OCT retinal volume measurement based on ETDRS grid

Abstract

The present disclosure proposes a method for retinal volume measurement based on an early treatment research (ETDRS) grid for diabetic retinopathy. The method includes: receiving an optical coherence tomography (OCT) image of a retina of a patient and ETDRS mapping information, the ETDRS mapping information identifying one or more partitions of the ETDRS grid; and segmenting the OCT image of the retina to identify one or more layer features corresponding to layers of the retina and one or more disease-related features associated with the one or more layer features. The method further includes determining one or more volumetric measurements of the one or more disease-related features based on the segmented OCT image. The one or more volumetric measurements correspond to the ETDRS mapping information. The method further includes generating a report based on the one or more volumetric measurements.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 355,467, filed on June 24, 2022, the entire contents of which are incorporated herein in their entirety. Technical Field

[0003] The present application relates generally to diabetic retinopathy, and more particularly to optical coherence tomography (OCT) retinal volume measurements based on the Early Treatment Diabetic Retinopathy Study (ETDRS) grid. Background Art

[0004] Diabetic retinopathy (DR) is a common complication of diabetes ("diabetes") in both type 1 and type 2 diabetes patients.DR may occur when high blood sugar levels cause damage to the blood vessels of the retina, and may include several progressive stages.For example, each stage of DR may include mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR and proliferative diabetic retinopathy (PDR).Each stage of DR may include specific disease-related features, which may occur at different locations (e.g., within the retina, under the retina and in the subretinal pigment epithelium (sub-RPE)), and may further cause retinal detachment.For example, the complication of the NPDR stage of DR may include weakening of the vascular wall, which may be observed as a tiny protrusion in the blood vessel, and the tiny protrusion may cause fluid (fluid) and blood to leak into the retina.Similarly, in the PDR stage of DR, new fragile blood vessels may be formed on the retina.These newly formed blood vessels may usually rupture, causing blood to leak into the vitreous, damage the optic nerve or both. If untreated, PDR can lead to severe vision loss and even blindness in diabetic patients. In addition, fluid leakage at any stage of DR may lead to diabetic macular edema (DME) (e.g., swelling and thickening of the macula of the retina), but DME is most likely to occur during the late stages of DR.

[0005] DR progress, DME and / or changes in vascular system can be visualized using, for example, color fundus photography (CFP) images or optical coherence tomography (OCT) images. For example, CFP imaging utilizes a fundus camera to record images of the inner surface of the eye to capture the retina, optic disc, macula, retinal blood vessels and the posterior pole (e.g., fundus). Imaging the inner surface of the eye can allow a clinician (e.g., an ophthalmologist, optometrist or other retinal specialist) to observe the presence of DR and the potential progression of DR. OCT imaging also includes capturing eyes in a non-invasive manner using light waves, the reflection of which is utilized to generate a cross-sectional two-dimensional (2D) image of the retina and retinal layers. For example, OCT imaging can distinguish retinal layers and any liquid or other deposits in or around the retinal layers. OCT imaging can further delineate biomarkers present in various retinal layers including deposits such as fatty exudates (e.g., hard fatty deposits left behind by leaking blood vessels), drusen (e.g., deposits that are not removed due to reduced waste removal capacity), abnormal blood vessels (e.g., irregular constriction and dilation of blood vessels), blood leakage or hemorrhage, and highly reflective material (HRM).

[0006] Treatment for DR may change based on the severity of DR and / or whether DR progresses to DME. For example, a clinician (e.g., an ophthalmologist, optometrist, or other retinal specialist) may typically forgo treatment of mild NPDR in a diabetic patient and simply observe the mild NPDR over time with frequent OCT scans. In contrast, moderate NPDR, severe NPDR, and / or DME may be treated with anti-vascular endothelial growth factor (anti-VEGF) antibodies, anti-angiopoietin-2 (anti-Ang-2) antibodies, or a combination thereof.

[0007] Visual acuity is a feature that health care professionals can study or test to detect the presence or progression of DR. The current standard for visual acuity testing is called the Early Treatment Diabetic Retinopathy Study (ETDRS) acuity test. The ETDRS scale ranges from 10 (no retinopathy) to 85 (late PDR). In many cases, the ETDRS acuity test may require a highly trained assessor to accurately interpret the 2D CFP images of 3D structures (such as retina). In addition, due to the lack of depth perception, CFP images may make abnormal observations challenging. Therefore, the results of CFP images may be inaccurate, dependent on assessors and time-consuming. Summary of the invention

[0008] Embodiments of the present disclosure relate to one or more computing devices, methods, and non-transitory computer-readable media for performing optical coherence tomography (OCT) retinal volume measurements based on an Early Treatment Diabetic Retinopathy Study (ETDRS) grid. In certain embodiments, one or more computing devices may receive an optical coherence tomography (OCT) image of a patient's retina and ETDRS mapping information that identifies one or more subfields of the ETDRS grid. In certain embodiments, one or more computing devices may segment the OCT image of the retina to identify one or more layer features corresponding to a layer of the retina and one or more disease-related features associated with the one or more layer features. In certain embodiments, one or more computing devices may determine one or more volume measurements of one or more disease-related features based on the segmented OCT image, wherein the one or more volume measurements correspond to the ETDRS mapping information. In certain embodiments, one or more computing devices may then generate a report based on the one or more volume measurements.

[0009] Indeed, by generating three-dimensional (3D) volume measurements derived from a cross-sectional two-dimensional (2D) OCT B-scan of a patient's retina, and by further utilizing an ETDRS grid constructed with known dimensions and partitions and corresponding to a 2D image of the patient's fovea, volume measurements quantifying one or more layer features or disease-related features of the patient's retina (e.g., volume, thickness, area, extent, area of ​​destruction, presence or absence of one or more disease-related features, number of one or more disease-related features) can be appropriately mapped to a 2D image of the patient's retina (e.g., en face image, retinal thickness map) as appropriate for use in a variety of clinical applications. The volume measurements and ETDRS mapping information can then be provided as a report to, for example, one or more clinicians (e.g., ophthalmologists, optometrists, or other retinal specialists) to improve and facilitate the diagnosis, prognosis, and treatment of diabetic retinopathy (DR), progression of DR, and / or diabetic macular edema (DME).

[0010] In some embodiments, one or more layer features may include Bruch's membrane (BM), the boundary of the myoid body and the ellipsoidal internal segment (BMEIS), the ganglion cell layer-inner plexiform layer (GCL-IPL), the inner boundary outer photoreceptor (IB-OPR) layer, the outer boundary outer photoreceptor (OB-OPR) layer, the inner boundary retinal pigment epithelium (IB-RPE) layer, the outer boundary retinal pigment epithelium (OB-RPE) layer, the inner limiting membrane (ILM), the inner plexiform layer-inner nuclear layer (IPL-INL), the inner plexiform layer-outer nuclear layer (IPL-ONL), the inner segment / outer segment junction (ISJ-OSJ) layer, the outer plexiform layer-Henle fiber layer (OPL-HFL) or the retinal nerve fiber layer-ganglion cell layer (RNFL-GCL).

[0011] In some embodiments, one or more disease-related features of one or more computing devices may include one or more liquid features, wherein the one or more liquid features include one or more of intraretinal fluid (IRF), subretinal fluid (SRF), or liquid corresponding to pigment epithelial detachment (PED). In some embodiments, one or more disease-related features may include one or more sediment features, wherein one or more sediment features include subretinal high reflective material (SHRM), intraretinal high reflective material (IHRM), or highly reflective retinal lesions (HRF). In some embodiments, one or more computing devices may identify one or more biomarkers based on one or more volume measurements. In some embodiments, one or more computing devices may receive a frontal image of the patient's retina, wherein the frontal image is associated with an OCT image. In some embodiments, before generating a report, one or more computing devices may map one or more volume measurements and ETDRS mapping information to the frontal image. For example, in some embodiments, mapping one or more volume measurements and ETDRS mapping information to the frontal image may include associating one or more volume metrics with the frontal image relative to one or more identified partitions.

[0012] In some embodiments, determining one or more volume measurements may include determining the total volume of one or more disease-related features relative to at least one of the one or more identified partitions. In some embodiments, determining one or more volume measurements may include determining the volume of liquid of one or more liquid features relative to at least one of the one or more identified partitions. In some embodiments, determining one or more volume measurements may include determining the volume of sediment of one or more sediment features relative to at least one of the one or more identified partitions. In some embodiments, determining one or more volume measurements may include determining the thickness of one or more of the various layers of the retina relative to at least one of the one or more identified partitions. In some embodiments, determining one or more volume measurements may include determining the extent of liquid of one or more liquid features relative to at least one of the one or more identified partitions.

[0013] In some embodiments, determining one or more volume measurements may include determining the quantity of one or more sediment features relative to at least one of the one or more identified partitions. In some embodiments, determining one or more volume measurements may include determining the area of ​​one or more disease-related features relative to at least one of the one or more identified partitions. In some embodiments, determining one or more volume measurements may include determining the presence or absence of one or more disease-related features relative to at least one of the one or more identified partitions. In some embodiments, determining one or more volume measurements may include determining the area of ​​damage of one or more disease-related features relative to at least one of the one or more identified partitions.

[0014] In some embodiments, one or more computing devices may classify a patient as having diabetic retinopathy (DR) based on one or more volumetric measurements. In some embodiments, classifying a patient as having DR may further include classifying the patient as having mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR, or proliferative diabetic retinopathy (PDR) based on one or more volumetric measurements.

[0015] In some embodiments, one or more computing devices may: receive a second OCT image of the patient's retina and second ETDRS mapping information, the second ETDRS mapping information identifying one or more partitions of the ETDRS grid; and segment the second OCT image of the retina to identify one or more second layer features corresponding to various layers of the retina and one or more second disease-related features associated with the one or more second layer features. In some embodiments, the one or more computing devices may determine one or more second volume measurements of the one or more second disease-related features and corresponding to the second ETDRS mapping information based on the second segmented OCT image, and determine the progression of diabetic retinopathy (DR) of the patient based on the one or more second volume measurements.

[0016] In certain embodiments, one or more computing devices may classify a patient as having diabetic macular edema (DME) based on one or more volume measurements. In certain embodiments, one or more computing devices may generate a recommendation for treatment of the patient based on one or more volume measurements or one or more second volume measurements. In certain embodiments, treatment may include anti-vascular endothelial growth factor (anti-VEGF) antibodies, anti-vascular endothelial growth factor-A (anti-VEGF-A) antibodies, or anti-angiopoietin-2 (anti-Ang-2) antibodies. In certain embodiments, anti-VEGF-A antibodies may include faricimab-svoa. In certain embodiments, anti-Ang-2 antibodies may include faricimab. In certain embodiments, the anti-VEGF antibody is selected from the group consisting of ranibizumab, aflibercept, brolucizumab, bevacizumab, and pegaptanib sodium. In some embodiments, one or more computing devices may determine whether the patient is responsive to treatment based on the one or more volume measurements or the one or more second volume measurements. In some embodiments, one or more computing devices may identify a precision cohort associated with the patient based on the one or more volume measurements or the one or more second volume measurements. For example, in some embodiments, a precision cohort may include a group of patients identified as being clinically similar to the patient based on the one or more volume measurements or the one or more second volume measurements.

