aspect, the process execution unit 206 may include an evaluation unit 2062 configured to execute a predetermined evaluation process based on the analysis data obtained by the analysis unit 2061.

[0132] According to such a configuration, it is possible to execute an evaluation regarding a desired region of the sample without performing three-dimensional image construction or landmark as in the prior art. Therefore, it is possible to improve the efficiency of resources required for the evaluation and shorten the processing time, and it is possible to improve the efficiency of the evaluation. Thereby, for example, it is also possible to suitably perform real-time evaluation.

[0133] In the ophthalmic apparatus 100 of this aspect, the process execution unit 206 may include an interested region setting unit 2063 configured to set an interested region (analysis target region and / or evaluation target region).

[0134] According to such a configuration, it is possible to set an interested region without performing three-dimensional image construction or landmark as in the prior art. Therefore, it is possible to improve the efficiency of resources required for setting the interested region and shorten the processing time, and it is possible to improve the efficiency thereof. Thereby, for example, it is also possible to suitably perform real-time setting of the interested region.

[0135] In the ophthalmic apparatus 100 of this aspect, the process execution unit 206 may include an inspection area setting unit 2064 configured to set an application area (inspection area) of a predetermined inspection for the sample.

[0136] According to such a configuration, it is possible to set an inspection area without performing three-dimensional image construction or landmark as in the prior art. Therefore, it is possible to improve the efficiency of resources required for setting the inspection area and shorten the processing time, and it is possible to improve the efficiency thereof. Thereby, for example, it is also possible to suitably perform real-time setting of the inspection area.

[0137] In the ophthalmic device 100 of the present aspect, the process execution unit 206 may include an imaging area setting unit 2065 configured to set a partial data set (imaging area) to which a predetermined imaging process is applied.

[0138] According to such a configuration, it is possible to set the imaging area without performing three-dimensional image construction or landmarking as in the prior art. Therefore, it is possible to improve the efficiency of resources required for imaging area setting and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, it is also possible to suitably perform real-time imaging area setting.

[0139] The ophthalmic device 100 of the present aspect may include means for preparing inspection data obtained from a sample by a predetermined inspection different from OCT. This inspection data preparation means includes, for example, means for receiving inspection data (reception unit 140), or means for applying an inspection to a sample to obtain inspection data. Further, the process execution unit 206 may include a comparison unit 2067 configured to execute a predetermined comparison process between the inspection data and at least a part of the three-dimensional data set.

[0140] According to such a configuration, it is possible to set a comparison process between the inspection data and the OCT data without performing three-dimensional image construction or landmarking as in the prior art. Therefore, it is possible to improve the efficiency of resources required for the comparison process and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, it is also possible to suitably perform real-time comparison processing.

[0141] As described above, the sample of the present aspect is a living eye, but it is possible to apply the same functions and configurations to an OCT device targeting samples other than living eyes. That is, any matter (function, hardware configuration, software configuration, etc.) related to the ophthalmic device 100 can be combined with an OCT device in any aspect. At this time, the actions and effects corresponding to the combined matters are exhibited.

[0142] Some exemplary aspects relate to a method of controlling an OCT device including an OCT scanner and a processor that applies an OCT scan to a sample. The control method may at least include the following steps: controlling the OCT scanner to collect a three-dimensional data set from the sample; controlling the processor to create a two-dimensional map based on the respective representative intensity values of a plurality of A-scan data included in the three-dimensional data set; controlling the processor to position the three-dimensional data set based on the two-dimensional map; controlling the processor to execute processing based on at least a partial data set of the positioned three-dimensional data set.

[0143] Any matter (functions, hardware configuration, software configuration, etc.) related to the ophthalmic device 100 can be combined with the control method of this aspect. At this time, the actions and effects corresponding to the combined matters are achieved.

[0144] Some exemplary aspects relate to a program that causes a computer to execute such a control method of an OCT device. Any of the matters described with respect to the ophthalmic device 100 can be combined with this program. Further, some exemplary aspects relate to a computer-readable non-transitory recording medium storing such a program. Any of the matters described with respect to the ophthalmic device 100 can be combined with this recording medium.

[0145] Some exemplary aspects relate to an apparatus (OCT data processing apparatus) for processing data collected using OCT. This OCT data processing apparatus may include at least the following elements: a receiving unit that receives a three-dimensional data set collected by applying an OCT scan to a sample; a map creating unit that creates a two-dimensional map based on representative intensity values of each of a plurality of A-scan data included in this three-dimensional data set; a positioning unit that positions the three-dimensional data set based on this two-dimensional map; and a processing execution unit that executes processing based on at least a partial data set of the positioned three-dimensional data set.

[0146] That is, this OCT data processing apparatus includes an element (receiving unit) that receives a three-dimensional data set obtained by an OCT scan from the outside (for example, an OCT apparatus, an image archiving system, a recording medium) instead of (or in addition to) the OCT scanner 220 of the aforementioned OCT apparatus (ophthalmic apparatus) 100. The receiving unit may include, for example, a communication device or a drive device.

[0147] It is possible to combine any matters (functions, hardware configurations, software configurations, etc.) related to the ophthalmic apparatus 100 with the OCT data processing apparatus of this aspect. At this time, actions and effects corresponding to the combined matters are exhibited.

[0148] Some exemplary aspects relate to a method for controlling an OCT data processing apparatus including a processor. This control method may include at least the following steps: a step of controlling the processor to receive a three-dimensional data set collected by applying an OCT scan to a sample; a step of controlling the processor to create a two-dimensional map based on representative intensity values of each of a plurality of A-scan data included in this three-dimensional data set; a step of controlling the processor to position the three-dimensional data set based on this two-dimensional map; and a step of controlling the processor to execute processing based on at least a partial data set of the positioned three-dimensional data set.

[0149] Any matters (functions, hardware configurations, software configurations, etc.) related to the ophthalmic device 100 can be combined with the control method of this aspect. At this time, the actions and effects corresponding to the combined matters are achieved.

[0150] Some exemplary aspects relate to a program that causes a computer to execute a control method of such an OCT data processing device. Any of the matters described with respect to the ophthalmic device 100 can be combined with this program. Further, some exemplary aspects relate to a computer-readable non-transitory recording medium having such a program recorded thereon. Any of the matters described with respect to the ophthalmic device 100 can be combined with this recording medium.

[0151] Some exemplary aspects of an OCT device (for example, the ophthalmic device 100), some exemplary aspects of a control method of an OCT device, some exemplary aspects of an OCT data processing device, or some exemplary aspects of a control method of an OCT data processing device provide a method for processing OCT data. This OCT data processing method may at least include the following steps: a step of preparing a three-dimensional data set collected from a sample; a step of creating a two-dimensional map based on the respective representative intensity values of a plurality of A-scan data included in this three-dimensional data set; a step of positioning the three-dimensional data set based on this two-dimensional map; and a step of executing processing based on at least a partial data set of the positioned three-dimensional data set.

[0152] Any matters (functions, hardware configurations, software configurations, etc.) related to the ophthalmic device 100 can be combined with the OCT data processing method of this aspect. At this time, the actions and effects corresponding to the combined matters are achieved.

[0153] Some exemplary aspects relate to a program that causes a computer to execute such an OCT data processing method. For this program, it is possible to combine any of the matters described with respect to the ophthalmic device 100. Further, some exemplary aspects relate to a computer-readable non-transitory recording medium having such a program recorded thereon. For this recording medium, it is possible to combine any of the matters described with respect to the ophthalmic device 100.

