A deep learning framework for comprehensive segmentation of deep grey nuclei.

ABSTRACT:

Background

Deep grey matter structures such as the thalamus and basal nuclei are implicated in numerous neurological disorders, yet accurate segmentation of these structures from standard T1-weighted MRI remains challenging due to poor intra-subcortical contrast, long preprocessing pipelines, and fragmented toolsets.

Methods

We introduce THOMASINA a deep learning pipeline for comprehensive subcortical segmentation from standard T1-weighted (T1w) as well as white-matter-nulled (WMn) MRI. The method leverages labels derived from a recently published state-of-the-art multi-atlas segmentation method to train multiple 3D deep learning-based segmentation models including SwinUNETR, DiNTS, and SegResNet. All networks were trained on cropped volumes and tested on held-out and out-of

SUBMITTER: Barat A 

PROVIDER: S-EPMC12723987 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature