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

翻译：脑肿瘤的精确分割在脑肿瘤疾病的诊断与治疗中起着关键作用，是量化肿瘤并提取其特征的关键技术。随着深度学习方法应用的日益增多，计算负担也逐渐加重。为实现兼具良好分割性能的轻量化模型，本研究提出MBDRes-U-Net模型，该模型采用三维（3D）U-Net编解码器框架，将多分支残差块与融合注意力机制集成于模型中。分支策略有效利用了多模态图像中丰富的局部特征，减轻了模型的计算负担，并增强了子肿瘤区域的分割性能。此外，在编码阶段，多分支残差块中引入了自适应加权膨胀卷积层，丰富了特征表达，提升了模型的分割精度。在脑肿瘤分割挑战赛（BraTS）2018与2019数据集上的实验表明，该架构能在显著降低计算开销的同时，保持较高的脑肿瘤分割精度。我们的代码发布于 https://github.com/Huaibei-normal-university-cv-laboratory/mbdresunet