GBA-Net: A Method for 3D Brain Tumor Segmentation Based on Multi-scale Gaussian Boundary Attention

Abstract

The complex nature of brain tumors, characterized by their individual shapes, sizes, and locations, as well as the presence of indistinct boundaries, presents a challenging task for precise automatic segmentation. While U-Net has been a top performer in medical image segmentation, it struggles with capturing multi-scale details, preserving information across layers, and focusing on critical features. To address these issues, a new 3D brain tumor segmentation network called GBA-Net is proposed, which introduces - 1) A multi-scale Gaussian boundary attention (GBA) module with the ability to automatically focus on boundary features, 2) Efficient inverted bottleneck convolution upsample (Up-IBC) and inverted bottleneck convolution downsample (Down-IBC) modules that enhance the richness of cross-scale information, 3) The high-low feature fusion (HLFF) module that mitigates information loss during the decoder’s restoration of full spatial resolution. The proposed GBA-Net achieves state-of-the-art performance on the 3D brain tumor dataset BraTS 2021. Cross-validation on the BraTS 2018 and BraTS 2019 datasets indicates that GBA-Net generalizes well on the external datasets.