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.

胰腺癌的早期准确检测和分割对于改善治疗结果至关重要。本研究提出了一种多尺度注意力网络，结合形状上下文和先验约束来实现强大的胰腺分割，通过多尺度特征提取模块和混合尺度注意力融合模块解决了模糊的胰腺边界问题，并引入形状上下文模块和主动形状模型来建模语义和形状先验。在 NIH 和 MSD 数据集上的实验证明了我们模型的有效性，其 Dice Score 分别提高了 1.01% 和 1.03%。我们的架构在处理胰腺模糊边界、尺度和形状变化方面具有强大的分割性能。