MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation Network

TL;DR

This paper introduces MBDRes-U-Net, a lightweight 3D U-Net based model with multibranch residual blocks and fused attention, achieving high-precision brain tumor segmentation with reduced computational load.

Contribution

The study presents a novel lightweight 3D U-Net architecture with multibranch residual blocks and adaptive weighted expansion convolution for improved segmentation accuracy.

Findings

Maintains high segmentation precision on BraTS datasets.

Reduces computational overhead compared to existing models.

Enhances segmentation of subtumor regions.

Abstract

Accurate segmentation of brain tumors plays a key role in the diagnosis and treatment of brain tumor diseases. It serves as a critical technology for quantifying tumors and extracting their features. With the increasing application of deep learning methods, the computational burden has become progressively heavier. To achieve a lightweight model with good segmentation performance, this study proposes the MBDRes-U-Net model using the three-dimensional (3D) U-Net codec framework, which integrates multibranch residual blocks and fused attention into the model. The computational burden of the model is reduced by the branch strategy, which effectively uses the rich local features in multimodal images and enhances the segmentation performance of subtumor regions. Additionally, during encoding, an adaptive weighted expansion convolution layer is introduced into the multi-branch residual block, which enriches the feature expression and improves the segmentation accuracy of the model. Experiments on the Brain Tumor Segmentation (BraTS) Challenge 2018 and 2019 datasets show that the architecture could maintain a high precision of brain tumor segmentation while considerably reducing the calculation overhead.Our code is released at https://github.com/Huaibei-normal-university-cv-laboratory/mbdresunet