Fully Automated Segmentation of Human Eyeball Using Three-Dimensional U-Net in T2 Magnetic Resonance Imaging.

ABSTRACT:

Purpose

To develop and validate a fully automated deep-learning-based tool for segmentation of the human eyeball using a three-dimensional (3D) U-Net, compare its performance to semiautomatic segmentation ground truth and a two-dimensional (2D) U-Net, and analyze age and sex differences in eyeball volume, as well as gaze-dependent volume consistency in normal subjects.

Methods

We retrospectively collected 474 magnetic resonance imaging (MRI) scans, including different gazing scans, from 119 patients. A 10-fold cross-validation was applied to separate the dataset into training, test, and validation sets for both the 3D U-Net and 2D U-Net. Performance accuracy was measured using four quantitative metrics compared to the ground truth, and Bland-Altman plot analysis was conducte

SUBMITTER: Yang JJ 

PROVIDER: S-EPMC10664726 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature