CoMNet: A MedNeXt-CorrDiff Framework for Multi-Site Brain Tumor Segmentation
Accurate brain tumor segmentation from multiparametric magnetic resonance imaging (MRI) is critical for treatment planning, response assessment, and neuro-oncology research. However, automated segmentation remains a difficult task in computer vision because of variation in tumor appearance and MRI protocols across patient scans. Moreover, clinically important regions such as enhancing tumor and tumor core are often small relative to the full brain volume, further increasing the difficulty of achieving high voxel-level precision. These challenges are amplified in multi-site datasets, where differences in scanner hardware and acquisition parameters can introduce non-biological variation. To...
| Authors | Michael L. Evans, MD Fayaz Bin Hossen, MD Shibly Sadique et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 June 2026 |
| Last updated at source | 30 June 2026 |
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