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

SCPMan: Shape Context and Prior Constrained Multi-scale Attention Network for Pancreatic Segmentation