[0154] In some aspects, the non-transitory recording medium having the program recorded thereon may be in any form, and examples thereof include a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like.

[0155] <Second Exemplary Aspect> FIGS. 8 and 9 illustrate the configuration of an OCT device (ophthalmic device) 150 according to one exemplary aspect. The ophthalmic device 150 provides OCT data processing in addition to OCT imaging.

[0156] The ophthalmic device 150 is configured to apply an OCT scan to a sample (eye) to collect a first three-dimensional data set and a second three-dimensional data set. Here, the first three-dimensional region, which is the application area (target) of the OCT scan for collecting the first three-dimensional data set, and the second three-dimensional region, which is the application area (target) of the OCT scan for collecting the second three-dimensional data set, may coincide with each other, may partially coincide with each other, or may not have a common region. Further, the ophthalmic device 150 may collect three or more three-dimensional data sets. In this way, the ophthalmic device 150 collects at least two three-dimensional data sets by applying an OCT scan to a sample. Also, considering eye movement and the like, the region where the OCT scan targeted at a certain three-dimensional region is actually applied does not necessarily coincide with the three-dimensional region, but by using fixation, tracking, or the like, a region substantially coinciding with the three-dimensional region can be scanned.

[0157] Furthermore, the ophthalmic apparatus 150 is configured to create a first two-dimensional map based on the respective representative intensity values of a plurality of A-scan data included in the first three-dimensional data set, and to create a second two-dimensional map based on the respective representative intensity values of a plurality of A-scan data included in the second three-dimensional data set. The process of creating a two-dimensional map from a three-dimensional data set is executed, for example, in the same manner as that of the first exemplary embodiment.

[0158] Furthermore, the ophthalmic apparatus 150 is configured to execute a process based on at least one of at least a first partial data set of the first three-dimensional data set and at least a second partial data set of the second three-dimensional data set, based on the first two-dimensional map and the second two-dimensional map. Hereinafter, such an ophthalmic apparatus 150 will be described in more detail.

[0159] As shown in FIG. 8, the ophthalmic apparatus 150 includes an optical element group similar to that of the ophthalmic apparatus 100 (FIG. 1) of the first exemplary embodiment. The ophthalmic apparatus 150 includes a light source 102 (for example, a broadband light source or a wavelength-variable light source) for generating a light beam. A beam splitter (BS) 104 splits the light beam from the light source 102 into a sample light beam (measurement light) and a reference light beam (reference light). In other words, the beam splitter 104 guides a part of the light beam from the light source 102 to the sample arm 106, and guides the other part to the reference arm 108.

[0160] The reference arm 108 includes a polarization controller 110 for adjusting the reference light beam (for example, for maximizing the interference efficiency), and a collimator 112 for outputting the reference light beam as a parallel light beam. The reference light beam output from the collimator 112 is made into a converging light beam by the lens 114 and projected onto the mirror 115. The reference light beam reflected by the mirror 115 returns to the beam splitter 104 through the reference arm 108. The lens 114 and the mirror 115 are integrally movable, whereby the distance from the collimator 112 is changed (in other words, the length of the path of the reference light beam is changed).

[0161] The sample arm 106 projects a sample light beam onto the eye 120 as a sample via a collimator 117, a two-dimensional scanner 116, and one or more objective lenses 118. The two-dimensional scanner 116 is, for example, a galvanometer mirror scanner or a MEMS scanner. The return light of the sample light beam projected onto the eye 120 returns to the beam splitter 104 through the sample arm 106. The two-dimensional scanner 116 enables an OCT scan of a three-dimensional region of the eye 120.

[0162] The beam splitter 104 superimposes the return light of the reference light beam and the return light of the sample light beam to generate an interference light beam. The interference light beam is guided to the detector 122 and detected. Thereby, the optical echo time delay is measured from the interference spectrum.

[0163] The detector 122 generates a plurality of output sets based on the combination (i.e., interferogram data) of the return light of the sample light beam supplied from the sample arm 106 and the return light of the reference light beam supplied from the reference arm 108. For example, each of the plurality of output sets generated by the detector 122 may correspond to the light intensity received at different wavelengths output from the light source 102. When the sample light beam is sequentially projected onto a plurality of XY positions by the two-dimensional scanner 116, the detected light intensity includes information on the reflection intensity distribution (backscattering intensity distribution) inside the eye 120 in the depth direction (Z direction) at each XY position.

[0164] In this way, a three-dimensional data set is obtained. The three-dimensional data set includes a plurality of A-scan data respectively corresponding to a plurality of XY positions. Each A-scan data represents the spectral intensity distribution at the corresponding XY position. The three-dimensional data set collected by the detector 122 is sent to the processing device 160.

[0165] The processing device 160 is configured to execute, for example, creation of a 2D map based on a 3D data set and processing related to the 3D data set based on the 2D map. The processing device 160 includes a processor that operates according to a processing program. A specific example of the processing device 160 will be described later.

[0166] The control device 170 controls each part of the ophthalmic device 150. For example, the control device 170 executes various controls for applying an OCT scan to a preset region of the eye 120. The control device 170 includes a processor that operates according to a control program. A specific example of the control device 170 will be described later.

[0167] Although illustration is omitted, the ophthalmic device 150 may further include a display device, an operation device, a communication device, and the like.

[0168] With reference to FIG. 9, the processing device 160 and the control device 170 will be further described. The processing device 160 includes a map creation unit 252 and a processing execution unit 256. The control device 170 includes a scan control unit 260.

[0169] The OCT scanner 270 shown in FIG. 9 applies an OCT scan to a sample (eye 120). The OCT scanner 270 of this embodiment includes, for example, the optical element group shown in FIG. 8, that is, a light source 102, a beam splitter 104, a sample arm 106 (including a collimator 117, a 2D scanner 116, an objective lens 118, etc.), a reference arm 108 (including a collimator 112, a lens 114, a mirror 115, etc.), and a detection unit 122. In some exemplary embodiments, the OCT scanner may have other configurations.

[0170] The control device 170 controls each part of the ophthalmic device 150. Among various controls, the control related to the OCT scan is executed by the scan control unit 260. The scan control unit 260 of this embodiment is configured to execute the control of the OCT scanner 270, for example, control of the light source 102, control of the two-dimensional scanner 116, movement control of the lens 114 and the mirror 115, etc. The scan control unit 260 includes a processor that operates according to a scan control program.

[0171] The processing device 160 executes various data processes (calculation, analysis, measurement, image processing, etc.). The above-mentioned two processes, that is, the creation of a two-dimensional map based on the three-dimensional data set and the process related to the three-dimensional data set based on the two-dimensional map are executed by the map creation unit 252 and the process execution unit 252, respectively.

[0172] The map creation unit 252 includes a processor that operates according to a map creation program. The process execution unit 256 includes a processor that operates according to a process execution program.

[0173] The three-dimensional data collected from the eye 120 by the OCT scan is input from the OCT scanner 270 to the map creation unit 252. In this embodiment, targeting the first three-dimensional region of the preset eye 120, the OCT scanner 270 executes an OCT scan under the control of the scan control unit 260, and targeting the preset second three-dimensional region, the OCT scanner 270 executes an OCT scan under the control of the scan control unit 260. Thereby, the first three-dimensional data set and the second three-dimensional data set are collected and supplied to the map creation unit 252.

