Estimating Uncertainty of Geographic Atrophy Segmentations with Bayesian Deep Learning.

ABSTRACT:

Purpose

To apply methods for quantifying uncertainty of deep learning segmentation of geographic atrophy (GA).

Design

Retrospective analysis of OCT images and model comparison.

Participants

One hundred twenty-six eyes from 87 participants with GA in the SWAGGER cohort of the Nonexudative Age-Related Macular Degeneration Imaged with Swept-Source OCT (SS-OCT) study.

Methods

The manual segmentations of GA lesions were conducted on structural subretinal pigment epithelium en face images from the SS-OCT images. Models were developed for 2 approximate Bayesian deep learning techniques, Monte Carlo dropout and ensemble, to assess the uncertainty of GA semantic segmentation and compared to a traditional deep learning model.

Main outcome measures

Model performanc

SUBMITTER: Spaide T 

PROVIDER: S-EPMC11459066 | biostudies-literature | 2025 Jan-Feb

REPOSITORIES: biostudies-literature