SCPMan: Shape Context and Prior Constrained Multi-scale Attention
  Network for Pancreatic Segmentation
Leilei Zeng Xuechen Li Xinquan Yang Linlin Shen Song Wu
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.