[0174] The map creation unit 252 creates a first two-dimensional map based on the respective representative intensity values of a plurality of A-scan data included in the first three-dimensional data set. The first three-dimensional data set is data before imaging processing (such as Fourier transform) is performed by the image data generation unit 206. The A-scan data is a spectral intensity distribution. In the same manner, a second two-dimensional map is created from the second three-dimensional data set. The process executed by the map creation unit 252 may be the same as that of the map creation unit 202 in the first exemplary embodiment.

[0175] The first two-dimensional map and the second two-dimensional map created by the map creation unit 252 are input to the process execution unit 256. The process execution unit 256 executes a process based on these two-dimensional maps and based on a partial data set of the first three-dimensional data set and / or a partial data set of the second three-dimensional data set. Note that, similar to the first exemplary embodiment, in this exemplary embodiment, the partial data set of the three-dimensional data set may be a part or the whole of this three-dimensional data set.

[0176] Hereinafter, among various processes executable by the process execution unit 256, examples of analysis, evaluation, setting of a region of interest (analysis target region, evaluation target region), setting of an inspection area, setting of an imaging area, comparison with inspection data, registration, tracking, and panoramic OCT imaging (mosaic OCT imaging, montage OCT imaging) will be described. Note that it is possible to combine any two or more of these examples. The process execution unit 256 includes elements shown in two or more of the combined examples. For example, the process execution unit 256 may include some or all of the elements shown in FIGS. 10A to 16.

[0177] The process execution unit 256A shown in FIG. 10A includes an analysis unit 2561. The analysis unit 2561 is configured to execute a predetermined analysis process based on at least one of a first partial data set of the first three-dimensional data set and a second partial data set of the second three-dimensional data set, based on the first two-dimensional map and the second two-dimensional map.

[0178] For example, the analysis unit 2561 performs layer thickness analysis. The site to be subjected to layer thickness analysis may be any eye tissue such as, for example, the retina, one sub-tissue of the retina, a combination of two or more sub-tissues of the retina, the choroid, one sub-tissue of the choroid, a combination of two or more sub-tissues of the choroid, the cornea, one sub-tissue of the cornea, a combination of two or more sub-tissues of the cornea, the lens, and the like. The layer thickness analysis includes, for example, segmentation for identifying a region (partial data set) of a three-dimensional data set corresponding to such a target site, and measurement of the thickness at at least one location of the identified partial data set.

[0179] The analysis unit 2561 may be capable of performing positioning processing similar to that of the positioning unit 204 in the first exemplary embodiment, and based on the result, can determine the position in the eye 120 corresponding to each layer thickness measurement position. Thereby, it becomes possible to grasp which part of the eye 120 has its layer thickness measured.

[0180] The analysis unit 2561 can apply such layer thickness analysis to at least one of the first partial data set and the second partial data set. The layer thickness analysis applied to only one of the first partial data set and the second partial data set may be, for example, the same as the layer thickness analysis in the first exemplary embodiment.

[0181] When applying layer thickness analysis to both the first partial data set and the second partial data set, if at least a part of the region of the eye 120 corresponding to the first partial data set and at least a part of the region of the eye 120 corresponding to the second partial data set are the same, the analysis unit 2561 can, for example, obtain the change in layer thickness in the common region. That is, the analysis unit 2561 can obtain the difference (such as difference, ratio, etc.) between the layer thickness of the common region based on the first partial data set and the layer thickness of the common region based on the second partial data set. When the time point at which the first three-dimensional data set is collected is different from the time point at which the second three-dimensional data set is collected, the difference thus obtained represents the temporal change (time-series change, change over time) of the layer thickness.

[0182] Here, an example of obtaining the time-series change of a predetermined parameter value (layer thickness value) based on two three-dimensional datasets (a first three-dimensional dataset and a second three-dimensional dataset) collected at different points in time has been described. However, it is also possible to perform a similar time-series analysis based on three or more three-dimensional datasets.

[0183] When layer thickness analysis is applied to both the first partial dataset and the second partial dataset, if at least a part of the region of the eye 120 corresponding to the first partial dataset is different from at least a part of the region of the eye 120 corresponding to the second partial dataset, the analysis unit 2561 can, for example, synthesize the layer thickness distribution based on the first partial dataset and the layer thickness distribution based on the second partial dataset to obtain a layer thickness distribution over a wider range.

[0184] The analysis processes executable by the analysis unit 2561 are not limited to layer thickness analysis. As one example, there is dimension analysis for measuring the dimensions of tissues. The tissues to be subjected to dimension analysis may be, for example, the optic nerve head (cup diameter, disc diameter, rim diameter, depth, etc.), lesion parts (area, volume, length, etc.), blood vessels (thickness, length, etc.), and the like. Dimension analysis includes, for example, segmentation for specifying a region (partial dataset) of a three-dimensional dataset corresponding to such a target tissue, and measurement of the dimensions of the specified partial dataset. Any of the above matters regarding layer thickness analysis can be applied to dimension analysis.

[0185] As another example of the analysis process, there is shape analysis for measuring the shape of tissue. The tissue to be subjected to shape analysis may be, for example, the optic nerve head, a lesion, a blood vessel, or the like. Shape analysis includes, for example, segmentation for identifying a region (sub-dataset) of a three-dimensional dataset corresponding to such a target tissue, and identification of the shape of the identified sub-dataset. Identification of the shape includes, for example, extraction of the contour of the sub-dataset and a process of obtaining the shape of the contour (circularity, roundness, ellipticity, cylindricity, etc.). Any of the above matters regarding layer thickness analysis can be applied to shape analysis.

[0186] In addition to such shape measurement, orientation analysis for measuring the orientation of the target tissue can be performed. Orientation analysis includes, for example, a process of obtaining a figure (e.g., an approximate ellipse) that approximates the shape of the contour of the sub-dataset, and a process of obtaining the orientation of this approximate figure (e.g., the orientation of the major axis of the approximate ellipse). In other examples, orientation analysis includes a process of obtaining a specific parameter (e.g., the maximum diameter) of the sub-dataset and a process of obtaining the orientation based on this parameter (e.g., the orientation of the line segment indicating the maximum diameter).

[0187] The processing execution unit 256B shown in FIG. 10B includes an analysis unit 2561 configured to execute a predetermined analysis process based on at least one of a first sub-dataset of a first three-dimensional dataset and a second sub-dataset of a second three-dimensional dataset, and an evaluation unit 2562 configured to execute a predetermined evaluation process based on the data obtained by this analysis process. The analysis unit 2561 may be the same as the analysis unit 2561 in FIG. 10A.

[0188] The evaluation unit 2562 can, for example, evaluate whether the data of the eye 120 is normal (whether there is a suspicion of a disease), determine the degree of the disease, or determine the degree of suspicion of the disease by comparing the data obtained by the analysis unit 2561 with normative data.

[0189] The evaluation process is not limited to comparison with normative data, and may include any evaluation process using statistics, any evaluation process using calculations, and the like.

[0190] The combination of the analysis process and the evaluation process applied to only one of the first partial data set and the second partial data set may be the same as, for example, the combination of the analysis process and the evaluation process of the first exemplary embodiment.

[0191] When the combination of the analysis process and the evaluation process is applied to both the first partial data set and the second partial data set, the processing execution unit 256B can, for example, obtain the time change of the analysis data and obtain the time change of the evaluation based thereon.

[0192] The processing execution unit 256C shown in FIG. 10C includes a region of interest setting unit 2563 configured to set a partial data set (region of interest which is at least a part of the three-dimensional data set) to which the analysis process is applied, and an analysis unit 2561 configured to execute a predetermined analysis process based on the set partial data set. The analysis unit 2561 may be the same as the analysis unit 2561 in FIG. 10A.

