Hyper-Connections for Adaptive Multi-Modal MRI Brain Tumor Segmentation

TL;DR

This paper introduces Hyper-Connections, a dynamic feature fusion method for multi-modal MRI brain tumor segmentation, improving model accuracy and sensitivity across various architectures with minimal added complexity.

Contribution

It presents Hyper-Connections as a novel, adaptable replacement for fixed residual connections, enhancing multi-modal feature integration in 3D medical image segmentation models.

Findings

Consistent +1.03% Dice improvement on BraTS 2021 dataset

Enhanced boundary delineation in tumor sub-regions

Sharper modality sensitivity in HC-equipped models

Abstract

We present the first study of Hyper-Connections (HC) for volumetric multi-modal brain tumor segmentation, integrating them as a drop-in replacement for fixed residual connections across five architectures: nnU-Net, SwinUNETR, VT-UNet, U-Net, and U-Netpp. Dynamic HC consistently improves all 3D models on the BraTS 2021 dataset, yielding up to +1.03 percent mean Dice gain with negligible parameter overhead. Gains are most pronounced in the Enhancing Tumor sub-region, reflecting improved fine-grained boundary delineation. Modality ablation further reveals that HC-equipped models develop sharper sensitivity toward clinically dominant sequences, specifically T1ce for Tumor Core and Enhancing Tumor, and FLAIR for Whole Tumor, a behavior absent in fixed-connection baselines and consistent across all architectures. In 2D settings, improvements are smaller and configuration-sensitive, suggesting that volumetric spatial context amplifies the benefit of adaptive aggregation. These results establish HC as a simple, efficient, and broadly applicable mechanism for multi-modal feature fusion in medical image segmentation.