Large-scale convolutional neural network for clinical target and multi-organ segmentation in gynecologic brachytherapy via multi-stage learning.

ABSTRACT:

Background

Accurate segmentation of high-risk clinical target volume (HRCTV) and organs-at-risk (OARs) is crucial for optimizing gynecologic brachytherapy treatment planning. However, performing this segmentation on Computed Tomography (CT) images remains particularly challenging due to anatomical variability, limited soft-tissue contrast, and the scarcity of annotated datasets. Compared to other radiotherapy domains, CT-based gynecologic brachytherapy segmentation is notably underrepresented in benchmarking studies.

Purpose

This study aims to improve the segmentation of HRCTV and OARs in gynecologic brachytherapy by introducing GynBTNet, a multi-stage learning framework. Through large-scale self-supervised pretraining and progressive finetuning, the model is designed to enh

SUBMITTER: Hu M 

PROVIDER: S-EPMC12681083 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature