Abstract: System/Method/Device for labelling images in an automated manner to satisfy a performance of a different algorithm and then applying active learning to learn a deep learning model which would enable ‘real-time’ operation of quality assessment and with high accuracy.

Abstract: A system/method/device for registering two OCT data sets defines multiple image pairs of corresponding 2D representations of one or more corresponding sub-volumes in the two OCT data sets. Matching landmarks in the multiple image pairs are identified, as a group, and a set of transformation parameters are defined based on matching landmarks from all of the image pairs. The two OCT data sets may then be registered based on the set of transformation parameters.

Abstract: Various methods for reducing artifacts in OCT images of an eye are described. In one exemplary method, three dimensional OCT image data of the eye is collected. Motion contrast information is calculated in the OCT image data. A first image and a second image are created from the motion contrast information. The first and the second images depict vasculature information regarding one or more upper portions and one or more deeper portions, respectively. The second image contains artifacts. Using an inverse calculation, a third image is determined that can be mixed with the first image to generate the second image. The third image depicts vasculature regarding the same one or more deeper portions as the second image but has reduced artifacts. A depth dependent correction method is also described that can be used in combination with the inverse problem based method to further reduce artifacts in OCT angiography images.

Abstract: A method and system for correcting axial motion in optical coherence tomography (OCT) data is provided. The method includes collecting, by a processor disposed of in an OCT device, a volume scan of an eye; segmenting a first retinal layer within the volume scan; applying an algorithm for periodic pattern removal of OCT data in the first retinal layer by determining a model of a Fourier transform applicable to a segment of the first retinal layer; and removing transform frequencies associated with the OCT data using the model of the Fourier transform; determining a measure of an amount of axial motion in accordance with a difference of an amount of OCT data captured on a surface of the first retinal layer before and after application of the algorithm for periodic pattern removal; and correcting, the amount of axial motion in the OCT data of the first retinal layer.

Abstract: A System/Method/Device for segmenting the choroid-scleral layer from an optical coherent tomography (OCT) volume scan. The present system uses a deep learning machine model based on a neural network that include multiple convolution layers, but no deconvolution layers. Rather, the present neural network is based on a novel architecture based on the discrete cosine transform.

Abstract: A System/Method/Device for automatic retinal layer segmentation from optical coherence tomography (OCT) implementing a deep learning machine model defined by any of multiple neural networks. The neural networks may use the feature of “attention”, and more specifically self-attention, such as by using transformers, to reduce the size of the OCT data and make the process more efficient. Additionally, new methods of data augmentation suitable for OCT data are presented.

Abstract: A system and method for creating a composite retinal thickness map of an eye, including collecting, by an optical coherence tomography (OCT) device, a set of a plurality of optical coherence tomography (OCT) volume scans of the eye. At least one set of the plurality of OCT volume scans is collected by the OCT device by: segmenting a target upper retinal layer and a target lower retinal layer which is lower than the target upper retinal layer within an OCT volume scan; determining a thickness map of a candidate between the upper retinal layer and lower retinal layer, and a confidence map associated with the thickness map; selecting at least one thickness map as a reference thickness map; registering at least one thickness map to the reference thickness map; and selectively combining a region of the thickness map with a corresponding region of the reference thickness map to define a composite retinal thickness map.

Abstract: Methods and systems are presented to analyze a retinal image of an eye and assigns features to known anatomical structures such as retinal layers. One example method includes receiving interferometric image data of an eye. A set of features is identified in the image data. A first subset of identified features is associated with known retinal structures using prior knowledge. A first set of characteristic metrics is determined of the first subset of features. A second set of characteristic metrics is determined of a second subset of features. Using the characteristic metrics of the first and the second sets, the second subset of features is associated with the retinal structures. Another example method includes dividing interferometric image data into patches. The image data in each patch is segmented to identify one or more layer boundaries. The segmentation results from each patch are stitched together into a single segmentation dataset.

Abstract: Various methods and systems for improved anterior segment optical coherence tomography (OCT) imaging are described. One example method includes collecting a set of B-scans over a range of different transverse locations on the cornea, segmenting each B-scan to identify an anterior corneal layer and an outer edge of Bowman's layer, calculating thickness values for each B-scan by computing the distance from the anterior corneal layer to the outer edge of the Bowman's layer, combining the thickness values from the B-scans to create a polar epithelial thickness map, converting the polar epithelial thickness map to a Cartesian epithelial thickness map using a fitting method, and storing or displaying the Cartesian epithelial thickness map or information derived from the Cartesian epithelial thickness map.

Abstract: A system and method for ophthalmic motion tracking. An anchor point and multiple auxiliary points are selected from a reference image. Individual live images in a series of images are then searched for matches of the anchor point and auxiliary point. First, the anchor point is found, and then searches for individual auxiliary points is limited to a search window defined by the known distance and/or orientation of the sought auxiliary point relative to the anchor point.

Abstract: Various methods for the detection and enhanced visualization of a particular structure or pathology of interest in a human eye are discussed in the present disclosure. An example method to visualize a given pathology (e.g., CNV) in an eye includes collecting optical coherence tomography (OCT) image data of the eye from an OCT system. The OCT image data is segmented to identify two or more retinal layer boundaries located in the eye. The two or more retinal layer boundaries are located at different depth locations in the eye. One of the identified layer boundaries is moved and reshaped to optimize visualization of the pathology located between the identified layer boundaries. The optimized visualization is displayed or stored or for a further analysis thereof.

