Swin Transformer-based automatic delineation of the hippocampus by MRI in hippocampus-sparing whole-brain radiotherapy.

ABSTRACT:

Objective

This study aims to develop and validate SwinHS, a deep learning-based automatic segmentation model designed for precise hippocampus delineation in patients receiving hippocampus-protected whole-brain radiotherapy. By streamlining this process, we seek to significantly improve workflow efficiency for clinicians.

Methods

A total of 100 three-dimensional T1-weighted MR images were collected, with 70 patients allocated for training and 30 for testing. Manual delineation of the hippocampus was performed according to RTOG0933 guidelines. The SwinHS model, which incorporates a 3D ELSA Transformer module and an sSE CNN decoder, was trained and tested on these datasets. To prove the effectiveness of SwinHS, this study compared the segmentation performance of SwinHS with tha

SUBMITTER: Li L 

PROVIDER: S-EPMC11502472 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature