US 11,727,561 B2
Automated methods for the objective quantification of retinal characteristics by retinal region and diagnosis of retinal pathology
Shlomit Schaal, Prospect, KY (US); Ayman El-Baz, Louisville, KY (US); and Amir Reza Hajrasouliha, Chicago, IL (US)
Assigned to University of Louisville Research Foundation, Inc., Louisville, KY (US)
Filed by UNIVERSITY OF LOUISVILLE RESEARCH FOUNDATION, INC., Louisville, KY (US)
Filed on Dec. 30, 2020, as Appl. No. 17/138,222.
Application 17/138,222 is a continuation of application No. 15/776,385, granted, now 10,891,729, previously published as PCT/US2016/020280, filed on Mar. 1, 2016.
Claims priority of provisional application 62/256,980, filed on Nov. 18, 2015.
Prior Publication US 2021/0125336 A1, Apr. 29, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 10/00 (2022.01); G06T 7/00 (2017.01); G06T 7/12 (2017.01); G06T 7/143 (2017.01); G06T 7/168 (2017.01); A61B 3/10 (2006.01); G06T 7/149 (2017.01)
CPC G06T 7/0012 (2013.01) [A61B 3/102 (2013.01); G06T 7/12 (2017.01); G06T 7/143 (2017.01); G06T 7/149 (2017.01); G06T 7/168 (2017.01); G06T 2207/10101 (2013.01); G06T 2207/20064 (2013.01); G06T 2207/20121 (2013.01); G06T 2207/20124 (2013.01); G06T 2207/30041 (2013.01)] 25 Claims
OG exemplary drawing
 
1. A retinal OCT segmentation algorithm for segmenting a test retinal OCT image into 13 retinal regions, the algorithm comprising:
(a) providing a probabilistic shape and intensity model derived from a plurality of OCT images generated from control subjects and segmented into 13 retinal regions;
(b) aligning the test OCT image with the probabilistic shape and intensity model according to retinal region shape;
(c) comparing pixels of the aligned test OCT image with the corresponding pixels in the control model by intensity, establishing a probable regional fit, and refining regional margins by estimating marginal density distribution with a linear combination a linear combination of discrete Gaussians (LCDG) to generate an initial regional segmentation map of the test OCT image comprising defined retinal regions; and
(d) fitting the initial segmentation map with a statistical model that accounts for spatial information to achieve a final segmentation map comprising 13 retinal regions.