SCPMan: Shape context and prior constrained multi-scale attention network for pancreatic segmentation

Leilei Zeng, Xuechen Li, Xinquan Yang, Wenting Chen, Jingxin Liu, Linlin Shen*, Song Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 efficiency 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.

Original languageEnglish
Article number124070
JournalExpert Systems with Applications
Publication statusPublished - 15 Oct 2024


  • Activate shape model
  • Deep learning
  • Medical image segmentation
  • Multi-scale


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