[0017] In some embodiments, the OCT image may include a time domain optical coherence tomography (TD-OCT) image or a spectral domain optical coherence tomography (SD-OCT) image. In some embodiments, the OCT image may include an image of the patient's fovea captured by an OCT ophthalmoscope, wherein the image of the fovea is further divided into three concentric circles with diameters of approximately 1 millimeter (mm), approximately 3 mm, and approximately 6 mm, respectively, according to the ETDRS grid.

[0018] In some embodiments, one or more computing devices may receive an optical coherence tomography angiography (OCT-A) image of the patient's retina and generate a 3D map of the retinal vessels of the retina based on the OCT-A image and one or more volume measurements. In some embodiments, one or more computing devices may receive a color fundus photography (CFP) image of the patient's retina and generate a composite image of the retina based on the CFP image and one or more volume measurements. In some embodiments, the report may include a table, a chart, an extensible markup language (XML) file, a hypertext markup language (HTML) file, a spreadsheet, a text file, an image file, a graphic file, a hyperlink, a web page, or any combination thereof. In some embodiments, one or more computing devices may transmit the report to a computing device associated with a clinician. In some embodiments, one or more computing devices may transmit the report to an electronic device associated with the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 An ophthalmic analysis and measurement system for performing OCT retinal volume measurements based on the ETDRS grid and generating reports based on the retinal volume measurements is presented.

[0020] Figure 2A A flow chart of a method for performing OCT retinal volume measurements based on the ETDRS grid and generating a report based on the retinal volume measurements is presented.

[0021] Figure 2B A flow chart of a method for performing OCT retinal volume measurement based on the ETDRS grid and mapping the volume measurement results and the ETDRS grid to a 2D image is shown.

[0022] Figure 3 , 3A , 3B, and 3C respectively show one or more high-resolution examples for performing OCT retinal volume measurement based on the ETDRS grid and mapping the volume measurement results and the ETDRS grid to a thickness map.

[0023] Figure 4 , 4A, 4B and 4C respectively show one or more high-resolution examples for performing OCT retinal volume measurement based on the ETDRS grid and mapping the volume measurement results and the ETDRS grid to the frontal retinal image.

[0024] Figure 5 , 5A , 5B and 5C respectively show one or more high-resolution examples for performing OCT retinal volume measurement based on the ETDRS grid and mapping the volume measurement results and the ETDRS grid to the frontal retinal image.

[0025] Figure 6 An example computing system for performing OCT retinal volume measurements based on an ETDRS grid and generating a report based on the retinal volume measurements is presented. DETAILED DESCRIPTION

[0026] Embodiments of the present disclosure relate to one or more computing devices, methods, and non-transitory computer-readable media for performing optical coherence tomography (OCT) retinal volume measurements based on an Early Treatment Diabetic Retinopathy Study (ETDRS) grid. In certain embodiments, one or more computing devices may receive an optical coherence tomography (OCT) image of a patient's retina and ETDRS mapping information that identifies one or more subfields of the ETDRS grid. In certain embodiments, one or more computing devices may segment the OCT image of the retina to identify one or more layer features corresponding to a layer of the retina and one or more disease-related features associated with the one or more layer features. In certain embodiments, one or more computing devices may determine one or more volume measurements of one or more disease-related features based on the segmented OCT image, wherein the one or more volume measurements correspond to the ETDRS mapping information. In certain embodiments, one or more computing devices may then generate a report based on the one or more volume measurements.

[0027] Indeed, by generating three-dimensional (3D) volume measurements derived from a cross-sectional two-dimensional (2D) OCT B-scan of a patient's retina, and by further utilizing an ETDRS grid constructed with known dimensions and partitions and corresponding to a 2D image of the patient's fovea, volume measurements quantifying one or more layer features or disease-related features of the patient's retina (e.g., volume, thickness, area, extent, area of ​​destruction, presence or absence of one or more disease-related features, number of one or more disease-related features) can be appropriately mapped to a 2D image of the patient's retina (e.g., frontal image, retinal thickness map) as appropriate for use in a variety of clinical applications. The volume measurements and ETDRS mapping information can then be provided as a report to, for example, one or more clinicians (e.g., ophthalmologists, optometrists, or other retinal specialists) to improve and facilitate the diagnosis, prognosis, and treatment of DR, progression of DR, and / or DME.

[0028] Figure 1 An ophthalmic analysis and measurement system 100 for performing OCT retinal volume measurements based on an ETDRS grid and generating a report based on the retinal volume measurements according to an embodiment of the present disclosure is shown. The ophthalmic analysis and measurement system 100 may include: a computing platform 102; a data storage device 104; an OCT image capture device 106 (e.g., an OCT ophthalmoscope), which may be associated with ETDRS mapping information 108 and an ETDRS grid 110; and a computing device 112, which according to an embodiment of the present disclosure may be associated with one or more clinicians 114 (e.g., an ophthalmologist, an optometrist, or other retinal specialist). In some embodiments, the computing platform 102 may include one or more cloud computing platforms, one or more mobile computing platforms (e.g., a smartphone, a tablet computer), or a combination thereof. In certain embodiments, the data storage device 104, the OCT image capture device 106 (e.g., an OCT ophthalmoscope), and the computing device 112 may each communicate with the computing platform 102.

[0029] In some embodiments, the OCT image capture device 106 (e.g., an OCT ophthalmoscope) may include one or more non-invasive image capture devices that can scan the patient's retina and generate one or more two-dimensional (2D) cross-sectional OCT images 116 (e.g., time domain OCT (TD-OCT) B scans, spectral domain OCT (SD-OCT) B scans) of the patient's retina. For example, in some embodiments, the OCT image 116 may include a certain number of OCT B scans that can be used to capture and render the depth of retinal layers. Specifically, in some embodiments, when capturing an image of the patient's retina, the OCT image capture device 106 may perform a series of one-dimensional (1D) scans (e.g., amplitude scans or "A scans") at different depth locations, and use the series of A scans to generate a 2D cross-sectional image (e.g., brightness scans or "B scans") of the patient's three-dimensional (3D) retina.

[0030] In certain embodiments, one or more OCT images 116 (e.g., one or more B-scans) may be associated with the ETDRS mapping information 108 and the ETDRS grid 110. For example, according to the presently disclosed embodiments, one or more OCT images 116 (e.g., one or more B-scans) may be generated along with one or more 2D images (e.g., en face images, infrared images, thickness maps) that are, for example, each centered on the anatomical center of the patient's macula (e.g., the patient's fovea). Therefore, the ETDRS grid 110, which has been constructed to correspond to a 2D image of the patient's fovea, can be superimposed on one or more 2D images (e.g., a frontal image, an infrared image, a thickness map), and the ETDRS mapping information 108 can be based on the ETDRS grid 110, including, for example, the following: nine partitions to identify the location of one or more target features or target areas: "Center" = the center point of the fovea; Inner circle: ISS = "Inner Superior Partition"; INS = "Inner Nasal Partition"; "Inner Internal Partition"; IIS = "Inner Inferior Partition"; ITS = "Inner Temporal Partition"; Outer circle: OSS = "Outer Superior Partition"; ONS = "Outer Nasal Partition"; OIS = "Outer Inferior Partition"; OTS = "Outer Temporal Partition".

[0031] In certain embodiments, the ETDRS mapping information 108 and the ETDRS grid 110 may be used to determine one or more volumetric measurements (e.g., volume, thickness, area, extent, area of ​​destruction, presence or absence of one or more disease-related features, a number of disease-related features) for characterizing and quantifying diabetic retinopathy (DR) or other disease-related features about the anatomical center of the patient's macula (e.g., the patient's fovea) from one or more OCT images 116 (e.g., one or more B-scans). For example, as noted above and as will be discussed in further detail below, the one or more OCT images 116 (e.g., one or more B-scans) may be generated along with one or more 2D images (e.g., en face images, infrared images, thickness maps), each of which, for example, is about the anatomical center of the patient's macula (e.g., the patient's fovea).

[0032] In certain embodiments, once the one or more OCT images 116 (e.g., one or more B-scans) are segmented and annotated to identify the retinal layers of the patient's retina and any disease-related features (e.g., fluid, deposited material), the computing platform 102 can determine one or more volumetric measurements (e.g., volume, thickness, area, extent, area of ​​destruction, presence or absence of one or more disease-related features, number of disease-related features) of the retinal layers and disease-related features (e.g., fluid, deposited material). In addition, since the one or more OCT images 116 (e.g., one or more B-scans) and the one or more 2D images (e.g., en face images, infrared images, thickness maps) can each be known to correspond to the patient's fovea, the one or more volumetric measurements determined from the one or more OCT images 116 (e.g., one or more B-scans) can then be appropriately mapped to the one or more 2D images (e.g., en face images, infrared images, thickness maps) based on the ETDRS mapping information 108 and the ETDRS grid 110.

[0033] In certain embodiments, such as Figure 1 As further depicted, the ETDRS grid 110 can be delimited by a circular area with a diameter of 6 millimeters (mm). The center point of the ETDRS grid 110 can be the center of the circle. The ETDRS grid 110 can be divided into nine standard partitions. The central partition can be a circle with a diameter of 1 mm. The ETDRS grid 110 can be further divided into four inner partitions and four outer partitions by a circle with a diameter of 3 mm concentric with the center. The inner and outer partitions can each be divided by four radial lines extending from the central circle to the outermost circle at, for example, 45°, 135°, 225°, and 315° and intersecting the 3 mm circle at four locations.

[0034] In certain embodiments, each of the four inner partitions and the four outer partitions can be labeled by their orientation (with respect to position relative to the center of the patient's macula): "superior," "nasal," "inferior," and "temporal." For example, in some embodiments, the superior inner partition can be the area bounded by the center circle, the 3 mm circle, the 315° radial line, and the 45° radial line. For example, the nasal partition can be the nasal partition oriented toward, for example, the midline of the patient's face and closest to the optic nerve head. In some embodiments, the ETDRS grids 110 for the left and right eyes can be flipped relative to the positions of the nasal and temporal partitions.

[0035] In certain embodiments, as previously noted, the ETDRS mapping information 108 can include information identifying and / or quantifying one or more features or regions of interest within one or more 2D images (e.g., en face images, infrared images, thickness maps) associated with one or more OCT images 116 (e.g., one or more B-scans), for example, based on the following: nine subareas of the ETDRS grid 110 (e.g., "center" = the center point of the fovea; inner circle: ISS = "Inner Superior Subarea"; INS = "Inner Nasal Subarea"; "Inner Internal Subarea"; IIS = "Inner Inferior Subarea"; ITS = "Inner Temporal Subarea"; outer circle: OSS = "Outer Superior Subarea"; ONS = "Outer Nasal Subarea"; OIS = "Outer Inferior Subarea"; OTS = "Outer Temporal Subarea"). For example, as shown in FIG. Figure 3 , 4 As further understood by 5, the ETDRS mapping information 108 can identify the location of one or more destination features or destination areas according to the ETDRS grid 110 (e.g., within one or more of the nine separate partitions, within an outer ring including the OSS, ONS, OIS, and OTS partitions, within an inner ring including the ISS, INS, IIS, and ITS, a disk area or portion of a disk area including the center, ITS, and OTS partitions, a disk area or portion of a disk area including the center, ISS, and OSS partitions, a disk area or portion of a disk area including the center, INS, and ONS partitions, a disk area or portion of a disk area including the center, ITS, and OTS partitions, or any various combinations thereof).