[0193] The region of interest setting unit 2563 sets the region of interest, for example, by analyzing the three-dimensional data set. The setting of the region of interest includes, for example, segmentation for specifying the region of interest in the three-dimensional data set. In this example, the region of interest setting unit 2563 sets a first region of interest in the first three-dimensional data set and a second region of interest in the second three-dimensional data set. The region of the eye 120 corresponding to the first region of interest and the region of the eye 120 corresponding to the second region of interest may be the same or different from each other.

[0194] In another example, the control device 170 causes a display device (not shown) to display a two-dimensional map (or any image based on a three-dimensional data set). The user designates a desired region within the displayed two-dimensional map (or image) using an operation device (not shown). The region of interest setting unit 2563 can set a region of interest in the three-dimensional data set based on the region designated by the user in the displayed two-dimensional map (or image). Thereby, a first region of interest in the first three-dimensional data set and a second region of interest in the second three-dimensional data set are set.

[0195] The eye region corresponding to the region of interest may include, for example, a lesion, blood vessels, optic disc, macula, sub-tissues of the fundus (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, Bruch's membrane, choroid, sclera, etc.), sub-tissues of the cornea (corneal epithelium, Bowman's membrane, stroma, Descemet's membrane, corneal endothelium, etc.), iris, lens, zonular fibers, ciliary body, vitreous body, and any of other eye tissues.

[0196] The evaluation unit 2562 can be combined with the processing execution unit 256C shown in FIG. 10C. The evaluation unit 2562 in this example executes a predetermined evaluation process based on the data obtained by the analysis process executed by the analysis unit 2561 based on the first region of interest and the second region of interest (the first partial data set and the second partial data set) set by the region of interest setting unit 2563. The evaluation unit 2562 in this example may be the same as the evaluation unit 2562 in FIG. 10B.

[0197] The processing execution unit 256D shown in FIG. 10D includes an analysis unit 2561 configured to execute a predetermined analysis process based on at least one of a first partial data set of a first three-dimensional data set and a second partial data set of a second three-dimensional data set, a region of interest setting unit 2563 configured to set partial data (a region of interest that is at least a part of the analysis data) to which an evaluation process is applied in the data obtained by this analysis process, and an evaluation unit 2562 configured to execute a predetermined evaluation process based on at least one of a first region of interest set for the analysis data of the first partial data set and a second region of interest set for the analysis data of the second partial data set. The analysis unit 2561 may be the same as the analysis unit 2561 in FIG. 10A. The evaluation unit 2562 may be the same as the evaluation unit 2562 in FIG. 10B.

[0198] The region of interest setting unit 2563, for example, identifies a partial data set by analyzing a three-dimensional data set, and sets partial data of the analysis data corresponding to this partial data set as the region of interest. The setting of the partial data set includes, for example, segmentation.

[0199] In another example, the region of interest setting unit 2563 sets the region of interest by analyzing the analysis data obtained by the analysis unit 2561. As an example, the region of interest setting unit 2563 executes a process of detecting characteristic partial data in the analysis data and a process of setting the region of interest based on the detected partial data.

[0200] In yet another example, the control device 170 causes a display device (not shown) to display a two-dimensional map (or any image based on a three-dimensional data set, or analysis data). The user designates a desired region in the displayed two-dimensional map (or image or analysis data) using an operation device (not shown). The region of interest setting unit 2563 can set the region of interest for the analysis data based on the region designated by the user in the displayed two-dimensional map (or image or analysis data).

[0201] The processing execution unit 256E shown in FIG. 11 includes an inspection area setting unit 2564 configured to execute setting of an application area (inspection area) for a predetermined inspection on the eye 120. The predetermined inspection may be any inspection, and examples include OCT scan, visual field inspection, microperimetry, electrophysiological inspection, and the like.

[0202] For example, the inspection area setting unit 2564 executes a process of analyzing any one or more of a two-dimensional map, a three-dimensional data set, and data generated based on at least one of these to determine a region of interest in the eye 120, and a process of setting an inspection area based on the determined region of interest. The region of interest is, for example, a lesion, a specific site, a specific tissue, or the like. Typically, the inspection area is set to include at least a part of the region of interest.

[0203] In one example, the inspection area setting unit 2564 includes a segmentation for a two-dimensional map (or a three-dimensional data set), a process of converting the region in the two-dimensional map identified thereby into a region of interest in the eye 120 based on the result of a positioning process, and a process of setting an inspection area based on this region of interest.

[0204] When the inspection area set by the inspection area setting unit 2564 is an OCT scan application area, information indicating this inspection area can be provided to the scan control unit 260. The scan control unit 260 controls the OCT scanner 270 to apply an OCT scan to this inspection area.

[0205] When the inspection area set by the inspection area setting unit 2564 is an OCT scan application area, information indicating this inspection area can be provided to another OCT device (inspection device 180) via the communication device described above.

[0206] When the inspection area set by the inspection area setting unit 2564 is an application area for a certain inspection, information indicating this inspection area can be provided to the inspection device 180 corresponding to this inspection via the communication device described above.

[0207] When the inspection area setting unit 2564 processes only one of the first partial dataset of the first three-dimensional dataset and the second partial dataset of the second three-dimensional dataset, this process may be the same as that of the inspection area setting unit 2064 in the first exemplary embodiment.

[0208] When the inspection area setting unit 2564 processes both the first partial dataset of the first three-dimensional dataset and the second partial dataset of the second three-dimensional dataset, the inspection area setting unit 2564 sets a first inspection area based on the first partial dataset and a second inspection area based on the second partial dataset, and these inspection areas can be combined to set a wider inspection area.

[0209] The processing execution unit 256F shown in FIG. 12 includes an imaging area setting unit 2565 configured to set a partial dataset (an imaging area that is at least a part of the three-dimensional dataset) to which the imaging process is applied, and an image data generation unit 2566 configured to generate image data of the set imaging area.

[0210] The imaging process includes at least a Fourier transform. Examples of the imaging process include general OCT image construction, motion contrast (such as OCT angiography), phase image construction, polarization image construction, and the like.

[0211] For example, the imaging area setting unit 2565 executes a process of analyzing any one or more of a two-dimensional map, a three-dimensional dataset, and data generated based on at least one of these to determine a region of interest of the eye 120, and a process of setting an imaging area based on the determined region of interest. The region of interest is, for example, a lesion, a specific site, a specific tissue, or the like. Typically, the imaging area is set to include at least a part of the region of interest.

[0212] In one example, the imaging area setting unit 2565 includes segmenting a two-dimensional map (or three-dimensional dataset), converting the region in the two-dimensional map identified thereby into a region of interest in the eye 120 based on the result of the positioning process, and setting an imaging area based on this region of interest.

[0213] The image data generation unit 2566 is configured to generate image data based on the data collected by the OCT scanner 270. For example, the image data generation unit 2566 forms image data of a tomographic image of the eye 120 based on the output from the OCT scanner 270 (sampling data, interference signal data). This image data generation process includes filter processing, fast Fourier transform (FFT), etc., similar to conventional (swept source or spectral domain) OCT. Through such processing, the reflection intensity profile (reflection intensity profile along the Z direction) in the A-line (scan path of the measurement light beam in the eye 120) corresponding to each XY position is obtained, and the image data of this A-line (A-scan image data) is formed by imaging this reflection intensity profile.

