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.

翻译：在CT扫描中精确分割胰腺及其病变对于胰腺癌的精准诊断与治疗至关重要。然而，由于胰腺与周围器官组织对比度低、解剖边界模糊、器官形状不规则以及病变尺寸小等多种因素，该任务仍极具挑战性。为解决这些问题，我们提出了DB-MSMUNet（双分支多尺度Mamba UNet），这是一种专为鲁棒性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）构建，该模块结合了可变形卷积与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：边缘解码器引入了边缘增强路径（EEP），以显式捕获边界线索并细化模糊轮廓；而区域解码器则集成了多层解码器（MLD），通过利用多尺度深层语义特征来保留细粒度细节并精确重建小尺寸病变。此外，我们在两个解码器的多个尺度上添加了辅助深度监督（ADS）头，以提供更精确的梯度反馈，并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实验：NIH胰腺数据集、MSD数据集以及合作医院提供的临床胰腺肿瘤数据集。DB-MSMUNet分别取得了89.47%、87.59%和89.02%的Dice相似系数，在分割精度、边缘保持能力以及跨数据集的鲁棒性方面均优于现有大多数先进方法。这些结果证明了所提方法在实际胰腺CT分割任务中的有效性和泛化能力。