Due to the poor prognosis of Pancreatic cancer, accurate early detection and segmentation are critical for improving treatment outcomes. However, pancreatic segmentation is challenged by blurred boundaries, high shape variability, and class imbalance. To tackle these problems, we propose a multiscale attention network with shape context and prior constraint for robust pancreas segmentation. Specifically, we proposed a Multi-scale Feature Extraction Module (MFE) and a Mixed-scale Attention Integration Module (MAI) to address unclear pancreas boundaries. Furthermore, a Shape Context Memory (SCM) module is introduced to jointly model semantics across scales and pancreatic shape. Active Shape Model (ASM) is further used to model the shape priors. Experiments on NIH and MSD datasets demonstrate the efficacy of our model, which improves the state-of-the-art Dice Score for 1.01% and 1.03% respectively. Our architecture provides robust segmentation performance, against the blurry boundaries, and variations in scale and shape of pancreas.

翻译：胰腺癌预后不良，因此准确的早期检测与分割对改善治疗效果至关重要。然而，胰腺分割面临边界模糊、形状变异大及类别不平衡等挑战。为解决这些问题，我们提出了一种融合形状上下文与先验约束的多尺度注意力网络，用于鲁棒的胰腺分割。具体而言，我们设计了多尺度特征提取模块（MFE）和混合尺度注意力整合模块（MAI）以处理模糊的胰腺边界；引入形状上下文记忆（SCM）模块协同建模跨尺度语义与胰腺形状，并进一步采用主动形状模型（ASM）建模形状先验。在NIH与MSD数据集上的实验表明，该模型分别将当前最优Dice得分提升1.01%和1.03%。我们的架构能够有效应对胰腺的边界模糊、尺度与形状变化，实现稳健的分割性能。