[0214] Furthermore, the image data generation unit 2566 can form a plurality of A-scan image data according to the mode of the OCT scan (deflection of the measurement light beam, movement of the A-scan position), and construct two-dimensional image data or three-dimensional image data by arranging these A-scan image data.

[0215] When a plurality of tomographic image data are obtained by raster scan or the like, the image data generation unit 2566 can embed these tomographic image data in a single three-dimensional coordinate system to construct stack data, and apply voxelization processing to this stack data to construct voxel data (volume data).

[0216] The image data generation unit 2566 can render stack data or volume data. The rendering method is arbitrary and may be, for example, volume rendering, multi-planar reformation (MPR), surface rendering, etc. Also, the image data generation unit 2566 can construct planar images (e.g., frontal images, en face images) from the stack data or volume data. For example, the image data generation unit 2566 can construct a projection image by integrating the stack data or volume data along each A-line.

[0217] In this example, the image data generation unit 2566 generates image data by applying an imaging process to an OCT data set (a partial data set of a three-dimensional data set) included in the imaging area set by the imaging area setting unit 2565.

[0218] In other examples, the image data generation unit 2566 generates image data by applying an imaging process to a three-dimensional data set. Further, the processing execution unit 256F extracts partial image data corresponding to the imaging area set by the imaging area setting unit 2565 from this image data. The extraction of the partial image data includes, for example, clipping, cropping, or trimming and the like.

[0219] The processing execution unit 256F shown in FIG. 12 has an image construction function (image data generation unit 2566), but an OCT device (ophthalmic device) in other exemplary embodiments may not have an image construction function. In this case, information indicating the imaging area set by the imaging area setting unit 2565 can be provided to an external device (including an imaging processor) via a communication device (not shown).

[0220] When the processing execution unit 256F processes only one of the first partial data set of the first three-dimensional data set and the second partial data set of the second three-dimensional data set, this processing may be the same as that of the processing execution unit 206F in the first exemplary embodiment.

[0221] When the processing execution unit 256F processes both the first partial data set of the first three-dimensional data set and the second partial data set of the second three-dimensional data set, the imaging area setting unit 2565 sets a first imaging area based on the first partial data set and a second imaging area based on the second partial data set, and can set a wider imaging area by combining these imaging areas.

[0222] In the example shown in FIG. 13, a reception unit 190 for receiving data (examination data) obtained from the eye 120 by a predetermined examination different from OCT is provided. Further, the processing execution unit 256G in this example includes a comparison unit 2567. The comparison unit 2567 is configured to execute a predetermined comparison process between at least a part of the examination data received by the reception unit 190 and at least a part of the three-dimensional data set collected by the OCT scanner 270.

[0223] The reception unit 190 receives examination data obtained from the eye 120 from the outside (for example, an ophthalmic device, an image archiving system, a recording medium). The reception unit 190 may include, for example, a communication device or a drive device.

[0224] The examination data may be data obtained by any modality or examination. Examples of the examination data include sensitivity distribution data obtained by a visual field examination of the eye 120, electroretinogram (EGR) obtained by an electrophysiological examination, and tear distribution data obtained by tear imaging (anterior eye imaging).

[0225] The comparison unit 2567 may be configured to execute, for example, a positioning process similar to that of the positioning unit 204, registration between a two-dimensional map based on the result of this positioning process and the examination data, registration between the three-dimensional data set and the examination data based on the result of this registration, and a predetermined comparison process based on the registered three-dimensional data set (at least a part thereof) and the examination data.

[0226] This comparison process may include, for example, comparison between a three-dimensional data set and inspection data, comparison between a two-dimensional map based on the three-dimensional data set and inspection data, comparison between image data based on the three-dimensional data set and inspection data, comparison between analysis data of the two-dimensional map and inspection data, comparison between analysis data of the image data and inspection data, comparison between the three-dimensional data set and processed data of the inspection data, comparison between a two-dimensional map based on the three-dimensional data set and processed data of the inspection data, comparison between image data based on the three-dimensional data set and processed data of the inspection data, comparison between analysis data of the two-dimensional map and processed data of the inspection data, and comparison between analysis data of the image data and processed data of the inspection data, including any one or more of them.

[0227] When the comparison unit 2567 processes only one of the first partial data set of the first three-dimensional data set and the second partial data set of the second three-dimensional data set, this process may be the same as that of the comparison unit 2067 in the first exemplary embodiment.

[0228] When the comparison unit 2567 processes both the first partial data set of the first three-dimensional data set and the second partial data set of the second three-dimensional data set, the comparison unit 2567 executes a comparison process based on the first partial data set and a comparison process based on the second partial data set, and can synthesize the two results obtained by these comparison processes to obtain a wider range of comparison results.

[0229] The processing execution unit 256H shown in FIG. 14 includes a registration unit 2568 configured to execute registration between the first partial data set and the second partial data set through registration between a first two-dimensional map based on the first three-dimensional data set and a second two-dimensional map based on the second three-dimensional data set.

[0230] The registration unit 2568 first compares the first two-dimensional map and the second two-dimensional map, and performs registration between the first two-dimensional map and the second two-dimensional map based on the result.

[0231] The comparison of two 2D maps may include an image correlation operation. One method that can be adopted for this image correlation operation is described, for example, in Japanese Patent No. 6276943 (International Publication No. 2015 / 029675). When using this method, the registration unit 2568 can obtain the displacement amount between the first 2D map and the second 2D map by applying phase only correlation (POC) to a pair of the first 2D map and the second 2D map. This displacement amount includes, for example, either one or both of the translation amount and the rotation amount. Registration is executed to change the relative positions of the first 2D map and the second 2D map so as to cancel out the obtained displacement amount.

[0232] For details of the 2D map comparison method using phase only correlation, refer to Japanese Patent No. 6276943. Also, the applicable 2D map comparison method is not limited to the above example, and any method or any modification thereof within the scope of the invention described in Japanese Patent No. 6276943 can be applied. Also, in order to compare two 2D maps, it is also possible to use any image correlation method other than phase only correlation or any image comparison method other than the image correlation method.

[0233] The processing execution unit 256I shown in FIG. 15 includes a registration unit 2569 configured to execute registration between a first 2D map based on a first 3D data set and a second 2D map based on a second 3D data set.

[0234] Similar to the registration unit 2568 in FIG. 14, the registration unit 2569 compares the first two-dimensional map and the second two-dimensional map, and performs registration between the first two-dimensional map and the second two-dimensional map based on the result. The comparison of the two two-dimensional maps includes, for example, an image correlation operation. This image correlation operation may include a phase-limited correlation operation. By the phase-limited correlation operation, the displacement amount between the first two-dimensional map and the second two-dimensional map can be obtained. This displacement amount includes, for example, either or both of the translation amount and the rotational movement amount. The registration is executed to change the relative positions of the first two-dimensional map and the second two-dimensional map so as to cancel out the obtained displacement amount. Note that the registration method is not limited to these examples.

[0235] The output from the registration unit 2569 (information indicating the displacement amount between the first two-dimensional map and the second two-dimensional map) is input to the scan control unit 260. Based on the information input from the registration unit 2569, the scan control unit 260 adjusts the application area of the OCT scan to the eye 120. For example, the scan control unit 260 adjusts the control signal sent to the two-dimensional optical scanner 116 so as to cancel out the displacement amount between the first two-dimensional map and the second two-dimensional map.

