Invention Grant
- Patent Title: Deep learning for optical coherence tomography segmentation
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Application No.: US17127651Application Date: 2020-12-18
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Publication No.: US11562484B2Publication Date: 2023-01-24
- Inventor: Muhammad K. Al-Qaisi , Parisa Rabbani , Hugang Ren
- Applicant: Alcon Inc.
- Applicant Address: CH Fribourg
- Assignee: Alcon Inc.
- Current Assignee: Alcon Inc.
- Current Assignee Address: CH Fribourg
- Main IPC: G06T7/00
- IPC: G06T7/00 ; G06T7/13 ; G06T3/60

Abstract:
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.
Public/Granted literature
- US20210192732A1 DEEP LEARNING FOR OPTICAL COHERENCE TOMOGRAPHY SEGMENTATION Public/Granted day:2021-06-24
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