Effective encoder-decoder neural network for segmentation of orbital tissue in computed tomography images of Graves' orbitopathy patients.

ABSTRACT:

Purpose

To propose a neural network (NN) that can effectively segment orbital tissue in computed tomography (CT) images of Graves' orbitopathy (GO) patients.

Methods

We analyzed orbital CT scans from 701 GO patients diagnosed between 2010 and 2019 and devised an effective NN specializing in semantic orbital tissue segmentation in GO patients' CT images. After four conventional (Attention U-Net, DeepLab V3+, SegNet, and HarDNet-MSEG) and the proposed NN train the various manual orbital tissue segmentations, we calculated the Dice coefficient and Intersection over Union for comparison.

Results

CT images of the eyeball, four rectus muscles, the optic nerve, and the lacrimal gland tissues from all 701 patients were analyzed in this study. In the axial image with the large

SUBMITTER: Lee SH 

PROVIDER: S-EPMC10171592 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature