SCPMan: Shape Context and Prior Constrained Multi-scale Attention Network for Pancreatic Segmentation
This work addresses the critical need for accurate pancreatic segmentation to improve early cancer detection and treatment outcomes, representing an incremental advance over existing methods.
The paper tackles pancreatic segmentation challenges including blurred boundaries, high shape variability, and class imbalance by proposing a multiscale attention network with shape context and prior constraints, achieving state-of-the-art Dice Score improvements of 1.01% and 1.03% on NIH and MSD datasets.
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.