[0036] In some embodiments, the ophthalmic analysis and measurement system 100 may include one or more processors 118, which may be implemented using hardware, software, firmware, or a combination thereof. In some embodiments, the one or more processors 118 may be included as part of the computing platform 102 and may be further utilized, for example, to support a retinal segmentation and volume measurement system 120 for segmenting and annotating one or more OCT images 116 (e.g., one or more B-scans) to label one or more layers of the patient's retina, fluid associated with one or more layers of the patient's retina, or one or more substances associated with one or more layers of the patient's retina. In some embodiments, the retinal segmentation and volume measurement system 120 may include one or more deep neural networks (DNNs) 122 or other similar machine learning models suitable for image segmentation (e.g., semantic image segmentation), feature extraction and selection, and classification of one or more OCT images 116 (e.g., one or more B-scans).

[0037] In some embodiments, the one or more deep learning models 122 may include, for example, a deep residual neural network (ResNet) image classification network (e.g., ResNet-50, ResNet-101, ResNet-152), a full resolution residual network (FRRN), a fully convolutional network (FCN) (e.g., U-Net), a pyramid scene parsing network (PSPNet), a fully convolutional dense neural network (FCDenseNet), a multi-path refinement network (RefineNet), a dilated convolutional network (e.g., DeepLabV3, DeepLabV+), a semantic segmentation network (SegNet), or other deep network convolutional network (DCNN) that may be suitable for: performing semantic segmentation and feature extraction and selection to segment and annotate one or more layer features (e.g., layers of the retina) and one or more fluid features or sediment features detectable from the OCT image 116 (e.g., one or more B-scans). For example, in some embodiments, the retinal segmentation and volume measurement system 120 may receive one or more OCT images 116 (e.g., one or more B-scans) for processing. In some embodiments, the one or more OCT images 116 may include an image of a patient's retina including a diabetic-related eye disease, such as DR or DME.

[0038] In some embodiments, as further depicted, the retinal segmentation and volume measurement system 120 may include a layer identification module 120 and a feature segmentation module 130, each of which may be implemented using software, firmware, hardware, or a combination thereof. In some embodiments, the layer identification module 120 and the feature segmentation module 130 may be used in conjunction to annotate one or more OCT images 116 (e.g., one or more B-scans) to assign class labels to one or more of the layers of the retina and disease-related features (e.g., fluid, reflective material) that may be associated with the layers of the retina.

[0039] For example, in some embodiments, the layer identification module 120 can generate a layer map 125 that includes a layer indicator set 126. The layer indicator set 126 identifies one or more retinal layers. In some embodiments, the layer indicator set 126 can include one or more layer features, where each layer feature can correspond to one or more boundaries (e.g., inner boundaries and / or outer boundaries) of a corresponding retinal layer of the retina, for example. For example, in some embodiments, one or more layer features can be Bruch's membrane (BM), the boundary between the myoid body and the ellipsoidal internal segment (BMEIS), the ganglion cell layer-inner plexiform layer (GCL-IPL), the inner boundary outer photoreceptor (IB-OPR) layer, the outer boundary outer photoreceptor (OB-OPR) layer, the inner boundary retinal pigment epithelium (IB-RPE) layer, the outer boundary retinal pigment epithelium (OB-RPE) layer, the inner limiting membrane (ILM), the inner plexiform layer-inner nuclear layer (IPL-INL), the inner plexiform layer-outer nuclear layer (IPL-ONL), the inner segment / outer segment junction (ISJ-OSJ) layer, the outer plexiform layer-Henle fiber layer (OPL-HFL) or the retinal nerve fiber layer-ganglion cell layer (RNFL-GCL).

[0040] In some embodiments, the layer indicator set 126 may include a collection of layer segments, where each layer segment is an area that identifies the thickness of a corresponding retinal layer. In one embodiment, the layer segments may be continuous or discontinuous areas. In some embodiments, the feature segmentation module 130 may generate an initial feature segmented image 135 including an initial set 136 of feature segments. In some embodiments, the initial feature segmented image 135 including the initial set 136 of feature segments and the layer map 125 including the layer indicator set 126 may be combined into, for example, a refined feature segmented image 140 including a feature segment refined set 141. In some embodiments, the refined feature segmented image 140 and the feature segment refined set 141 may include one or more OCT images 116 (e.g., one or more B-scans) that include annotations of a collection of one or more retinal layers and disease-related features (e.g., fluid, deposited material). For example, one or more disease-related features may include a liquid feature, which may include one or more of intraretinal fluid (IRF), subretinal fluid (SRF), or fluid corresponding to pigment epithelial detachment (PED). In addition, one or more disease-related features may include deposited material, which may include subretinal high reflective material (SHRM), intraretinal high reflective material (IHRM), or high reflective retinal lesions (HRF).

[0041] In some embodiments, after segmenting and annotating one or more OCT images 116 (e.g., one or more B-scans) to include classification labels for the layers and disease-related features (e.g., fluid, deposited material) of the retina, the computing platform 102 can then determine one or more volume measurements 142 (e.g., volume measurements, thickness measurements, area measurements, range measurements, damage area measurements) from the one or more OCT images 116 (e.g., one or more B-scans). Specifically, in some embodiments, once the one or more OCT images 116 (e.g., one or more B-scans) are segmented and annotated to identify the retinal layers of the patient's retina and any disease-related features (e.g., fluid, deposited material), the computing platform 102 can determine one or more volume measurements 142 (e.g., volume, thickness, area, range, damage area, presence or absence of one or more disease-related features, number of one or more disease-related features) of the retinal layers and disease-related features (e.g., fluid, deposited material) from the one or more OCT images 116 (e.g., one or more B-scans).

[0042] For example, in some embodiments, the computing platform 102 can determine one or more volumetric measurements 142 (e.g., volume, thickness, area, extent, area of ​​destruction, presence or absence of one or more disease-related features, number of one or more disease-related features) from one or more OCT images 116 (e.g., one or more B-scans) based on the ETDRS mapping information 108 and the ETDRS grid 110. As previously noted, the one or more OCT images 116 (e.g., one or more B-scans) can each be captured and generated along with one or more 2D images (e.g., en face images, infrared images, thickness maps), wherein the one or more OCT images 116 (e.g., one or more B-scans) and the one or more 2D images (e.g., en face images, infrared images, thickness maps) can each be known to correspond to the patient's fovea.

[0043] Thus, in some embodiments, based on the ETDRS mapping information 108 and known measurements and dimensions of the ETDRS grid 110 (e.g., three concentric circles with diameters of 1 mm, 3 mm, and 6 mm, respectively, and four radial lines extending from the center circle to the outermost circle and intersecting the 3 mm circle at four locations), and knowledge of depth information (e.g., retinal layer depth) of one or more OCT images 116 (e.g., one or more B-scans), the computing platform 102 can utilize one or more image processing techniques (e.g., morphological image processing) to estimate one or more volume measurements 142. For example, in some embodiments, the computing platform 102 can derive a certain number of subspace constraints (e.g., indications of 3D subspaces such as intraretinal subspaces and subretinal subspaces in which disease-related features may appear) based on layer characteristics (e.g., BM, BMEIS, GCL-IPL, IB-OPR, OB-OPR layer, IB-RPE, OB-RPE, ILM, IPL-INL, IPL-ONL, ISJ-OSJ, OPL-HFL, RNFL-GCL), and the derived number of subspace constraints can then be mapped and / or masked relative to the nine partitions of the ETDRS grid 110. The computing platform 102 can then determine one or more volumetric measurements 142 (e.g., volume, thickness, area, extent, area of ​​damage, presence or absence of one or more disease-related features, number of one or more disease-related features) by, for example, counting the number of pixels in each partition of the ETDRS grid 110 and converting the determined number of pixels in each partition of the ETDRS grid 110 to microns (for thickness), square microns (for area), and cubic microns (for volume), and so on.

[0044] In some embodiments, volume measurements 142 may include, for example, total volume of one or more disease-related features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., total volume in cubic microns between various layers of the retina), volume of one or more liquid features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., total volume in cubic microns for various liquids), volume of one or more sediment features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., total volume in cubic microns for various sedimentary materials), volume of one or more of the layers of the retina relative to The thickness of one or more of the nine partitions of the ETDRS grid 110 (e.g., thickness measurements in microns for all layers of the retina, thickness measurements in microns relative to one or more specific layers of the retina, or thickness measurements in microns of "slabs" or spaces between layers of the retina), the liquid extent of one or more liquid features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., volume measurements in microns for various liquids), or the quantity of one or more deposit features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., a numerical value representing the total amount of identified deposited material).

[0045] In some embodiments, the one or more volumetric measurements 142 may further include, for example, an area of ​​one or more disease-related features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., frontal image area measurements in square microns for various fluids or deposited materials), an indication of the presence or absence of one or more disease-related features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., a binary value representing the presence or absence of various fluids and deposited materials), an area of ​​destruction of one or more disease-related features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., frontal image destruction area in squared representation for various fluids or deposited materials), or one or more other volumetric measurements that may be used, for example, by one or more clinicians 114 (e.g., ophthalmologists, optometrists, or other retinal specialists) to qualitatively and quantify DR-related features, DME-related features, or other disease-related features relative to the anatomical center of a patient's macula (e.g., the patient's fovea).

[0046] In some embodiments, Figure 1As further depicted, after determining the one or more volume measurements 142, the one or more volume measurements 142 and the corresponding one or more segmented and annotated OCT images 116 (e.g., one or more B-scans) may be stored by the computing platform 102 to the data storage device 104. In other embodiments, such as Figure 1 As further shown, after determining one or more volumetric measurements 142, the one or more volumetric measurements 142 and corresponding one or more segmented and annotated OCT images 116 (e.g., one or more B-scans) can be included in one or more clinical reports 144 and then transmitted by the computing platform 102 to a computing device 112 associated with one or more clinicians 114 (e.g., ophthalmologists, optometrists, or other retinal specialists) for analysis and review.

[0047] For example, in some embodiments, the clinical report 144 may include, for example, a table, chart, extensible markup language (XML) file, hypertext markup language (HTML) file, spreadsheet, text file, image file, graphic file, hyperlink, web page, or other file accessible and viewable by one or more clinicians 114 (e.g., ophthalmologists, optometrists, or other retinal specialists) on the computing device 112. In one embodiment, the one or more clinical reports 144 may also be transmitted by the computing platform 102 to a computing device associated with the patient or one or more additional scientific or medical professionals (e.g., biomarker scientists, data scientists) for further analysis and / or clinical application.

[0048] In some embodiments, because the one or more volumetric measurements 142 can be associated with the ETDRS mapping information 108 and the ETDRS grid 110, one or more clinicians 114 (e.g., ophthalmologists, optometrists, or other retinal specialists) can utilize the clinical report 144 to accurately and efficiently characterize and quantify DR-related features, DME-related features, or other disease-related features with respect to the anatomical center of the patient's macula (e.g., the patient's fovea). For example, in one embodiment, as shown in FIG. Figure 3 , 4As will be further appreciated from the examples 300, 400, and 500 shown in FIGS. 5A and 5B, the clinical report 144 may include retinal volume measurements for each of the nine subareas of the ETDRS grid 110. In certain embodiments, one or more clinicians 114 (e.g., ophthalmologists, optometrists, or other retinal specialists) may then utilize the retinal volume measurements to determine, for example, whether one or more of the retinal volume measurements are outside of a specification range to determine a diagnosis of DR and / or DME, one or more treatments for DR and / or DME, and / or progression of DR and / or DME.

