Brain Metastases Segmentation for BraTS 2026 Task 1: A Multi-Architecture Comparison

arXiv:2609.24769 · cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.CV

Submitted: 2026-09-21

Updated: 2026-09-21

Comments: 10 pages, 1 figure. Accepted as a poster at the BraTS 2026 Challenge, MICCAI 2026. Code: https://github.com/mahdiislam79/BraTS_METS_2026

Code: https://github.com/mahdiislam79/BraTS_METS_2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• nnU-Net — 27× in this paper · explained in 2 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Fingerprinting — 2× in this paper · explained in 2 episodes
• Adaptive Weighting — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Magnetic Resonance Imaging — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation
• Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI
• Primus: Enforcing Attention Usage for 3D Medical Image Segmentation

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