[0236] Typically, the OCT scanner 270 repeatedly applies OCT scans to the eye 120 to sequentially collect a three-dimensional data set. The sequentially collected three-dimensional data sets are sequentially (in real time) input into the processing device 160. The mapping unit 252 sequentially (in real time) creates two-dimensional maps from the sequentially input three-dimensional data sets. The sequentially created two-dimensional maps are sequentially (in real time) input into the registration unit 2569. The registration unit 2569 sequentially (in real time) applies the aforementioned registration to the sequentially input two-dimensional maps. For example, the registration unit 2569 applies registration to a pair of the nth input two-dimensional map and the (n + 1)th input two-dimensional map (n is a positive integer). Thereby, the displacement amount between two continuously obtained two-dimensional maps, that is, the difference (displacement amount) between the position of the eye 120 at the time when the nth three-dimensional data set is collected and the position of the eye 120 at the time when the (n + 1)th three-dimensional data set is collected is obtained substantially in real time. The sequentially obtained displacement amounts are sequentially (in real time) input into the scan control unit 260. The scan control unit 260 adjusts the control signal to the two-dimensional optical scanner 116 so as to cancel the sequentially input displacement amounts. By repeating such a series of real-time processes, adjustment (tracking) of the OCT scan application area according to the movement of the eye 120 is realized.

[0237] In the example shown in FIG. 16, the first three-dimensional data set and the second three-dimensional data set are collected from different three-dimensional regions of the eye 120. That is, the first three-dimensional data set is collected from the first three-dimensional region of the eye 120, and the second three-dimensional data set is collected from a second three-dimensional region different from the first three-dimensional region. Typically, a part of the first three-dimensional region and a part of the second three-dimensional region are common.

[0238] The processing execution unit 256J shown in FIG. 16 includes a registration unit 2570, an image data generation unit 2571, and an image composition unit 2572.

[0239] The registration unit 2570 is configured to perform registration between a first two-dimensional map based on a first three-dimensional data set and a second two-dimensional map based on a second three-dimensional data set. The process executed by the registration unit 2570 may be the same as that of the registration unit 2569 in FIG. 15.

[0240] The image data generation unit 2571 generates first image data from a first partial data set of the first three-dimensional data set and generates second image data from a second partial data set of the second three-dimensional data set. The process executed by the image data generation unit 2571 may be the same as that of the image data generation unit 2066 in FIG. 12.

[0241] The image composition unit 2572 composes the first image data and the second image data generated by the image data generation unit 2571 based on the result of the registration between the first two-dimensional map and the second two-dimensional map obtained by the registration unit 2570.

[0242] The result of the registration between the first two-dimensional map and the second two-dimensional map includes the relative displacement amount between the first two-dimensional map and the second two-dimensional map. This relative displacement amount indicates the relative positional relationship between the first two-dimensional map and the second two-dimensional map. The relative positional relationship between the first two-dimensional map and the second two-dimensional map corresponds to the relative positional relationship (particularly, the relative positional relationship in the XY plane) between the first three-dimensional data set and the second three-dimensional data set. The image composition unit 2572 arranges and composes the first image data and the second image data according to this relative positional relationship.

[0243] The relative positional relationship in the Z direction between the first three-dimensional dataset and the second three-dimensional dataset is obtained, for example, by analyzing the first three-dimensional dataset and the second three-dimensional dataset, or by analyzing the first image data and the second image data. For example, registration between the first image data and the second image data can be performed by registering the common region between the first image data and the second image data.

[0244] In another example, when a third three-dimensional dataset including at least a part of the first three-dimensional dataset and at least a part of the second three-dimensional dataset is collected, registration between the first three-dimensional dataset (such as the first two-dimensional map, the first image data, etc.) and the second three-dimensional dataset (such as the second two-dimensional map, the second image data, etc.) can be performed via this third three-dimensional dataset or its processed data (two-dimensional map, image data, etc.).

[0245] In addition to the data processing described above, the processing device 160 may be capable of executing various data processing. The processing device 160 can process data (OCT data) acquired using an OCT scan. The OCT data is, for example, interference signal data (such as at least a part of a three-dimensional dataset), a reflection intensity profile, or image data.

[0246] The processing device 160 may be capable of processing data other than OCT data. For example, when the ophthalmic device 150 has a data acquisition device other than the OCT scanner 270, the processing device 160 can process the data acquired by this data acquisition device. The ophthalmic device adopted as the data acquisition device may be, for example, an ophthalmic imaging device (ophthalmic imaging apparatus) such as a fundus camera, a scanning laser ophthalmoscope (SLO), a surgical microscope, or a slit lamp microscope. As another example, there are ophthalmic measurement devices such as a refractometer, a keratometer, a tonometer, an axial length measuring device, a specular microscope, a wavefront analyzer, or a perimeter. Further, when the OCT device is an arbitrary medical device (that is, when the OCT device is a device used in an arbitrary medical department), the medical device adopted as the data acquisition device may be, for example, an arbitrary medical imaging device and / or an arbitrary medical examination device. Also, for an OCT device used in a field other than medicine, a data acquisition device corresponding to that field can be adopted.

[0247] Some examples of the operations that can be performed by the ophthalmic device 150 having the configuration exemplified above will be described.

[0248] Referring to FIG. 17. In this example, first, the scan control unit 260 controls the OCT scanner 270 so as to apply an OCT scan to the eye 120 and collect a first three-dimensional data set and a second three-dimensional data set (S11). More generally, two or more three-dimensional data sets can be collected.

[0249] Next, the map creation unit 252 creates a first two-dimensional map based on the respective representative intensity values of the plurality of A-scan data included in the first three-dimensional data set collected in step S11, and creates a second two-dimensional map based on the respective representative intensity values of the plurality of A-scan data included in the second three-dimensional data set collected in step S11 (S12).

[0250] Next, the processing execution unit 256 executes processing based on at least one of the first partial data set of the first three-dimensional data set collected in step S11 and the second partial data set of the second three-dimensional data set, based on the first two-dimensional map and the second two-dimensional map created in step S12.

[0251] The processing in step S13 may include, for example, any one or more of a predetermined analysis process, a predetermined evaluation process, setting of a region of interest, setting of an inspection area, setting of an imaging area, generation of image data, a predetermined comparison process with inspection data, registration, tracking, and panoramic OCT imaging. In other words, the processing execution unit 256 may include any one or more of an analysis unit 2561, an evaluation unit 2562, a region of interest setting unit 2563, an inspection area setting unit 2564, an imaging area setting unit 2565, an image data generation unit 2566, a comparison unit 2567, a registration unit 2568, a registration unit 2569, a registration unit 2570, an image data generation unit 2571, and an image synthesis unit 2572.

[0252] Some effects of the ophthalmic apparatus (OCT apparatus) 150 according to this aspect will be described.

[0253] The ophthalmic apparatus 150 according to this aspect includes an OCT scanner 270, a map creation unit 252, and a processing execution unit 256. The OCT scanner 270 applies an OCT scan to a sample (eye 120) to collect a first three-dimensional data set and a second three-dimensional data set. The map creation unit 252 creates a first two-dimensional map based on the representative intensity value of each of the plurality of A-scan data included in the first three-dimensional data set. Further, the map creation unit 252 creates a second two-dimensional map based on the representative intensity value of each of the plurality of A-scan data included in the second three-dimensional data set. The processing execution unit 256 executes processing based on at least one of at least the first partial data set of the first three-dimensional data set and at least the second partial data set of the second three-dimensional data set, based on the first two-dimensional map and the second two-dimensional map.