[0049] In certain embodiments, based on the one or more volumetric measurements 142, the computing platform 102 may classify the patient as having DR and / or DME and generate a recommendation for one or more suitable treatments for the patient (e.g., an anti-vascular endothelial growth factor (anti-VEGF) antibody, an anti-vascular endothelial growth factor-A (anti-VEGF-A) antibody, or an anti-angiopoietin-2 (anti-Ang-2) antibody), and include such data in a clinical report 144 provided to one or more clinicians 114 (e.g., an ophthalmologist, optometrist, or other retinal specialist). For example, in some embodiments, the anti-VEGF-A antibody may include faricizumab and the anti-Ang-2 antibody may include faricizumab. In some embodiments, the anti-VEGF antibody may be selected from the group consisting of ranibizumab, aflibercept, bruceizumab, bevacizumab, and pegaptanib sodium.

[0050] In some embodiments, the computing platform 102 may identify a precision cohort associated with the patient based on the one or more volumetric measurements 142. For example, in some embodiments, the precision cohort may include a group of patients identified as being clinically similar to the patient based on the one or more volumetric measurements 142. For example, in one embodiment, the precision cohort may be identified as having the same stage of DR or other similar retinal disease as the patient and / or as being best responsive to one or more specific treatment regimens, such that the patient may be recommended to undergo the same or similar treatment regimen as the precision cohort.

[0051] Figure 2A A flow chart of a method 200A for performing OCT retinal volume measurement based on an ETDRS grid and generating a report based on the retinal volume measurement according to an embodiment of the present disclosure is shown. The method 200A may utilize one or more processing devices (e.g., as described above with respect to Figure 1The computing platform 102 discussed above is performed, and the one or more processing devices may include hardware (e.g., a general-purpose processor, a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), a system on a chip (SoC), a microcontroller, a field-programmable gate array (FPGA), a central processing unit (CPU), an application processor (AP), a vision processing unit (VPU), a neural processing unit (NPU), a neural decision processor (NDP), a deep learning processor (DLP), a tensor processing unit (TPU), a neuromorphic processing unit (NPU), or any other processing device that may be suitable for processing retinal data and making one or more decisions based thereon), firmware (e.g., microcode), or some combination thereof.

[0052] The method 200A may begin at block 202, where one or more computing devices receive an optical coherence tomography (OCT) image of a patient's retina and ETDRS mapping information that identifies one or more partitions of an ETDRS grid. For example, in some embodiments, the computing platform 102 may receive one or more OCT B-scans of the patient's retina. The method 200A may then continue at block 204, where the one or more computing devices segment the OCT image of the retina to identify one or more layer features corresponding to layers of the retina and one or more disease-related features associated with the one or more layer features. For example, in some embodiments, the computing platform 102 may segment the OCT image 116 (e.g., one or more B-scans) and identify and annotate a set of one or more retinal layers and disease-related features (e.g., fluid, deposited material). The one or more disease-related features may include, for example, fluid features (e.g., IRF, SRF, PED, etc.) and deposited material (e.g., SHRM, IHRM, HRF, etc.).

[0053] Method 200A may then continue at block 206, where one or more computing devices determine one or more volume measurements of one or more disease-related features based on the segmented OCT image, where the one or more volume measurements correspond to the ETDRS mapping information. Figure 1 As discussed, based on the ETDRS mapping information 108 and known measurements and dimensions of the ETDRS grid 110 (e.g., three concentric circles having diameters of 1 mm, 3 mm, and 6 mm, respectively, and four radial lines extending from the center circle to the outermost circle and intersecting the 3 mm circle at four locations), and knowledge of depth information (e.g., retinal layer depth) from one or more OCT images 116 (e.g., one or more B-scans), the computing platform 102 can utilize one or more image processing techniques (e.g., morphological image processing) to determine a certain number of volume measurements 142.

[0054] For example, in some embodiments, the computing platform 102 can derive a certain number of subspace constraints (e.g., indications of 3D subspaces such as intraretinal subspaces and subretinal subspaces in which disease-related features may appear) based on layer characteristics (e.g., BM, BMEIS, GCL-IPL, IB-OPR, OB-OPR layer, IB-RPE, OB-RPE, ILM, IPL-INL, IPL-ONL, ISJ-OSJ, OPL-HFL, RNFL-GCL), and the derived number of subspace constraints can then be mapped and / or masked relative to the nine partitions of the ETDRS grid 110. The computing platform 102 can then determine one or more volumetric measurements 142 (e.g., volume, thickness, area, extent, area of ​​damage, presence or absence of one or more disease-related features, number of one or more disease-related features) by, for example, counting the number of pixels in each partition of the ETDRS grid 110 and converting the determined number of pixels in each partition of the ETDRS grid 110 to microns (for thickness), square microns (for area), and cubic microns (for volume), and so on.

[0055] For example, the number of volume measurements 142 may include, for example, one or more disease-related features relative to the total volume of one or more of the nine partitions of the ETDRS grid 110 (e.g., the total volume in cubic microns between the various layers of the retina), one or more liquid features relative to the liquid volume of one or more of the nine partitions of the ETDRS grid 110 (e.g., the total volume in cubic microns for various liquids), one or more sediment features relative to the sediment volume of one or more of the nine partitions of the ETDRS grid 110 (e.g., the total volume in cubic microns for various sediment materials), one or more of the layers of the retina relative to The thickness of one or more liquid features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., thickness measurements in microns for all layers of the retina, thickness measurements in microns relative to one or more specific layers of the retina, or thickness measurements in microns of "slabs" or spaces between layers of the retina), the liquid extent of one or more liquid features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., volume measurements in microns for various liquids), or the quantity of one or more deposit features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., a value representing the total amount of identified deposited material).

[0056] In some embodiments, the number of volume measurements 142 may further include, for example, an area of ​​one or more disease-related features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., frontal image area measurements in square micrometers for various liquids or deposited materials), an indication of the presence or absence of one or more disease-related features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., a binary value representing the presence or absence of various liquids and deposited materials), or a destruction area of ​​one or more disease-related features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., frontal image destruction area in squared representations for various liquids or deposited materials). The method 200A may then end at block 208, where the one or more processing devices determine to generate a report based on the one or more volume measurements. For example, as generally discussed above, the clinical report 144 may include a table, chart, XML file, HTML file, spreadsheet, text file, image file, graphic file, hyperlink, web page, or other file that is accessible and viewable on the computing device 112, for example, by one or more clinicians 114 (e.g., an ophthalmologist, optometrist, or other retinal specialist).

[0057] Figure 2B A flow chart of a method 200B for performing OCT retinal volume measurement based on an ETDRS grid and mapping the volume measurement result and the ETDRS grid to a 2D retinal image according to an embodiment of the present disclosure is shown. The method 200B may utilize one or more processing devices (e.g., as described above with respect to Figure 1 The computing platform 102 discussed above is performed, and the one or more processing devices may include hardware (e.g., a general-purpose processor, a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), a system on a chip (SoC), a microcontroller, a field-programmable gate array (FPGA), a central processing unit (CPU), an application processor (AP), a vision processing unit (VPU), a neural processing unit (NPU), a neural decision processor (NDP), a deep learning processor (DLP), a tensor processing unit (TPU), a neuromorphic processing unit (NPU), or any other processing device that may be suitable for processing retinal data and making one or more decisions based thereon), firmware (e.g., microcode), or some combination thereof.

[0058] Method 200B may begin at block 210, where one or more computing devices receive an optical coherence tomography (OCT) image and an en face image or thickness map of a patient's retina and ETDRS mapping information that identifies one or more partitions of the ETDRS grid. For example, in some embodiments, computing platform 102 may receive one or more OCT images 116 (e.g., one or more B-scans) and one or more 2D images (e.g., en face images, infrared images, thickness maps), each of which is known to correspond to the patient's fovea. Method 200B may then continue at block 212, where one or more computing devices segment the OCT image of the retina to identify one or more layer features corresponding to layers of the retina and one or more disease-related features associated with the one or more layer features. For example, in some embodiments, computing platform 102 may segment the OCT image 116 (e.g., one or more B-scans) and identify and annotate a set of one or more retinal layers and disease-related features (e.g., fluid, deposited material). The one or more disease-related characteristics may include, for example, fluid characteristics (eg, IRF, SRF, PED, etc.) and deposited material (eg, SHRM, IHRM, HRF, etc.).

[0059] Method 200B may then continue at block 214, where one or more computing devices determine one or more volume measurements of one or more disease-related features based on the segmented OCT image, where the one or more volume measurements correspond to the ETDRS mapping information. Figure 1 As discussed, based on the ETDRS mapping information 108 and known measurements and dimensions of the ETDRS grid 110 (e.g., three concentric circles having diameters of 1 mm, 3 mm, and 6 mm, respectively, and four radial lines extending from the center circle to the outermost circle and intersecting the 3 mm circle at four locations), and knowledge of depth information (e.g., retinal layer depth) from one or more OCT images 116 (e.g., one or more B-scans), the computing platform 102 can utilize one or more image processing techniques (e.g., morphological image processing) to determine a certain number of volume measurements 142.

[0060] For example, in some embodiments, the computing platform 102 can derive a certain number of subspace constraints (e.g., indications of 3D subspaces such as intraretinal subspaces and subretinal subspaces in which disease-related features may appear) based on layer characteristics (e.g., BM, BMEIS, GCL-IPL, IB-OPR, OB-OPR layer, IB-RPE, OB-RPE, ILM, IPL-INL, IPL-ONL, ISJ-OSJ, OPL-HFL, RNFL-GCL), and the derived number of subspace constraints can then be mapped and / or masked relative to the nine partitions of the ETDRS grid 110. The computing platform 102 can then determine one or more volumetric measurements 142 (e.g., volume, thickness, area, extent, area of ​​damage, presence or absence of one or more disease-related features, number of one or more disease-related features) by, for example, counting the number of pixels in each partition of the ETDRS grid 110 and converting the determined number of pixels in each partition of the ETDRS grid 110 to microns (for thickness), square microns (for area), and cubic microns (for volume), and so on.

[0061] In some embodiments, the number of volume measurements 142 may include, for example, the total volume of one or more disease-related features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., the total volume in cubic microns between various layers of the retina), the volume of one or more liquid features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., the total volume in cubic microns for various liquids), the volume of one or more sediment features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., the total volume in cubic microns for various sediment materials), one or more of the layers of the retina, and the total volume of one or more sediment features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., the total volume in cubic microns for various sediment materials). The thickness of one or more liquid features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., thickness measurements in microns for all layers of the retina, thickness measurements in microns for one or more specific layers of the retina, or thickness measurements in microns for "slabs" or spaces between layers of the retina), the liquid extent of one or more liquid features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., volume measurements in microns for various liquids), or the quantity of one or more deposit features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., a numerical value representing the total amount of identified deposited material).

[0062] In some embodiments, the one or more volumetric measurements 142 may further include, for example, an area of ​​one or more disease-related features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., frontal image area measurements in square microns for various liquids or deposited materials), an indication of the presence or absence of one or more disease-related features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., binary values ​​representing the presence or absence of various liquids and deposited materials), or a destruction area of ​​one or more disease-related features relative to one or more of the nine partitions of the ETDRS grid 110 (e.g., frontal image destruction area in squared units for various liquids or deposited materials).

