Carl Zeiss Meditec Gmbh

Carl Zeiss Meditec Ag

Homayoun Bagherinia

Luis De Sisternes

Paul Francis Stetson

Utkarsh Sharma

Nathan Shemonski

Mary K Durbin

Vincent Michael Patella

Lin An

Yingjian Wang

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.

Method and system for axial motion correction

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.

Systems and methods for reducing artifacts in oct angiography images

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.

Methods and systems to detect and classify retinal structures in interferometric imaging data

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.

Systems and methods for improved anterior segment OCT imaging

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.

Methods for detection and enhanced visualization of pathologies in a human eye

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.

Systems and methods for reducing artifacts in OCT angiography images

A method and system for correction of decorrelation tail artifacts in optical coherence tomography (OCT) angiography volumetric data defines a movable target subvolume within the OCT-A volumetric data. The target subvolume is axially moveable within the OCT-A volumetric data in discrete axial steps. At each axial step, a reference subvolume corresponding to a depth location in the OCT A volumetric data is defined axially offset from the target subvolume. The reference subvolume may be defined within the OCT A volumetric data, or defined within a different (previously corrected) OCT-A volume. Irrespective, corrected OCT-A data that corrects for decorrelation tail artifacts in the target subvolume is defined using information in the reference subvolume and information in the target subvolume.

Correction of decorrelation tail artifacts in a whole OCT-A volume

Various systems and methods for improved OCT angiography imaging are described. An example method of identifying intraretinal fluid in optical coherence tomography (OCT) image data of an eye includes collecting OCT image data using an OCT system. The data includes at least one cluster scan containing OCT image data collected at approximately same set of locations on the sample. A first motion contrast image is generated by applying a first OCT angiography processing technique to the cluster scan to highlight motion contrast in the sample. A second motion contrast image is generated by applying a second OCT angiography processing technique to the cluster scan to highlight motion contrast in the sample. An image displaying intraretinal fluid in the eye is generated using the first and second motion contrast images and then displayed or stored or a further analysis thereof.

Methods for high sensitivity flow visualization

Various methods for automatically identifying the Schwalbe's line location in an optical coherence tomography (OCT) image of the anterior chamber of an eye are described. In one example method, the posterior corneal surface in the ROI is segmented by using one or more segmentation approaches to produce a segmented output. A curvature is computed at each segmented point to identify a set of local curvature maxima locations. Features at each local curvature maxima location are evaluated. The Schwalbe's line location is identified using the evaluated features at each maxima location. Other methods for identifying the Schwalbe's line location discussed in the present application are based on identification of a location of maximum curvature in the curvature function and identification of a maximum distance to the convex-hull of a model fit.

Methods and systems for automatically identifying the Schwalbe's line

An efficient method of evaluating the level of contrast of an OCT dataset is presented. The method develops a metric to segregate useful and not-so-useful data in one or more OCT B-scans, in order to reduce spurious subsequent analyses of the data by downstream segmentation algorithms. It is designed to be fast and efficient and is applied to determining autofocus of an OCT instrument real-time and in identifying a real image from its complex conjugate twin.

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Oakland, CA, United States of America

Homayoun Bagherinia