[0254] According to such an ophthalmic device 150, since processing based on two (or more) three-dimensional data sets can be performed using a two-dimensional map created from the three-dimensional data set collected by an OCT scan, without performing three-dimensional image construction or landmarking as in the inventions described in Patent Document 1 (U.S. Patent No. 7,884,945) and Patent Document 2 (U.S. Patent No. 8,405,834), it is possible to execute processing of OCT data collected from a sample. Therefore, it is possible to improve the efficiency of resources required for processing and shorten the processing time, and it is possible to improve the efficiency of OCT data processing. Thereby, for example, it is also possible to suitably perform real-time processing.

[0255] In the ophthalmic device 150 of this aspect, the processing execution unit 256 may include an analysis unit 2561 configured to execute a predetermined analysis process based on at least one of at least a first partial data set of the first three-dimensional data set and at least a second partial data set of the second three-dimensional data set, based on the first two-dimensional map and the second two-dimensional map.

[0256] According to such a configuration, it is possible to execute analysis of OCT data collected from a sample without performing three-dimensional image construction or landmarking as in the prior art. Therefore, it is possible to improve the efficiency of resources required for analysis and shorten the processing time, and it is possible to improve the efficiency of analysis. Thereby, for example, it is also possible to suitably perform real-time analysis.

[0257] In the ophthalmic device 150 of this aspect, the processing execution unit 256 may include an evaluation unit 2562 configured to execute a predetermined evaluation process based on the analysis data obtained by the analysis unit 2561, based on the first two-dimensional map and the second two-dimensional map.

[0258] According to such a configuration, it is possible to perform an evaluation regarding a desired region of a sample without performing three-dimensional image construction or landmarking as in the prior art. Therefore, it is possible to improve the efficiency of resources required for the evaluation and shorten the processing time, and it is possible to improve the efficiency of the evaluation. Thereby, for example, it is also possible to suitably perform real-time evaluation.

[0259] In the ophthalmic apparatus 150 of the present aspect, the processing execution unit 256 may include a region of interest setting unit 2563 configured to set a region of interest (analysis target region and / or evaluation target region) for the first three-dimensional data set and / or the second three-dimensional data set based on the first two-dimensional map and the second two-dimensional map.

[0260] According to such a configuration, it is possible to set a region of interest without performing three-dimensional image construction or landmarking as in the prior art. Therefore, it is possible to improve the efficiency of resources required for region of interest setting and shorten the processing time, and it is possible to improve the efficiency thereof. Thereby, for example, it is also possible to suitably perform real-time region of interest setting.

[0261] In the ophthalmic apparatus 150 of the present aspect, the processing execution unit 256 may include an inspection area setting unit 2564 configured to set an application area (inspection area) of a predetermined inspection for a sample based on the first two-dimensional map and the second two-dimensional map.

[0262] According to such a configuration, it is possible to set an inspection area without performing three-dimensional image construction or landmarking as in the prior art. Therefore, it is possible to improve the efficiency of resources required for inspection area setting and shorten the processing time, and it is possible to improve the efficiency thereof. Thereby, for example, it is also possible to suitably perform real-time inspection area setting.

[0263] In the ophthalmic apparatus 150 of the present aspect, the process execution unit 256 may include an imaging area setting unit 2565 configured to set a partial data set (imaging area) to which a predetermined imaging process is applied based on the first two-dimensional map and the second two-dimensional map.

[0264] According to such a configuration, it is possible to set the imaging area without performing three-dimensional image construction or landmarks as in the prior art. Therefore, it is possible to improve the efficiency of resources required for imaging area setting and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, it is also possible to suitably perform real-time imaging area setting.

[0265] The ophthalmic apparatus 150 of the present aspect may include means for preparing inspection data obtained from a sample by a predetermined inspection different from OCT. This inspection data preparation means includes, for example, means for receiving inspection data (reception unit 190) or means for applying an inspection to a sample to obtain inspection data. Further, the process execution unit 256 may include a comparison unit 2567 configured to execute a predetermined comparison process between the inspection data and at least a part of the three-dimensional data set based on the first two-dimensional map and the second two-dimensional map.

[0266] According to such a configuration, it is possible to set the comparison process between the inspection data and the OCT data without performing three-dimensional image construction or landmarks as in the prior art. Therefore, it is possible to improve the efficiency of resources required for the comparison process and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, it is also possible to suitably perform real-time comparison processing.

[0267] In the ophthalmic apparatus 150 of the present aspect, the process execution unit 256 may include a registration unit 2568 that executes registration between at least a first partial data set and at least a second partial data set via registration between the first two-dimensional map and the second two-dimensional map.

[0268] According to such a configuration, it is possible to perform registration between two (or more) OCT data without performing three-dimensional image construction or landmark like the prior art. Therefore, it is possible to improve the efficiency of resources required for registration and shorten the processing time, and it is possible to improve the efficiency. Thereby, for example, it is also possible to suitably perform real-time registration.

[0269] In the ophthalmic apparatus 150 of the present aspect, the process execution unit 256 may include a registration unit 2569 for adjusting the application area of the OCT scan for the sample through registration between the first two-dimensional map and the second two-dimensional map.

[0270] Furthermore, in the ophthalmic apparatus 150 of the present aspect, the process execution unit 256 (registration unit 2569) sequentially processes the three-dimensional data sets sequentially collected from the sample, and sequentially adjusts the application area of the OCT scan for the sample, thereby enabling tracking according to the movement of the sample.

[0271] According to such a configuration, it is possible to perform tracking according to the movement of the sample without performing three-dimensional image construction or landmark like the prior art. Therefore, it is possible to improve the efficiency of resources required for tracking and shorten the processing time, and it is possible to improve the efficiency. Thereby, it becomes possible to suitably perform real-time tracking.

[0272] In the process executed by the process execution unit 256 of the ophthalmic apparatus 150 of the present aspect, the registration (comparison of two-dimensional maps) between the first two-dimensional map and the second two-dimensional map may include an image correlation operation. This image correlation operation may obtain the displacement amount between the first two-dimensional map and the second two-dimensional map, and may perform registration between the first two-dimensional map and the second two-dimensional map based on this displacement amount. This displacement amount may include at least one of a translation amount and a rotation amount.

[0273] According to such a configuration, it is possible to efficiently obtain the relative positional relationship between two two-dimensional maps by image correlation (typically, phase-only correlation) without going through processes that require a lot of resources such as landmark detection.

[0274] The first three-dimensional data set and the second three-dimensional data set collected by the ophthalmic device 150 of this aspect may be collected from different three-dimensional regions of the sample. In this case, the processing execution unit 256 may execute the synthesis of the first image data generated from at least the first partial data set and the second image data generated from at least the second partial data set through registration between the first two-dimensional map and the second two-dimensional map.

[0275] According to such a configuration, it is possible to efficiently synthesize a plurality of image data corresponding to different regions of the sample without going through processes that require a lot of resources such as landmark detection.

[0276] As described above, the sample of this aspect is a living eye, but it is possible to apply similar functions and configurations to an OCT device targeting samples other than the living eye. That is, any matter (functions, hardware configuration, software configuration, etc.) related to the ophthalmic device 150 can be combined with an OCT device in any aspect. At this time, the actions and effects corresponding to the combined matters are achieved.

