Alcon, Inc.

Parisa Rabbani

Sahar Hosseinzadeh Kassani

Qiang Li

Hugang Ren

Muhammad Al-Qaisi

Wei Wei

Lance Noller

An imaging system includes a housing assembly having a head unit configured to be at least partially directed towards a target site. An optical coherence tomography (OCT) module and a visualization module are located in the housing assembly and configured to respectively obtain OCT data and visualization data of the target site. The system includes a controller configured to generate a scanning pattern for a region of calibration selected in a calibration target. OCT data of the region of calibration is synchronously acquired. The controller is configured to obtain a projected two-dimensional OCT image of the region of calibration based on the OCT data, as an inverse mean-intensity projection. The controller is configured to register the projected two-dimensional OCT image to a corresponding view extracted from the visualization data, via a cascaded image registration process having a coarse registration stage and a fine registration stage.

Calibration of imaging system with combined optical coherence tomography and visualization module

The techniques described herein provide improved techniques for segmenting corneal epithelium layer. A method includes receiving an optical coherence tomography (OCT) image of an eye; generating, based on the OCT image, a binarized image of the eye; generating, based on the binarized image of the eye and the OCT image, a binary mask of a cornea of the eye; segmenting, based on the binary mask of the cornea of the eye, an anterior cornea of the eye on the OCT image; generating, based on the OCT image and the segmented anterior cornea, a binary mask for an epithelium layer of the eye; segmenting, based on the binary mask for the epithelium layer of the eye, a Bowman's layer in the cornea of the eye on the OCT image; and causing the segmented anterior cornea and the segmented Bowman's layer data to be used for generation of an epithelium map.

Corneal epithelium segmentation in optical coherence tomography images

Systems and methods are presented for providing a machine learning model for segmenting an optical coherence tomography (OCT) image. A first OCT image is obtained, and then labeled with identified boundaries associated with different tissues in the first OCT image using a graph search algorithm. Portions of the labeled first OCT image are extracted to generate a first plurality of image tiles. A second plurality of image tiles is generated by manipulating at least one image tile from the first plurality of image tiles, such as by rotating and/or flipping the at least one image tile. The machine learning model is trained using the first plurality of image tiles and the second plurality of image tiles. The trained machine learning model is used to perform segmentation in a second OCT image.

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Aliso Viejo, CA, United States of America

Parisa Rabbani