轻量级脑肿瘤分割模型，兼顾高精度与低计算开销。
MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation Network
- 多分支残差结构融合注意力机制，增强局部特征提取。
- 在BraTS 2018/2019数据集上达高精度，计算量显著降低。
- 适合临床部署，尤其对算力有限的场景友好。
准确分割脑肿瘤在疾病诊断与治疗中至关重要，是量化肿瘤和提取特征的关键技术。随着深度学习应用增多，模型计算负担日益加重。为实现高性能且轻量的分割模型，本文提出MBDRes-U-Net，基于3D U-Net编码器解码器框架，引入多分支残差模块与融合注意力机制。通过分支策略有效利用多模态图像中的丰富局部特征，提升亚肿瘤区域分割性能。编码阶段在多分支残差块中加入自适应加权扩展卷积层，增强特征表达能力，提高分割精度。在BraTS Challenge 2018和2019数据集上的实验表明，该模型在保持高精度的同时显著降低计算开销。代码已开源。
原文摘要 · Abstract (English)
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
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