Assessing nnU-Net Generalization across Brain Tumor Populations in BraTS-GoAT 2026

2026-09-14

AI Summary

研究使用3D nnU-Net在1,351个标记案例上训练，通过五折交叉验证评估脑肿瘤分割性能，分析了不同区域Dice分数的变化及其影响因素。

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.

Problem

• nnU-Net
• Brain Tumor Segmentation
• Generalization
• BraTS-GoAT
• Dice Score

Innovation

• nnU-Net
• cross-validation
• test-time mirroring
• tumor segmentation
• generalization