Abstract: Various methods and systems for improved anterior segment optical coherence tomography (OCT) imaging are described. One example method includes collecting a set of B-scans over a range of different transverse locations on the cornea, segmenting each B-scan to identify an anterior corneal layer and an outer edge of Bowman's layer, calculating thickness values for each B-scan by computing the distance from the anterior corneal layer to the outer edge of the Bowman's layer, combining the thickness values from the B-scans to create a polar epithelial thickness map, converting the polar epithelial thickness map to a Cartesian epithelial thickness map using a fitting method, and storing or displaying the Cartesian epithelial thickness map or information derived from the Cartesian epithelial thickness map.

Abstract: Methods and systems are presented to analyze a retinal image of an eye and assigns features to known anatomical structures such as retinal layers. One example method includes receiving interferometric image data of an eye. A set of features is identified in the image data. A first subset of identified features is associated with known retinal structures using prior knowledge. A first set of characteristic metrics is determined of the first subset of features. A second set of characteristic metrics is determined of a second subset of features. Using the characteristic metrics of the first and the second sets, the second subset of features is associated with the retinal structures. Another example method includes dividing interferometric image data into patches. The image data in each patch is segmented to identify one or more layer boundaries. The segmentation results from each patch are stitched together into a single segmentation dataset.

Abstract: Various methods and systems for improved anterior segment optical coherence tomography (OCT) imaging are described. One example method includes collecting a set of OCT data of the cornea of the eye; segmenting the set of OCT data to identify one or more corneal layers; fitting a two-dimensional model of corneal surfaces to the one or more corneal layers; determining motion-correction parameters by minimizing error between the one or more corneal layers and the two-dimensional model of the corneal surfaces; and creating a motion-corrected corneal image dataset from the set of OCT data using the motion-correction parameters. The motion-corrected corneal image dataset can be used to create a model of the anterior and/or posterior surfaces of the cornea. The model of the cornea is used to generate high density and motion-artifact free epithelial thickness maps, which are used for identifying or quantifying pathology such as keratoconus.

Abstract: Methods and systems are presented to analyze a retinal image of an eye and assigns features to known anatomical structures such as retinal layers. One example method includes receiving interferometric image data of an eye. A set of features is identified in the image data. A first subset of identified features is associated with known retinal structures using prior knowledge. A first set of characteristic metrics is determined of the first subset of features. A second set of characteristic metrics is determined of a second subset of features. Using the characteristic metrics of the first and the second sets, the second subset of features is associated with the retinal structures. Another example method includes dividing interferometric image data into patches. The image data in each patch is segmented to identify one or more layer boundaries. The segmentation results from each patch are stitched together into a single segmentation dataset.

Abstract: Various methods for the detection and enhanced visualization of a particular structure or pathology of interest in a human eye are discussed in the present disclosure. An example method to visualize a given pathology (e.g., CNV) in an eye includes collecting optical coherence tomography (OCT) image data of the eye from an OCT system. The OCT image data is segmented to identify two or more retinal layer boundaries located in the eye. The two or more retinal layer boundaries are located at different depth locations in the eye. One of the identified layer boundaries is moved and reshaped to optimize visualization of the pathology located between the identified layer boundaries. The optimized visualization is displayed or stored or for a further analysis thereof.

Abstract: Various methods for the detection and enhanced visualization of a particular structure or pathology of interest in a human eye are discussed in the present disclosure. An example method to visualize a given pathology (e.g., CNV) in an eye includes collecting optical coherence tomography (OCT) image data of the eye from an OCT system. The OCT image data is segmented to identify two or more retinal layer boundaries located in the eye. The two or more retinal layer boundaries are located at different depth locations in the eye. One of the identified layer boundaries is moved and reshaped to optimize visualization of the pathology located between the identified layer boundaries. The optimized visualization is displayed or stored or for a further analysis thereof.

Abstract: Various methods for reducing artifacts in OCT images of an eye are described. In one exemplary method, three dimensional OCT image data of the eye is collected. Motion contrast information is calculated in the OCT image data. A first image and a second image are created from the motion contrast information. The first and the second images depict vasculature information regarding one or more upper portions and one or more deeper portions, respectively. The second image contains artifacts. Using an inverse calculation, a third image is determined that can be mixed with the first image to generate the second image. The third image depicts vasculature regarding the same one or more deeper portions as the second image but has reduced artifacts. A depth dependent correction method is also described that can be used in combination with the inverse problem based method to further reduce artifacts in OCT angiography images.

Abstract: Various methods for reducing artifacts in OCT images of an eye are described. In one exemplary method, three dimensional OCT image data of the eye is collected. Motion contrast information is calculated in the OCT image data. A first image and a second image are created from the motion contrast information. The first and the second images depict vasculature information regarding one or more upper portions and one or more deeper portions, respectively. The second image contains artifacts. Using an inverse calculation, a third image is determined that can be mixed with the first image to generate the second image. The third image depicts vasculature regarding the same one or more deeper portions as the second image but has reduced artifacts. A depth dependent correction method is also described that can be used in combination with the inverse problem based method to further reduce artifacts in OCT angiography images.

Abstract: Various methods for reducing artifacts in OCT images of an eye are described. In one exemplary method, three dimensional OCT image data of the eye is collected. Motion contrast information is calculated in the OCT image data. A first image and a second image are created from the motion contrast information. The first and the second images depict vasculature information regarding one or more upper portions and one or more deeper portions, respectively. The second image contains artifacts. Using an inverse calculation, a third image is determined that can be mixed with the first image to generate the second image. The third image depicts vasculature regarding the same one or more deeper portions as the second image but has reduced artifacts. A depth dependent correction method is also described that can be used in combination with the inverse problem based method to further reduce artifacts in OCT angiography images.