[0063] The method 200B may then end at box 216, where one or more processing devices map the one or more volume measurements and the ETDRS mapping information to an en face image or thickness map. For example, as previously noted, once the one or more OCT images 116 (e.g., one or more B-scans) are segmented and annotated to identify the retinal layers of the patient's retina and any disease-related features (e.g., fluid, deposited material), the computing platform 102 may determine one or more volume measurements (e.g., volume, thickness, area, extent, area of ​​destruction, presence or absence of one or more disease-related features) of the retinal layers and disease-related features (e.g., fluid, deposited material). For example, since the one or more OCT images 116 (e.g., one or more B-scans) and the en face image or thickness map may each be known to correspond to the patient's fovea, the one or more volume measurements 142 determined from the one or more OCT images 116 (e.g., one or more B-scans) may then be appropriately mapped to the en face image or thickness map based on the ETDRS mapping information 108 and the ETDRS grid 110.

[0064] Figure 3 , 3A , 3B and 3C respectively show one or more high-resolution examples 300, 300A, 300B and 300C for performing OCT retinal volume measurement based on the ETDRS grid and mapping the volume measurement results and the ETDRS grid to a thickness map according to an embodiment of the present disclosure. It should be understood that one or more high-resolution examples 300, 300A, 300B and 300C may represent only one example of an embodiment of the present disclosure. In fact, in other examples, according to embodiments of the present disclosure, any of a variety of retinal volume measurements (e.g., volume measurements, thickness measurements, area measurements, range measurements, presence or absence indications, damage area measurements, etc.) can be calculated for each OCT B scan and for any individual partitions of the ETDRS grid.

[0065] For example, Figure 3 The high resolution example 300 shows an OCT B scan 302 (e.g., with Figure 3A ), a segmented and annotated OCT B scan 304 (e.g., corresponding to a high-resolution OCTB scan 300A of Figure 3B ) and thickness map 306 (e.g., corresponding to a high-resolution segmented and annotated OCT B scan 300B of Figure 3C 300C), showing one or more layer thickness measurements. Figure 3 , OCT B scan 302 can be an OCT B scan generated based on one or more scans of the patient's retina by an ophthalmoscope or other retinal image capture device. OCT B scan 302 can be associated with an ETDRS grid and / or ETDRS mapping information (e.g., the location of one or more features or regions of interest according to the nine partitions of the ETDRS grid). Similarly, thickness map 306 can be generated with OCT B scan 302, and OCT B scan 302 and thickness map 306 can each correspond to an image of, for example, the anatomical center of the patient's macula (e.g., the patient's fovea).

[0066] As further illustrated, the segmented and annotated OCT B-scan 304 may include annotations (e.g., colored lines) marking one or more boundaries of the layers of the patient's retina. One or more retinal volume measurements (e.g., the thickness of each of the layer features of the retina) may then be determined based on the ETDRS grid and / or the ETDRS mapping information, and the one or more retinal volume measurements may then be mapped to a thickness map 306 corresponding to the OCT B-scan 302. Specifically, since the OCT B-scan 302 and the thickness map 306 may each be known to correspond to the patient's fovea, the one or more volume measurements determined from the segmented and annotated OCT B-scan 304 may be suitably mapped to the thickness map 306 according to the ETDRS grid and / or the ETDRS mapping information.

[0067] For example, Figure 3As shown, a segmented and annotated OCT B-scan 304 can be used to determine thickness measurements of one or more layers of the retina. The thickness measurements (e.g., numerical values) can then be mapped to a thickness map 306 according to the ETDRS grid and / or ETDRS mapping information (e.g., according to the nine partitions 308, 310, 312, 314, 316, 318, 320, 322, and 324 of the ETDRS grid). In this manner, the thickness measurements can quantify the thickness of the visual layers as depicted by the thickness map 306. For example, as shown, the thickness measurements may include J microns mapped to partition 308, K microns mapped to partition 310, L microns mapped to partition 312, M microns mapped to partition 314, N microns mapped to partition 316, Q microns mapped to partition 318, P microns mapped to partition 320, R microns mapped to partition 322, and S microns mapped to partition 324, where J, K, L, M, N, Q, P, R, and S each represent a numerical value.

[0068] Figure 4 , 4A , 4B and 4C respectively show one or more additional high-resolution operating examples 400, 400A, 400B and 400C for performing OCT retinal volume measurement based on the ETDRS grid and mapping the volume measurement results and the ETDRS grid to a frontal retinal image map according to an embodiment of the present disclosure. Figure 4 The high resolution run example 400 shows a central OCTB scan 402 (e.g., with Figure 4A 400A), a segmented and annotated OCTB scan 404 (e.g., corresponding to a high-resolution central OCT B scan 400A of Figure 4B ) and en face image 406 (e.g., corresponding to a high resolution segmented and annotated OCT B scan 400B of Figure 4C 400C), showing one or more liquid and SRHM volume measurements. Figure 4 The central OCT B scan 402 may be an OCT B scan generated based on one or more scans of the patient's retina by an ophthalmoscope or other retinal image capture device. The central OCT B scan 402 may be associated with an ETDRS grid and / or ETDRS mapping information.

[0069] like Figure 4 As further shown, the segmented and annotated OCT B-scan 404 may include annotations (e.g., colored lines) marking one or more disease-related features (e.g., fluid and SRHM) between one or more layers of the patient's retina. One or more retinal volume measurements (e.g., volume of fluid and SRHM in cubic millimeters (mm)) may then be determined. 3) or the total volume measurement in cubic micrometers (μm) of liquid and SRHM 3 ) and one or more retinal volume measurements can be suitably mapped to the en face image 406 corresponding to the central OCT B scan 402.

[0070] For example, Figure 4 As shown, the segmented and annotated OCT B-scan 404 can be used to determine volume measurements of fluid and SRHM. The volume measurements (e.g., values) can then be mapped to the en face image 406 according to the ETDRS grid and / or ETDRS mapping information (e.g., according to the nine partitions 408, 410, 412, 414, 416, 418, 420, 422, and 424 of the ETDRS grid). For example, as shown in the en face image 406, the volume measurements of fluid and SRHM can include J mm mapped to the partition 408. 3 , K mm mapped to partition 410 3 , mapped to L mm of partition 412 3 , M mm mapped to partition 414 3 , Nmm mapped to partition 416 3 , Q mm mapped to partition 418 3 , P mm mapped to partition 420 3 , R mm mapped to partition 422 3 and S mm mapped to partition 424 3 , where J, K, L, M, N, Q, P, R, and S each represent a numerical value. In this way, the volume measurements of the liquid and SRHM can be visually corresponded to the liquid and SRHM volumes as displayed by the front image 406.

[0071] Figure 5 , 5A 5B and 5C respectively illustrate one or more additional high-resolution operating examples 500, 500A, 500B and 500C for performing OCT retinal volume measurement based on an ETDRS grid and mapping the volume measurement results and the ETDRS grid to an en face retinal image map according to an embodiment of the present disclosure. The high-resolution operating example 500 illustrates a central OCT B scan 502 (e.g., Figure 5A ), a segmented and annotated OCT B scan 504 (e.g., corresponding to a high-resolution central OCT B scan 500A of Figure 5B ) and en face image 506 (e.g., corresponding to a high resolution segmented and annotated OCT B scan 500B of Figure 5C), showing one or more IRHM area measurements. Figure 5 The central OCT B scan 502 may be an OCT B scan generated based on one or more scans of the patient's retina by an ophthalmoscope or other retinal image capture device. The central OCT B scan 502 may be associated with an ETDRS grid and / or ETDRS mapping information.

[0072] like Figure 5 As further illustrated, the segmented and annotated OCT B-scan 504 may include annotations (e.g., colored lines) that mark one or more disease-related features (e.g., IRHM) as deposited or dispersed in one or more layers of the patient's retina. One or more retinal volume measurements of the deposited material (e.g., volume of the deposited material in square micrometers (μm)) may be determined. 2 ) in units of area measurement and / or the area of ​​deposited material in square micrometers (μm 2 ) in units of destruction area measurements), and one or more area measurements can be suitably mapped to the en-face image 506 corresponding to the central OCT B-scan 502.

[0073] For example, Figure 5 As shown, the segmented and annotated OCT B-scan 504 can be used to determine the area measurement of the IRHM. The area measurement (e.g., numerical value) can then be mapped to the en face image 506 according to the ETDRS grid and / or the ETDRS mapping information (e.g., according to the nine partitions 508, 510, 512, 514, 516, 518, 520, 522, and 524 of the ETDRS grid). For example, as shown in the en face image 506, the area measurement of the IRHM can include Jμm mapped to the partition 408. 2 , Kμm mapped to partition 410 2 , Lμm mapped to partition 412 2 , Mμm mapped to partition 414 2 , Nμm mapped to partition 416 2 , Qμm mapped to partition 418 2 , Pμm mapped to partition 420 2 , Rμm mapped to partition 422 2 and Sμm mapped to partition 424 2 , where J, K, L, M, N, Q, P, R, and S each represent a numerical value. In this way, the area measurement of the IRHM can be visually corresponded to the area of ​​the IRHM as displayed by the frontal image 506.

[0074] In certain embodiments, such as with respect to Figure 3C High-resolution thickness map of 300C, Figure 4C High-resolution frontal retinal images of 400C and Figure 5C It can be appreciated that the ETDRS grid can be mapped to one or more high-resolution retinal images 300C, 400C, and 500C so that the nine subdivisions of the ETDRS grid can be visually aligned with visual features displayed by one or more high-resolution retinal images 300C, 400C, and 500C. In this manner, the determined and reported retinal volume measurements (e.g., volume measurements, thickness measurements, area measurements, range measurements, presence or absence indications, destruction area measurements, etc.) relative to the nine subdivisions of the ETDRS can be visually aligned with the disease-related features (e.g., layer thickness, fluid features, deposited material features) grid displayed by one or more high-resolution retinal images 300C, 400C, and 500C.

[0075] Figure 6 An example of one or more computing devices 600 that can be used to perform OCT retinal volume measurements based on an ETDRS grid and generate a clinical report based on the retinal volume measurements according to an embodiment of the present disclosure is shown. In some embodiments, one or more computing devices 600 can perform one or more steps of one or more methods described or shown herein. In some embodiments, one or more computing devices 600 provide functionality described or shown herein. In some embodiments, software running on one or more computing devices 600 performs one or more steps of one or more methods described or shown herein, or provides functionality described or shown herein. Certain embodiments include one or more portions of one or more computing devices 600.

[0076] The present disclosure contemplates any suitable number of computing systems 600. The present disclosure contemplates one or more computing devices 600 in any suitable physical form. By way of example and not limitation, one or more computing devices 600 may be an embedded computing system, a system on a chip (SOC), a single board computer system (SBC) (e.g., a computer on a module (COM) or a system on a module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a computer system network, a mobile phone, a personal digital assistant (PDA), a server, a tablet computer system, an augmented / virtual reality device, or a combination of two or more thereof. Where appropriate, one or more computing devices 600 may be: integrated or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks.

[0077] Where appropriate, one or more computing devices 600 may perform one or more steps of one or more methods described or illustrated herein without substantial spatial or temporal limitations. By way of example and not limitation, one or more computing devices 600 may perform one or more steps of one or more methods described or illustrated herein in real time or in batch mode. Where appropriate, one or more computing devices 600 may perform one or more steps of one or more methods described or illustrated herein at different times or in different locations.

