SegGuidedNet: Sub-Region-Aware Attention Supervision for Interpretable Brain Tumor Segmentation

TL;DR

SegGuidedNet is a lightweight 3D neural network that improves brain tumor sub-region segmentation accuracy and interpretability by explicitly supervising spatial attention maps, outperforming existing models on BraTS benchmarks.

Contribution

The paper introduces SegGuidedNet with a novel SegAttentionGate module that provides spatial supervision and interpretability without significant parameter overhead.

Findings

Achieves high Dice scores on BraTS2021 and BraTS2023 datasets.

Surpasses ensemble-based models like nnU-Net and HNF-Netv2 as a single model.

Approaches the performance of a 10-model ensemble with lower inference cost.

Abstract

Accurate segmentation of brain tumour sub-regions from multi-parametric MRI is critical for treatment planning yet remains challenging due to morphological variability, class imbalance, and overlapping appearances of tumour regions across imaging sequences. We propose SegGuidedNet, a three-dimensional residual encoder--decoder network introducing a novel SegAttentionGate module that explicitly supervises the decoder to produce spatially discriminative attention maps for each tumour sub-region necrotic core, peritumoral oedema, and enhancing tumour via a lightweight auxiliary loss, adding less than 0.2% parameter overhead. This sub-region supervision maintains decoder discriminability between visually ambiguous classes while providing free-of-cost spatial interpretability at inference without any post-hoc explanation method. Evaluated independently on BraTS2021 and BraTS2023 GLI across 251 held-out subjects each, SegGuidedNet achieves mean Dice of 0.905 (ET= 0.873, TC=0.906, WT=0.935) and 0.897 (ET=0.859, TC=0.902, WT=0.931) respectively, surpassing ensemble-based nnU-Net and HNF-Netv2 as a single model and approaching Swin UNETR a 10-model ensemble within 2--4 Dice points at a fraction of the inference cost. The consistency of results across two benchmark editions further confirms the generalisability of the proposed approach, offering competitive accuracy with built-in interpretability in a lightweight, clinically practical framework.