Invention Grant
- Patent Title: Neural-network-driven topology for optical coherence tomography segmentation
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Application No.: US17594191Application Date: 2020-03-25
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Publication No.: US12056877B2Publication Date: 2024-08-06
- Inventor: Yufan He , Jerry L. Prince , Aaron Carass
- Applicant: The Johns Hopkins University
- Applicant Address: US MD Baltimore
- Assignee: The Johns Hopkins University
- Current Assignee: The Johns Hopkins University
- Current Assignee Address: US MD Baltimore
- Agency: Harrity & Harrity, LLP
- International Application: PCT/US2020/024622 2020.03.25
- International Announcement: WO2020/210028A 2020.10.15
- Date entered country: 2021-10-06
- Main IPC: G06K9/00
- IPC: G06K9/00 ; A61B3/00 ; A61B3/10 ; A61B3/12 ; G06N3/08 ; G06T7/00 ; G06T7/11 ; G06T7/12 ; G06T7/143 ; G06T7/73 ; G16H20/10 ; G16H70/60

Abstract:
A device receives a two-dimensional (2-D) image that depicts a cross-sectional view of a macula comprised of layers and boundaries to segment the layers, and determines spatial coordinates of the 2-D image that include x-coordinates and y-coordinates. The device uses a data model, that has been trained using a deep learning technique, to process the 2-D image and the spatial coordinates to generate boundary maps that indicate likelihoods of voxels of the 2-D image being in positions that are part of particular boundaries. The device determines, by analyzing the boundary maps, an initial set of boundary positions, and determines a final set of boundary positions by using a topological order identification technique to refine the initial set of boundary positions. The device determines the thickness levels of the layers of the macula based on the final set of boundary positions, and performs one or more actions based on the thickness levels.
Public/Granted literature
- US20220156941A1 A NEURAL-NETWORK-DRIVEN TOPOLOGY FOR OPTICAL COHERENCE TOMOGRAPHY SEGMENTATION Public/Granted day:2022-05-19
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