[0078] In some embodiments, one or more computing devices 600 include a processor 602, a memory 604, a database 606, an input / output (I / O) interface 608, a communication interface 610, and a bus 612. Although the present disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, the present disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement. In some embodiments, the processor 602 includes hardware for executing instructions, such as those constituting a computer program. As an example and not limitation, to execute instructions, the processor 602 may: retrieve (or extract) instructions from an internal register, an internal cache, the memory 604, or the database 606; decode and execute these instructions; and then write one or more results to an internal register, an internal cache, the memory 604, or the database 606. In some embodiments, the processor 602 may include one or more internal caches for data, instructions, or addresses. Where appropriate, the present disclosure contemplates a processor 602 including any suitable number of any suitable internal caches. As an example and not limitation, processor 602 may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). The instructions in the instruction cache may be a copy of the instructions in memory 604 or database 606, and the instruction cache may speed up the retrieval of these instructions by processor 602.

[0079] The data in the data cache may be: a copy of the data in the memory 604 or database 606 for operation by instructions executed at the processor 602; the result of a previous instruction executed at the processor 602 for access or writing to the memory 604 or database 606 by subsequent instructions executed at the processor 602; or other suitable data. The data cache may speed up the read or write operation of the processor 602. The TLB may speed up the virtual address translation of the processor 602. In some embodiments, the processor 602 may include one or more internal registers for data, instructions, or addresses. Where appropriate, the present disclosure contemplates a processor 602 including any suitable number of any suitable internal registers. Where appropriate, the processor 602 may include one or more arithmetic logic units (ALUs); may be a multi-core processor; or may include one or more processors 602. Although the present disclosure describes and illustrates a particular processor, the present disclosure contemplates any suitable processor.

[0080] In some embodiments, the memory 604 includes a main memory for storing instructions for the processor 602 to execute or data for the processor 602 to operate on. As an example and not limitation, one or more computing devices 600 can load instructions from a database 606 or another source (such as, for example, another one or more computing devices 600) into the memory 604. The processor 602 can then load the instructions from the memory 604 into an internal register or an internal cache. In order to execute the instructions, the processor 602 can retrieve the instructions from the internal register or the internal cache and decode them. During or after the execution of the instructions, the processor 602 can write one or more results (which can be intermediate results or final results) to the internal register or the internal cache. The processor 602 can then write one or more of those results to the memory 604.

[0081] In some embodiments, the processor 602 executes only instructions in one or more internal registers, internal caches, or memory 604 (rather than in the database 606 or elsewhere), and operates only on data in one or more internal registers, internal caches, or memory 604 (rather than in the database 606 or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may connect the processor 602 to the memory 604. The bus 612 may include one or more memory buses, as described below. In some embodiments, one or more memory management units (MMUs) reside between the processor 602 and the memory 604 and facilitate access to the memory 604 requested by the processor 602. In some embodiments, the memory 604 includes a random access memory (RAM). Where appropriate, the RAM may be a volatile memory. Where appropriate, the RAM may be a dynamic RAM (DRAM) or a static RAM (SRAM). In addition, where appropriate, the RAM may be a single-port or multi-port RAM. The present disclosure contemplates any suitable RAM. Where appropriate, the memory 604 may include one or more memory devices 604. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory.

[0082] In some embodiments, database 606 includes a mass storage device for data or instructions. As an example and not limitation, database 606 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive, or a combination of two or more thereof. Where appropriate, database 606 may include a removable or non-removable (or fixed) medium. Where appropriate, database 606 may be inside or outside one or more computing devices 600. In some embodiments, database 606 is a non-volatile solid-state memory. In some embodiments, database 606 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), a flash memory, or a combination of two or more thereof. The present disclosure contemplates a mass database 606 in any suitable physical form. Where appropriate, database 606 may include one or more storage control units that facilitate communication between processor 602 and database 606. Where appropriate, database 606 may include one or more databases 606. Although this disclosure describes and illustrates particular storage devices, this disclosure contemplates any suitable storage devices.

[0083] In some embodiments, the I / O interface 608 includes: hardware, software, or both, which provides one or more interfaces for communicating between one or more computing devices 600 and one or more I / O devices. Where appropriate, one or more computing devices 600 may include one or more of these I / O devices. One or more of these I / O devices can enable communication between a person and one or more computing devices 600. As an example and not limitation, the I / O device may include a keyboard, a keypad, a microphone, a monitor, a mouse, a printer, a scanner, a speaker, a still camera, a stylus, a tablet computer, a touch screen, a trackball, a camera, another suitable I / O device, or a combination of two or more thereof. The I / O device may include one or more sensors. The present disclosure contemplates any suitable I / O device and any suitable I / O interface 608 for them. Where appropriate, the I / O interface 608 may include one or more devices or software drivers (enabling the processor 602 to drive one or more of these I / O devices). Where appropriate, the I / O interface 608 may include one or more I / O interfaces 608. Although this disclosure describes and illustrates a particular I / O interface, this disclosure contemplates any suitable I / O interface.

[0084] In some embodiments, the communication interface 610 includes: hardware, software, or both, which provides one or more interfaces for communication (such as, for example, packet-based communication) between one or more computing devices 600 and one or more other computing devices 600 or one or more networks. As an example and not limitation, the communication interface 610 may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network, or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network (such as a WI-FI network). The present disclosure contemplates any suitable network and any suitable communication interface 610 for it.

[0085] By way of example and not limitation, one or more computing devices 600 may communicate with a self-organizing network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), one or more parts of the Internet, or a combination of two or more thereof. One or more parts of one or more of these networks may be wired or wireless. As an example, one or more computing devices 600 may communicate with a wireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular phone network (such as, for example, a global system for mobile communications (GSM) network), other suitable wireless networks, or a combination of two or more thereof. Where appropriate, one or more computing devices 600 may include any suitable communication interface 610 for any of these networks. Where appropriate, the communication interface 610 may include one or more communication interfaces 610. Although the present disclosure describes and illustrates a specific communication interface, the present disclosure encompasses any suitable communication interface.

[0086] In some embodiments, bus 612 includes hardware, software, or both that couples one or more components of computing device 600 to each other. By way of example and not limitation, bus 612 may include an accelerated graphics port (AGP) or other graphics bus, an enhanced industry standard architecture (EISA) bus, a front side bus (FSB), a hypertransport (HT) interconnect, an industry standard architecture (ISA) bus, an INFINIBAND interconnect, a low pin count (LPC) bus, a memory bus, a micro channel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serial advanced technology attachment (SATA) bus, a video electronics standard association local (VLB) bus, another suitable bus, or a combination of two or more thereof. Where appropriate, bus 612 may include one or more buses 612. Although the present disclosure describes and illustrates a particular bus, the present disclosure contemplates any suitable bus or interconnect.

[0087] Herein, one or more computer-readable non-transitory storage media may include one or more semiconductor-based or other integrated circuits (ICs) (such as, for example, field programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard disk drives (HHDs), optical disks, optical disk drives (ODDs), magneto-optical disks, magneto-optical disk drives, floppy disks, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM drives, SECURE DIGITAL cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more thereof. Where appropriate, the computer-readable non-transitory storage medium may be a volatile storage medium, a non-volatile storage medium, or a combination of a volatile storage medium and a non-volatile storage medium.

[0088] As used herein, "or" is inclusive and not exclusive, unless expressly stated otherwise or the context indicates otherwise. Thus, as used herein, "A or B" means "A, B, or both," unless expressly stated otherwise or the context indicates otherwise. Furthermore, as used herein, "and" means both jointly and severally, unless expressly stated otherwise or the context indicates otherwise. Thus, as used herein, "A and B" means "A and B, jointly or severally," unless expressly stated otherwise or the context indicates otherwise.

[0089] As used herein, “automatically” and its derivatives mean “without human intervention” unless expressly indicated otherwise or the context indicates otherwise.

[0090] The embodiments disclosed herein are merely examples, and the scope of the present disclosure is not limited thereto. Embodiments according to the present disclosure are particularly disclosed in the attached claims for methods, storage media, systems, and computer program products, wherein any feature mentioned in one claim category (e.g., method) can be claimed for protection in another claim category (e.g., system). Dependents or references in the attached claims are selected only for formal reasons. However, any subject matter resulting from intentional reference to any preceding claim (particularly multiple dependents) may also be claimed, so any combination of claims and their features may be disclosed and claimed regardless of the dependents selected in the attached claims. The subject matter that may be claimed includes not only a combination of features as listed in the attached claims, but also any other combination of features in the claims, wherein each feature mentioned in the claims may be combined with any other feature or combination of other features in the claims. In addition, any embodiment and feature described or depicted herein may be claimed in a separate claim and / or in any combination with any embodiment or feature described or depicted herein or with any feature of the attached claims.

[0091] The scope of the present disclosure covers all changes, substitutions, variations, alterations and modifications to the exemplary embodiments described or shown herein that will be understood by those of ordinary skill in the art. The scope of the present disclosure is not limited to the exemplary embodiments described or shown herein. In addition, although the present disclosure describes and illustrates the corresponding embodiments herein as including specific parts, elements, features, functions, operations or steps, any of these embodiments may include any combination or arrangement of any parts, elements, features, functions, operations or steps described or shown anywhere herein that will be understood by those of ordinary skill in the art. In addition, in the appended claims, references to devices or systems or parts of devices or systems adapted to, arranged to, capable of, configured to, enabled to, operable to or operated to perform specific functions, cover the device, system, component, whether or not it or the specific function is activated, turned on or unlocked, as long as the device, system or component is so adapted, arranged, capable of, configured to, enabled to, operable or operated. In addition, although the present disclosure describes or illustrates the specific advantages provided by certain embodiments, certain embodiments may not provide, partially provide or provide all of these advantages.

Claims

1. A method for performing retinal volume measurement based on the Early Treatment Diabetic Retinopathy Study (ETDRS) grid by one or more computing devices: receiving an optical coherence tomography (OCT) image of a patient's retina and ETDRS mapping information, the ETDRS mapping information identifying one or more partitions of the ETDRS grid; segmenting the OCT image of the retina to identify one or more layer features corresponding to layers of the retina and one or more disease-related features associated with the one or more layer features; determining one or more volume measurements of the one or more disease-related features based on the segmented OCT image, wherein the one or more volume measurements correspond to the ETDRS mapping information; as well as A report is generated based on the one or more volume measurements.

2. The method of claim 1 , wherein the one or more layer features include Bruch's membrane (BM), the boundary of the myoid body and ellipsoidal inner segment (BMEIS), the ganglion cell layer-inner plexiform layer (GCL-IPL), the inner boundary outer photoreceptor (IB-OPR) layer, the outer boundary outer photoreceptor (OB-OPR) layer, the inner boundary retinal pigment epithelium (IB-RPE) layer, the outer boundary retinal pigment epithelium (OB-RPE) layer, the inner limiting membrane (ILM), the inner plexiform layer-inner nuclear layer (IPL-INL), the inner plexiform layer-outer nuclear layer (IPL-ONL), the inner segment / outer segment junction (ISJ-OSJ) layer, the outer plexiform layer-Henle fiber layer (OPL-HFL) or the retinal nerve fiber layer-ganglion cell layer (RNFL-GCL).

3. The method of claim 1, wherein the one or more disease-related features comprise one or more fluid features, the one or more fluid features comprising one or more of intraretinal fluid (IRF), subretinal fluid (SRF), or fluid corresponding to pigment epithelial detachment (PED).

4. The method of claim 1, wherein the one or more disease-related features comprise one or more deposit features, wherein the one or more deposit features comprise subretinal hyperreflective material (SHRM), intraretinal hyperreflective material (IHRM), or hyperreflective retinal foci (HRF). 5 . The method of claim 1 , further comprising identifying one or more biomarkers based on the one or more volume measurements.

