DB-MSMUNet：双分支多尺度Mamba UNet用于胰腺CT扫描分割
DB-MSMUNet:Dual Branch Multi-scale Mamba UNet for Pancreatic CT Scans Segmentation
AI总结：
DB-MSMUNet通过双分支多尺度Mamba架构提升胰腺CT分割精度，采用边缘增强和多尺度语义特征提升边缘保留与小病变重建能力。
摘要：
胰腺及其病变在CT扫描中的准确分割对于胰腺癌的精确诊断和治疗至关重要。然而，由于组织对比度低、周围器官模糊、器官形状不规则以及病变大小小等因素，这一任务仍然极具挑战性。为解决这些问题，我们提出DB-MSMUNet（双分支多尺度Mamba UNet），一种专门针对鲁棒胰腺分割设计的新型编码器-解码器架构。编码器采用多尺度Mamba模块（MSMM），结合可变形卷积和多尺度状态空间建模，以增强全局上下文建模和局部形变适应能力。网络采用双解码器设计：边缘解码器引入边缘增强路径（EEP）以显式捕捉边界线索并细化模糊轮廓，而区域解码器结合多层解码器（MLD）以保留细粒度细节并利用多尺度深层语义特征准确重建小病变。此外，在多个尺度上添加辅助深度监督（ADS）头，为两个解码器提供更准确的梯度反馈，进一步增强多尺度特征的判别能力。我们在三个数据集上进行了广泛的实验：NIH胰腺数据集、MSD数据集以及由合作医院提供的临床胰腺肿瘤数据集。DB-MSMUNet在三个数据集上分别实现了Dice相似系数为89.47%、87.59%和89.02%，在分割精度、边缘保留和不同数据集的鲁棒性方面均优于大多数现有最先进的方法。这些结果证明了所提出方法在实际胰腺CT分割任务中的有效性和通用性。
英文摘要：
Accurate segmentation of the pancreas and its lesions in CT scans is crucial for the precise diagnosis and treatment of pancreatic cancer. However, it remains a highly challenging task due to several factors such as low tissue contrast with surrounding organs, blurry anatomical boundaries, irregular organ shapes, and the small size of lesions. To tackle these issues, we propose DB-MSMUNet (Dual-Branch Multi-scale Mamba UNet), a novel encoder-decoder architecture designed specifically for robust pancreatic segmentation. The encoder is constructed using a Multi-scale Mamba Module (MSMM), which combines deformable convolutions and multi-scale state space modeling to enhance both global context modeling and local deformation adaptation. The network employs a dual-decoder design: the edge decoder introduces an Edge Enhancement Path (EEP) to explicitly capture boundary cues and refine fuzzy contours, while the area decoder incorporates a Multi-layer Decoder (MLD) to preserve fine-grained details and accurately reconstruct small lesions by leveraging multi-scale deep semantic features. Furthermore, Auxiliary Deep Supervision (ADS) heads are added at multiple scales to both decoders, providing more accurate gradient feedback and further enhancing the discriminative capability of multi-scale features. We conduct extensive experiments on three datasets: the NIH Pancreas dataset, the MSD dataset, and a clinical pancreatic tumor dataset provided by collaborating hospitals. DB-MSMUNet achieves Dice Similarity Coefficients of 89.47%, 87.59%, and 89.02%, respectively, outperforming most existing state-of-the-art methods in terms of segmentation accuracy, edge preservation, and robustness across different datasets. These results demonstrate the effectiveness and generalizability of the proposed method for real-world pancreatic CT segmentation tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
