论文摘要：胰腺癌的特点是起病隐匿且预后极差。胰腺及其肿瘤的精确分割对于实现精准医疗至关重要。胰腺表现为一个小且对比度低的目标，而胰腺肿瘤则更小且形状极不规则。现有的方法，如卷积神经网络，受限于其小的感受野，难以建模长程依赖关系。同时，Transformer固定的计算路径给其适应3D胰腺和肿瘤分割带来了挑战。本研究提出了全局感知与局部细化（GALR）模型，该模型解决了长程依赖关系和多尺度建模的难题，实现了胰腺及其肿瘤边界的精确3D分割。该模型包含两个核心创新模块：多尺度双注意力Mamba（MDAM）模块通过选择性状态空间模型动态捕获重塑序列的关键信息，并结合膨胀卷积多尺度融合来增强多尺度对象识别能力。该模块整合了双重注意力机制以捕捉跨区域的解剖关联，增强了低对比度区域的分割敏感度，并实现了精准医学图像分割的全局感知。轻量级分组卷积增强（LGCE）模块采用参数高效的链接结构，在解码阶段构建了针对小目标边界的特征细化流程，有效抑制了诸如伪影等噪声对目标轮廓的干扰。该模块通过局部细化机制显著提高了定位精度，并实现了精确的边界轮廓分割。在CT和MRI两个数据集上的实验结果表明，GALR在胰腺和肿瘤分割任务中表现最佳。在数据集1上，我们的方法在胰腺和肿瘤分割任务中分别实现了80.27%和51.72%的Dice系数，分别比之前的最佳模型高出0.46%和1.18%。其他指标的表现也很出色。在数据集2上的实验也得出了相同的结论。实验结果表明，GALR为胰腺疾病的计算机辅助诊断提供了一种高效的临床解决方案。

Abstract:Pancreatic cancer is characterized by insidious onset and extremely poor prognosis. Accurate segmentation of the pancreas and its tumors is crucial for achieving precision medicine. The pancreas presents as a small, low-contrast target, while pancreatic tumors appear even smaller with highly irregular shapes. Existing methods, such as Convolutional Neural Networks, are limited by their small receptive fields and thus struggle to model long-range dependencies. Meanwhile, the fixed computational path of Transformers presents challenges for their adaptation to 3D pancreas and tumor segmentation. This study proposes the Global Awareness and Local Refinement (GALR) model, which addresses the challenges of long-range dependencies and multi-scale modeling, achieving precise 3D segmentation of the pancreas and its tumor boundaries. The model consists of two core innovative modules: The Multi-scale Dual Attention Mamba (MDAM) module dynamically captures the key information of the remodeling sequence through the Selective State Space Model, and combines the dilated convolution multi-scale fusion to enhance the multi-scale object recognition ability. The module integrates the dual attention mechanism to capture the cross-regional anatomical correlation, strengthens the segmentation sensitivity of low contrast regions, and realizes global perception of precision medical image segmentation. The Lightweight Grouped Convolutional Enhancement (LGCE) module uses a parameter-efficient linkage structure to construct a feature refinement flow for small target boundaries in the decoding stage, which effectively suppresses the interference of noise such as artifacts on the target contour. This module significantly improves the localization accuracy through the Local Refinement mechanism, and achieves accurate boundary contour segmentation. Experimental results on two datasets, CT and MRI, show that GALR has the best performance in pancreas and tumor segmentation tasks. On dataset 1, our method achieves Dice coefficients of 80.27% and 51.72% in pancreas and tumor segmentation tasks, respectively, which are 0.46% and 1.18% higher than the previous best model. The performance of other indicators is also excellent. The experiment on dataset 2 also leads to the same conclusion. The experimental results indicate that GALR provides an efficient clinical solution for computeraided diagnosis of pancreatic diseases.