[0277] Some exemplary aspects relate to a method of controlling an OCT apparatus including an OCT scanner that applies OCT scans to a sample and a processor. The control method may at least include the following steps: controlling the OCT scanner to collect a first three-dimensional dataset and a second three-dimensional dataset from the sample; creating a first two-dimensional map based on respective representative intensity values of a plurality of A-scan data included in the first three-dimensional dataset; controlling the processor to create a second two-dimensional map based on respective representative intensity values of a plurality of A-scan data included in the second three-dimensional dataset; controlling the processor to perform processing based on at least one of at least a first partial dataset of the first three-dimensional dataset and at least a second partial dataset of the second three-dimensional dataset based on the first two-dimensional map and the second two-dimensional map.

[0278] Any matters (functions, hardware configurations, software configurations, etc.) regarding the ophthalmic device 150 can be combined with the control method of this aspect. At this time, actions and effects corresponding to the combined matters are achieved.

[0279] Some exemplary aspects relate to a program that causes a computer to execute such a control method of the OCT apparatus. Any of the matters described regarding the ophthalmic device 150 can be combined with this program. Further, some exemplary aspects relate to a computer-readable non-transitory recording medium having such a program recorded thereon. Any of the matters described regarding the ophthalmic device 150 can be combined with this recording medium.

[0280] Some exemplary aspects relate to an apparatus for processing data collected using OCT (OCT data processing apparatus). This OCT data processing apparatus may include at least the following elements: a reception unit that receives a first three-dimensional data set and a second three-dimensional data set collected by applying an OCT scan to a sample; a map creation unit that creates a first two-dimensional map based on the respective representative intensity values of a plurality of A-scan data included in the first three-dimensional data set, and creates a second two-dimensional map based on the respective representative intensity values of a plurality of A-scan data included in the second three-dimensional data set; and a process execution unit that executes a process based on at least one of at least a first partial data set of the first three-dimensional data set and at least a second partial data set of the second three-dimensional data set based on the first two-dimensional map and the second two-dimensional map.

[0281] That is, this OCT data processing apparatus includes an element (reception unit) that receives a three-dimensional data set obtained by an OCT scan from the outside (for example, an OCT apparatus, an image archiving system, a recording medium) instead of (or in addition to) the OCT scanner 270 of the aforementioned OCT apparatus (ophthalmic apparatus) 150. The reception unit may include, for example, a communication device or a drive device.

[0282] It is possible to combine any matters (functions, hardware configurations, software configurations, etc.) related to the ophthalmic apparatus 150 with the OCT data processing apparatus of this aspect. At this time, the actions and effects corresponding to the combined matters are achieved.

[0283] Some exemplary aspects relate to a method of controlling an OCT data processing apparatus including a processor. The control method may at least include the following steps: controlling the processor to receive a first three-dimensional data set and a second three-dimensional data set collected by applying an OCT scan to a sample; creating a first two-dimensional map based on representative intensity values of respective ones of a plurality of A-scan data included in the first three-dimensional data set; controlling the processor to create a second two-dimensional map based on representative intensity values of respective ones of a plurality of A-scan data included in the second three-dimensional data set; controlling the processor to perform processing based on at least one of at least a first partial data set of the first three-dimensional data set and at least a second partial data set of the second three-dimensional data set based on the first two-dimensional map and the second two-dimensional map.

[0284] Any matter (functions, hardware configuration, software configuration, etc.) regarding the ophthalmic apparatus 150 can be combined with the control method of this aspect. At this time, actions and effects corresponding to the combined matters are achieved.

[0285] Some exemplary aspects relate to a program for causing a computer to execute such a control method of an OCT data processing apparatus. Any of the matters described regarding the ophthalmic apparatus 150 can be combined with this program. Further, some exemplary aspects relate to a computer-readable non-transitory recording medium having such a program recorded thereon. Any of the matters described regarding the ophthalmic apparatus 150 can be combined with this recording medium.

[0286] OCT apparatuses in some exemplary embodiments (e.g., ophthalmic apparatus 150), control methods of OCT apparatuses in some exemplary embodiments, OCT data processing apparatuses in some exemplary embodiments, or control methods of OCT data processing apparatuses in some exemplary embodiments provide a method for processing OCT data. This OCT data processing method may at least include the following steps: preparing a first three-dimensional data set and a second three-dimensional data set collected from a sample; creating a first two-dimensional map based on representative intensity values of each of a plurality of A-scan data included in the first three-dimensional data set; creating a second two-dimensional map based on representative intensity values of each of a plurality of A-scan data included in the second three-dimensional data set; and performing processing based on at least one of at least a first partial data set of the first three-dimensional data set and at least a second partial data set of the second three-dimensional data set based on the first two-dimensional map and the second two-dimensional map.

[0287] Any matters (functions, hardware configurations, software configurations, etc.) regarding the ophthalmic apparatus 150 can be combined with the OCT data processing method of this embodiment. At this time, the actions and effects corresponding to the combined matters are achieved.

[0288] Some exemplary embodiments relate to a program for causing a computer to execute such an OCT data processing method. Any of the matters described with respect to the ophthalmic apparatus 150 can be combined with this program. Also, some exemplary embodiments relate to a computer-readable non-transitory recording medium recording such a program. Any of the matters described with respect to the ophthalmic apparatus 150 can be combined with this recording medium.

[0289] In some embodiments, the non-transitory recording medium recording the program may be in any form, and examples thereof include magnetic disks, optical disks, magneto-optical disks, semiconductor memories, and the like.

[0290] Several exemplary aspects described above are merely examples of embodiments of the present invention. Therefore, any modifications (omissions, substitutions, additions, etc.) within the scope of the gist of the present invention are possible.

Explanation of Signs

[0291] 100, 150 Ophthalmic device (OCT device) 124, 160 Processing device 126, 170 Control device 202, 252 Map creation unit 204 Positioning unit 206, 256 Processing execution unit 210, 260 Scan control unit 220, 270 OCT scanner

Claims

1. A method for generating OCT image data by applying an imaging process of the Fourier domain method, which includes a Fourier transform for converting the spectral intensity distribution, which represents the relationship between the wavenumber values and the intensity values, included in each A-scan data of a three-dimensional dataset collected by applying a Fourier domain method optical coherence tomography (OCT) scan to a sample, into a reflection intensity distribution in the depth direction, to the A-scan data, wherein: A three-dimensional dataset composed of a plurality of A-scan data, each of which is a spectral intensity distribution representing the relationship between the wavenumber values and the intensity values, collected from the sample is prepared; Without applying the imaging process of the Fourier domain method to the plurality of A-scan data, a high-pass filter is applied to each A-scan data to extract an amplitude component, a representative intensity value, which is a single estimated intensity value, is determined based on the inverse cumulative distribution function from the extracted amplitude component, and a two-dimensional map representing the distribution of the plurality of representative intensity values determined from the plurality of A-scan data on a plane orthogonal to the depth direction is created; Based on the two-dimensional map, positioning of the three-dimensional dataset for determining the positional relationship between the three-dimensional dataset and the region of the sample is performed; The imaging process of the Fourier domain method is applied to at least a part of the three-dimensional dataset to generate OCT image data. OCT data processing method.

2. An application area setting process for setting an application area for a predetermined inspection on the sample is executed. The OCT data processing method according to Claim 1.

3. A partial dataset setting process for setting a partial dataset to which a predetermined imaging process is applied is executed. The OCT data processing method according to Claim 1 or 2.

4. Inspection data obtained from the sample by a predetermined inspection different from OCT is prepared; A predetermined comparison process between the inspection data and at least a part of the three-dimensional dataset is executed. The OCT data processing method according to any one of Claims 1 to 3.

Images