6. The method according to claim 1, further comprising: receiving an en face image of the retina of the patient, wherein the en face image is associated with the OCT image; as well as Prior to generating the report, the one or more volume measurements and the ETDRS mapping information are mapped to the en face image.

7. The method of claim 6, wherein mapping the one or more volume measurements and the ETDRS mapping information to the en face image comprises associating one or more volume metrics with the en face image relative to one or more identified partitions.

8. The method of claim 1, wherein determining the one or more volume measurements comprises determining a total volume of the one or more disease-related features relative to at least one of the one or more identified partitions.

9. The method of claim 1, wherein determining the one or more volume measurements comprises determining a liquid volume of one or more liquid characteristics relative to at least one of the one or more identified partitions.

10. The method of claim 1, wherein determining the one or more volume measurements comprises determining a sediment volume of one or more sediment features relative to at least one of the one or more identified subareas.

11. The method of claim 1 , wherein determining the one or more volume measurements comprises determining a thickness of one or more of the layer features of the retina relative to at least one of the one or more identified partitions.

12. The method of claim 1, wherein determining the one or more volume measurements comprises determining a liquid range of one or more liquid characteristics relative to at least one of the one or more identified partitions.

13. The method of claim 1, wherein determining the one or more volume measurements comprises determining a quantity of one or more sediment features relative to at least one of the one or more identified partitions.

14. The method of claim 1, wherein determining the one or more volume measurements comprises determining an area of ​​the one or more disease-related features relative to at least one of the one or more identified partitions.

15. The method of claim 1, wherein determining the one or more volume measurements comprises determining the presence or absence of the one or more disease-related features relative to at least one of the one or more identified partitions.

16. The method of claim 1, wherein determining the one or more volumetric measurements comprises determining an area of ​​disruption of the one or more disease-related features relative to at least one of the one or more identified partitions.

17. The method of claim 1, further comprising classifying the patient as having diabetic retinopathy (DR) based on the one or more volume measurements.

18. The method of claim 17, wherein classifying the patient as having DR further comprises classifying the patient as having mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR, or proliferative diabetic retinopathy (PDR) based on the one or more volume measurements.

19. The method according to any one of claims 17 to 18, further comprising: receiving a second OCT image of the retina of the patient and second ETDRS mapping information, the second ETDRS mapping information identifying one or more partitions of the ETDRS grid; segmenting the second OCT image of the retina to identify one or more second layer features corresponding to layers of the retina and one or more second disease-related features associated with the one or more second layer features; determining one or more second volume measurements of the one or more second disease-related features based on the segmented second OCT image, wherein the one or more second volume measurements correspond to the second ETDRS mapping information; as well as A progression of diabetic retinopathy (DR) in the patient is determined based on the one or more second volume measurements.

20. The method of claim 1, further comprising classifying the patient as having diabetic macular edema (DME) based on the one or more volume measurements.

21. The method of any one of claims 17 to 20, further comprising generating a recommendation for treatment of the patient based on the one or more volume measurements or the one or more second volume measurements.

22. The method of any one of claims 17 to 21, wherein the treatment comprises an anti-vascular endothelial growth factor (anti-VEGF) antibody, an anti-vascular endothelial growth factor-A (anti-VEGF-A) antibody, or an anti-angiopoietin-2 (anti-Ang-2) antibody.

23. The method of claim 22, wherein the anti-VEGF-A antibody comprises faricimab-svoa.

24. The method of claim 22, wherein the anti-Ang-2 antibody comprises faricizumab.

25. The method of claim 22, wherein the anti-VEGF antibody is selected from the group consisting of ranibizumab, aflibercept, bruceizumab, bevacizumab, and pegaptanib sodium.

26. The method according to any one of claims 17 to 25, further comprising: A determination is made as to whether the patient is responding to the treatment based on the one or more volume measurements or the one or more second volume measurements.

27. The method according to any one of claims 17 to 26, further comprising identifying a precision cohort associated with the patient based on the one or more volume measurements or the one or more second volume measurements, wherein the precision cohort comprises a group of patients identified as being clinically similar to the patient based on the one or more volume measurements or the one or more second volume measurements.

28. The method of claim 1, wherein the OCT image comprises a time domain optical coherence tomography (TD-OCT) image or a spectral domain optical coherence tomography (SD-OCT) image.

29. The method of claim 1, wherein the OCT image comprises an image of the patient's fovea captured by an OCT ophthalmoscope, and wherein the image of the fovea is further divided into three concentric circles having diameters of approximately 1 millimeter (mm), approximately 3 mm, and approximately 6 mm, respectively, according to the ETDRS grid.

30. The method of claim 1, further comprising: receiving an optical coherence tomography angiography (OCT-A) image of the retina of the patient; as well as A retinal vascular 3D map of the retina is generated based on the OCT-A image and the one or more volume measurements.

31. The method of claim 1, further comprising: receiving a color fundus photography (CFP) image of the retina of the patient; as well as A composite image of the retina is generated based on the CFP image and the one or more volumetric measurements.

32. The method of claim 1, wherein the report comprises a table, a chart, an Extensible Markup Language (XML) file, a Hypertext Markup Language (HTML) file, a spreadsheet, a text file, an image file, a graphic file, a hyperlink, a web page, or any combination thereof.

33. The method of claim 32, further comprising transmitting the report to a computing device associated with a clinician.

34. The method of claim 32, further comprising transmitting the report to an electronic device associated with the patient.

35. A system for performing retinal volume measurement based on an Early Treatment Diabetic Retinopathy Study (ETDRS) grid, the system comprising one or more computing devices, the one or more computing devices comprising: one or more non-transitory computer-readable storage media including instructions; and One or more processors coupled to one or more storage media, the one or more processors configured to execute the instructions to: receiving an optical coherence tomography (OCT) image of a patient's retina and ETDRS mapping information, the ETDRS mapping information identifying one or more partitions of the ETDRS grid; segmenting the OCT image of the retina to identify one or more layer features corresponding to layers of the retina and one or more disease-related features detectable from the OCT image; determining one or more volume measurements of the one or more disease-related features associated with at least one of the layer features and corresponding to the ETDRS mapping information based on the segmented OCT image; as well as A report is generated based on the one or more volume measurements.

36. A system according to claim 35, wherein the one or more layer features include Bruch's membrane (BM), boundary of myoid body and ellipsoidal internal segment (BMEIS), ganglion cell layer-inner plexiform layer (GCL-IPL), inner boundary outer photoreceptor (IB-OPR) layer, outer boundary outer photoreceptor (OB-OPR) layer, inner boundary retinal pigment epithelium (IB-RPE) layer, outer boundary retinal pigment epithelium (OB-RPE) layer, inner limiting membrane (ILM), inner plexiform layer-inner nuclear layer (IPL-INL), inner plexiform layer-outer nuclear layer (IPL-ONL), inner segment / outer segment junction (ISJ-OSJ) layer, outer plexiform layer-Henle fiber layer (OPL-HFL) or retinal nerve fiber layer-ganglion cell layer (RNFL-GCL).

37. A system according to claim 35, wherein the one or more disease-related characteristics include one or more fluid characteristics, and the one or more fluid characteristics include one or more of intraretinal fluid (IRF), subretinal fluid (SRF), or fluid corresponding to pigment epithelial detachment (PED).

38. A system according to claim 35, wherein the one or more disease-related features include one or more deposit features, and the one or more deposit features include subretinal hyperreflective material (SHRM), intraretinal hyperreflective material (IHRM), or hyperreflective retinal foci (HRF).

39. The system of claim 35, wherein the instructions further comprise instructions to identify one or more biomarkers based on the one or more volume measurements.

40. The system of claim 35, wherein the instructions further comprise instructions to: receiving an en face image of the retina of the patient, wherein the en face image is associated with the OCT image; and Prior to generating the report, the one or more volume measurements and the ETDRS mapping information are mapped to the en face image.

41. The method of claim 40, wherein the instructions for mapping the one or more volume measurements and the ETDRS mapping information to the en face image further comprise instructions for associating one or more volume metrics with the en face image relative to one or more identified partitions.

42. A system according to claim 35, wherein the instructions for determining the one or more volume measurements further include instructions for determining the total volume of the one or more disease-related features relative to at least one of the one or more identified partitions.

43. A system according to claim 35, wherein the instructions for determining the one or more volume measurements further include instructions for determining the liquid volume of one or more liquid characteristics relative to at least one of the one or more identified partitions.

44. The system of claim 35, wherein the instructions for determining the one or more volume measurements further comprise instructions for determining a sediment volume of one or more sediment features relative to at least one of the one or more identified partitions.

45. A system according to claim 35, wherein the instructions for determining the one or more volume measurements further include instructions for determining the thickness of one or more of the layer features of the retina relative to at least one of the one or more identified partitions.

46. ​​A system according to claim 35, wherein the instructions for determining the one or more volume measurements further include instructions for determining the liquid range of one or more liquid characteristics relative to at least one of the one or more identified partitions.

47. The system of claim 35, wherein the instructions to determine the one or more volume measurements further comprise instructions to determine a quantity of one or more sediment features relative to at least one of the one or more identified partitions.

48. A system according to claim 35, wherein the instructions for determining the one or more volume measurements further include instructions for determining the area of ​​the one or more disease-related features relative to at least one of the one or more identified partitions.

49. A system according to claim 35, wherein the instructions for determining the one or more volume measurements further include instructions for determining the presence or absence of the one or more disease-related features relative to at least one of the one or more identified partitions.

50. A system according to claim 35, wherein the instructions for determining the one or more volume measurements further include instructions for determining an area of ​​damage of the one or more disease-related features relative to at least one of the one or more identified partitions.

51. The system of claim 35, wherein the instructions further comprise instructions to classify the patient as having diabetic retinopathy (DR) based on the one or more volume measurements.

52. A system according to claim 51, wherein the instructions for classifying the patient as having DR further include instructions for classifying the patient as having mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR, or proliferative diabetic retinopathy (PDR) based on the one or more volume measurements.

53. The system of any one of claims 51 to 52, wherein the instructions further comprise instructions to: receiving a second OCT image of the retina of the patient and second ETDRS mapping information, the second ETDRS mapping information identifying one or more partitions of the ETDRS grid; segmenting the second OCT image of the retina to identify one or more second layer features corresponding to layers of the retina and one or more second disease-related features detectable from the second OCT image; determining one or more second volume measurements of the one or more second disease-related features associated with at least one of the second layer features and corresponding to the second ETDRS mapping information based on the second segmented OCT image; and A progression of diabetic retinopathy (DR) in the patient is determined based on the one or more second volume measurements.

54. The system of claim 35, wherein the instructions further comprise instructions to classify the patient as having diabetic macular edema (DME) based on the one or more volume measurements.

55. The system of any one of claims 51 to 54, wherein the instructions further comprise instructions to generate a recommendation for treatment of the patient based on the one or more volume measurements or the one or more second volume measurements.

56. The system of any one of claims 51 to 55, wherein the treatment comprises an anti-vascular endothelial growth factor (anti-VEGF) antibody, an anti-vascular endothelial growth factor-A (anti-VEGF-A) antibody, or an anti-angiopoietin-2 (anti-Ang-2) antibody.

57. The system of claim 56, wherein the anti-VEGF-A antibody comprises faricizumab.

58. The system of claim 56, wherein the anti-Ang-2 antibody comprises faricizumab.

59. The system of claim 56, wherein the anti-VEGF antibody is selected from the group consisting of ranibizumab, aflibercept, bruceizumab, bevacizumab, and pegaptanib sodium.

