A Bayesian deep segmentation framework for glioblastoma tumor segmentation using follow-up MRIs.

ABSTRACT:

Background

Glioblastoma (GBM) is the most common malignant brain tumor with an abysmal prognosis. Since complete tumor cell removal is impossible due to the infiltrative nature of GBM, accurate measurement is paramount for GBM assessment. Preoperative magnetic resonance images (MRIs) are crucial for initial diagnosis and surgical planning, while follow-up MRIs are vital for evaluating treatment response. The structural changes in the brain caused by surgical and therapeutic measures create significant differences between preoperative and follow-up MRIs. In clinical research, advanced deep learning models trained on preoperative MRIs are often applied to assess follow-up scans, but their effectiveness in this context remains underexplored. Our study evaluates the performance of thes

SUBMITTER: Kabir T 

PROVIDER: S-EPMC12588840 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature