| title | Assessing nnU-Net Generalization across Brain Tumor Populations in BraTS-GoAT 2026 |
|---|---|
| authors | Tristan Kirscher, Vivian Metzger, Philippe Meyer, Xavier Coubez |
| date | 2026-09-14 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.15524 |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | BraTS-GoAT evaluates tumor segmentation across heterogeneous populations. We trained a conventional 3D nnU-Net on 1,351 labeled cases using five-fold cross-validation and 1,000 epochs per fold. The final predictor averaged all folds and applied test-time mirroring. On pooled official validation, global DSC values were 0.7805, 0.8288, and 0.8854 for enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Under matched fold-0 inference, mean regional Dice decreased from 0.9058 on source out-of-fold (OOF) cases to 0.8310 on pooled validation (difference--0.0747). Mirroring gave small single-fold gains but no clear ensemble benefit; a residual-encoder alternative reached 0.8282 mean Dice. In labeled OOF predictions, failure cases had substantially smaller reference ET volumes; after adjustment for ET and WT volume, lower Dice remained associated with more disconnected ET components and a smaller fraction of ET contained in the largest component. |
cite
@misc{kirscher2026assessing,
title={Assessing nnU-Net Generalization across Brain Tumor Populations in BraTS-GoAT 2026},
author={Tristan Kirscher and Vivian Metzger and Philippe Meyer and Xavier Coubez},
year={2026},
eprint={2609.15524},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.15524}
}