60. The system of any one of claims 51 to 59, wherein the instructions further comprise instructions to: A determination is made as to whether the patient is responding to the treatment based on the one or more volume measurements or the one or more second volume measurements.

61. A system according to any one of claims 51 to 60, wherein the instructions further include instructions for identifying a precision cohort associated with the patient based on the one or more volume measurements or the one or more second volume measurements, wherein the precision cohort includes a group of patients identified as being clinically similar to the patient based on the one or more volume measurements or the one or more second volume measurements.

62. The system of claim 35, wherein the OCT image comprises a time domain optical coherence tomography (TD-OCT) image or a spectral domain optical coherence tomography (SD-OCT) image.

63. The system of claim 35, wherein the OCT image comprises an image of the patient's fovea captured by an OCT ophthalmoscope, and wherein the image of the fovea is further divided into three concentric circles having diameters of approximately 1 millimeter (mm), approximately 3 mm, and approximately 6 mm, respectively, according to the ETDRS grid.

64. The system of claim 35, wherein the instructions further comprise instructions to: receiving an en face image of the retina of the patient, wherein the en face image is associated with the OCT image; and Prior to generating the report, the one or more volume measurements and the ETDRS mapping information are mapped to the en face image.

65. The system of claim 35, wherein the instructions further comprise instructions to: receiving an optical coherence tomography angiography (OCT-A) image of the retina of the patient; and A retinal vascular 3D map of the retina is generated based on the OCT-A image and the one or more volume measurements.

66. The system of claim 35, wherein the instructions further comprise instructions to: receiving a color fundus photography (CFP) image of the retina of the patient; and A composite image of the retina is generated based on the CFP image and the one or more volumetric measurements.

67. The system of claim 35, wherein the report comprises a table, a chart, an Extensible Markup Language (XML) file, a Hypertext Markup Language (HTML) file, a spreadsheet, a text file, an image file, a graphic file, a hyperlink, a web page, or any combination thereof.

68. The system of claim 67, wherein the instructions further comprise instructions to transmit the report to a computing device associated with a clinician.

69. The system of claim 67, wherein the instructions further comprise instructions to transmit the report to an electronic device associated with the patient.

70. A non-transitory computer readable medium comprising instructions for performing retinal volume measurement based on an Early Treatment Diabetic Retinopathy Study (ETDRS) grid, the instructions, when executed by one or more processors of one or more computing devices, cause the one or more processors to: receiving an optical coherence tomography (OCT) image of a patient's retina and ETDRS mapping information, the ETDRS mapping information identifying one or more partitions of the ETDRS grid; segmenting the OCT image of the retina to identify one or more layer features corresponding to layers of the retina and one or more disease-related features detectable from the OCT image; determining one or more volume measurements of the one or more disease-related features associated with at least one of the layer features and corresponding to the ETDRS mapping information based on the segmented OCT image; as well as A report is generated based on the one or more volume measurements.

71. A non-transitory computer-readable medium according to claim 70, wherein the one or more layer features include Bruch's membrane (BM), the boundary of the myoid body and the ellipsoidal internal segment (BMEIS), the ganglion cell layer-inner plexiform layer (GCL-IPL), the inner boundary outer photoreceptor (IB-OPR) layer, the outer boundary outer photoreceptor (OB-OPR) layer, the inner boundary retinal pigment epithelium (IB-RPE) layer, the outer boundary retinal pigment epithelium (OB-RPE) layer, the inner limiting membrane (ILM), the inner plexiform layer-inner nuclear layer (IPL-INL), the inner plexiform layer-outer nuclear layer (IPL-ONL), the inner segment / outer segment junction (ISJ-OSJ) layer, the outer plexiform layer-Henle fiber layer (OPL-HFL) or the retinal nerve fiber layer-ganglion cell layer (RNFL-GCL).

72. A non-transitory computer-readable medium according to claim 70, wherein the one or more disease-related characteristics include one or more fluid characteristics, and the one or more fluid characteristics include one or more of intraretinal fluid (IRF), subretinal fluid (SRF), or fluid corresponding to pigment epithelial detachment (PED).

73. A non-transitory computer-readable medium according to claim 70, wherein the one or more disease-related characteristics include one or more deposit characteristics, and the one or more deposit characteristics include subretinal highly reflective material (SHRM), intraretinal highly reflective material (IHRM), or highly reflective retinal foci (HRF).

74. The non-transitory computer readable medium of claim 70, wherein the instructions further comprise instructions to identify one or more biomarkers based on the one or more volume measurements.

75. The non-transitory computer readable medium of claim 70, wherein the instructions further comprise instructions to: receiving an en face image of the retina of the patient, wherein the en face image is associated with the OCT image; and Prior to generating the report, the one or more volume measurements and the ETDRS mapping information are mapped to the en face image.

76. The non-transitory computer-readable medium of claim 75, wherein the instructions for mapping the one or more volume measurements and the ETDRS mapping information to the en face image further comprise instructions for associating one or more volume metrics with the en face image relative to one or more identified partitions.

77. A non-transitory computer-readable medium according to claim 70, wherein the instructions for determining the one or more volume measurements further include instructions for determining the total volume of the one or more disease-related features relative to at least one of the one or more identified partitions.

78. A non-transitory computer-readable medium according to claim 70, wherein the instructions for determining the one or more volume measurements further include instructions for determining the liquid volume of one or more liquid characteristics relative to at least one of the one or more identified partitions.

79. The non-transitory computer-readable medium of claim 70, wherein the instructions for determining the one or more volume measurements further comprise instructions for determining a sediment volume of one or more sediment features relative to at least one of the one or more identified partitions.

80. The non-transitory computer-readable medium of claim 70, wherein the instructions for determining the one or more volume measurements further comprise instructions for determining a thickness of one or more of the layer features of the retina relative to at least one of the one or more identified partitions.

81. A non-transitory computer-readable medium according to claim 70, wherein the instructions for determining the one or more volume measurements further include instructions for determining a liquid range of one or more liquid characteristics relative to at least one of the one or more identified partitions.

82. The non-transitory computer-readable medium of claim 70, wherein the instructions for determining the one or more volume measurements further comprise instructions for determining a quantity of one or more sediment features relative to at least one of the one or more identified partitions.

83. The non-transitory computer-readable medium of claim 70, wherein the instructions for determining the one or more volume measurements further comprise instructions for determining the area of ​​the one or more disease-related features relative to at least one of the one or more identified partitions.

84. The non-transitory computer-readable medium of claim 70, wherein the instructions for determining the one or more volume measurements further comprise instructions for determining the presence or absence of the one or more disease-related features relative to at least one of the one or more identified partitions.

85. The non-transitory computer-readable medium of claim 70, wherein the instructions for determining the one or more volume measurements further comprise instructions for determining an area of ​​damage of the one or more disease-related features relative to at least one of the one or more identified partitions.

86. The non-transitory computer readable medium of claim 70, wherein the instructions further comprise instructions to classify the patient as having diabetic retinopathy (DR) based on the one or more volume measurements.

87. A non-transitory computer-readable medium according to claim 86, wherein the instructions for classifying the patient as having DR further include instructions for classifying the patient as having mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR, or proliferative diabetic retinopathy (PDR) based on the one or more volume measurements.

88. The non-transitory computer readable medium of any one of claims 86 to 87, wherein the instructions further comprise instructions to: receiving a second OCT image of the retina of the patient and second ETDRS mapping information, the second ETDRS mapping information identifying one or more partitions of the ETDRS grid; segmenting the second OCT image of the retina to identify one or more second layer features corresponding to layers of the retina and one or more second disease-related features detectable from the second OCT image; determining one or more second volume measurements of the one or more second disease-related features associated with at least one of the second layer features and corresponding to the second ETDRS mapping information based on the second segmented OCT image; and A progression of diabetic retinopathy (DR) in the patient is determined based on the one or more second volume measurements.

89. The non-transitory computer-readable medium of claim 70, wherein the instructions further comprise instructions for classifying the patient as having diabetic macular edema (DME) based on the one or more volume measurements.

90. The non-transitory computer-readable medium of any one of claims 86 to 90, wherein the instructions further comprise instructions to generate a recommendation for treatment of the patient based on the one or more volume measurements or the one or more second volume measurements.

91. The non-transitory computer-readable medium of any one of claims 86 to 90, wherein the treatment comprises an anti-vascular endothelial growth factor (anti-VEGF) antibody, an anti-vascular endothelial growth factor-A (anti-VEGF-A) antibody, or an anti-angiopoietin-2 (anti-Ang-2) antibody.

92. The non-transitory computer readable medium of claim 91, wherein the anti-VEGF-A antibody comprises faricizumab.

93. The non-transitory computer readable medium of claim 91, wherein the anti-Ang-2 antibody comprises faricizumab.

94. The non-transitory computer readable medium of claim 91, wherein the anti-VEGF antibody is selected from the group consisting of ranibizumab, aflibercept, bruceizumab, bevacizumab, and pegaptanib sodium.

95. The non-transitory computer readable medium of any one of claims 86 to 94, wherein the instructions further comprise instructions to: A determination is made as to whether the patient is responding to the treatment based on the one or more volume measurements or the one or more second volume measurements.

96. A non-transitory computer-readable medium according to any one of claims 86 to 95, wherein the instructions further include instructions for identifying a precision cohort associated with the patient based on the one or more volume measurements or the one or more second volume measurements, wherein the precision cohort includes a group of patients identified as being clinically similar to the patient based on the one or more volume measurements or the one or more second volume measurements.

97. The non-transitory computer-readable medium of claim 70, wherein the OCT image comprises a time domain optical coherence tomography (TD-OCT) image or a spectral domain optical coherence tomography (SD-OCT) image.

98. The non-transitory computer-readable medium of claim 70, wherein the OCT image comprises an image of the patient's fovea captured by an OCT ophthalmoscope, and wherein the image of the fovea is further divided into three concentric circles having diameters of approximately 1 millimeter (mm), approximately 3 mm, and approximately 6 mm, respectively, according to the ETDRS grid.

99. The non-transitory computer readable medium of claim 70, wherein the instructions further comprise instructions to: receiving an en face image of the retina of the patient, wherein the en face image is associated with the OCT image; and Prior to generating the report, the one or more volume measurements and the ETDRS mapping information are mapped to the en face image.

100. The non-transitory computer readable medium of claim 70, wherein the instructions further comprise instructions to: receiving an optical coherence tomography angiography (OCT-A) image of the retina of the patient; and A retinal vascular 3D map of the retina is generated based on the OCT-A image and the one or more volume measurements.

101. The non-transitory computer readable medium of claim 70, wherein the instructions further comprise instructions to: receiving a color fundus photography (CFP) image of the retina of the patient; and A composite image of the retina is generated based on the CFP image and the one or more volumetric measurements.

102. The non-transitory computer-readable medium of claim 70, wherein the report comprises a table, a chart, an Extensible Markup Language (XML) file, a Hypertext Markup Language (HTML) file, a spreadsheet, a text file, an image file, a graphic file, a hyperlink, a web page, or any combination thereof.

103. The non-transitory computer-readable medium of claim 102, wherein the instructions further comprise instructions to transmit the report to a computing device associated with a clinician.

104. The non-transitory computer-readable medium of claim 102, wherein the instructions further comprise instructions to transmit the report to an electronic device associated with the patient.

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