Application of Deep Learning Methods for Binarization of the Choroid in Optical Coherence Tomography Images.

ABSTRACT:

Purpose

The purpose of this study was to develop a deep learning model for automatic binarization of the choroidal tissue, separating choroidal blood vessels from nonvascular stromal tissue, in optical coherence tomography (OCT) images from healthy young subjects.

Methods

OCT images from an observational longitudinal study of 100 children were used for training, validation, and testing of 5 fully semantic networks, which provided a binarized output of the choroid. These outputs were compared with ground truth images, generated from a local binarization technique after manually optimizing the analysis window size for each individual image. The performance was evaluated using accuracy and repeatability metrics. The methods were also compared with a fixed window size local bina

SUBMITTER: Muller J 

PROVIDER: S-EPMC8857621 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature