Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations

2026-09-02

AI Summary

该研究针对脑肿瘤分割的泛化问题，采用nnU-Net框架结合半监督学习和肿瘤感知变形增强方法，提高了不同肿瘤区域的分割精度。

Abstract

This work presents an approach to the Generalizability Across Tumors (BraTS-GoAT) task of the BraTS 2026 Challenge, which focuses on robust segmentation of brain tumor sub-regions across a heterogeneous patient population. The proposed method employs the nnU-Net framework with a large residual encoder architecture, integrating a semi-supervised learning technique with pseudo-labels generated from the unlabeled training data and a tumor-aware deformable augmentation that locally deforms the lesion while preserving the surrounding anatomy. We evaluate the individual contributions of each component, as well as their combination, using varying proportions of the most confident pseudo-labeled cases. The submitted configuration for the generalization task achieves Dice and NSD scores of 0.881 and 0.473 for Whole Tumor, 0.817 and 0.490 for Tumor Core, and 0.775 and 0.533 for Enhancing Tumor on the BraTS-GoAT validation set, improving over the labeled-only baselines across all tumor regions and confirming that self-training and the proposed augmentation are complementary. Our source code is publicly available at https://github.com/Henrique-zan/brats-goat-2026/.

Problem

• brain tumor segmentation
• heterogeneous patient population
• generalizability

Innovation

• self-training
• tumor-aware deformations
• semi-supervised learning
• pseudo-labels