An improvement method for pancreas CT segmentation using superpixel-based active contour

Phys Med Biol. 2024 Apr 12. doi: 10.1088/1361-6560/ad3e5c. Online ahead of print.ABSTRACTPancreas is one of the most challenging organs for CT image automatic segmentation due to its complex shapes and fuzzy edges. It is simple and universal to use the traditional segmentation method as a post-processor of deep learning method for segmentation accuracy improvement. As the most suitable traditional segmentation method for pancreatic segmentation, the active contour model(ACM), still suffers from the problems of weak boundary leakage and slow contour evolution speed. Therefore, a convenient post-processor for any deep learning methods using superpixel-based active contour model(SbACM) is proposed to improve the segmentation accuracy. Firstly, the superpixels with strong adhesion to edges are used to guide the design of narrowband and energy function. A multi-scale evolution strategy is also proposed to reduce the weak boundary leakage and comprehensively improve the evolution speed. Secondly, using the original image and the coarse segmentation results obtained from deep learning methods as inputs, the proposed SbACM method is used as a post-processor for fine segmentation. Finally, the pancreatic segmentation public dataset TCIA from the National Institutes of Health(NIH, USA) is used for evaluation, and the Wilcoxon Test confirmed that the improvement of proposed method is statistically significant. The results show that: (1) The superpixel-based narrowband shape and dynamic ...
Source: Physics in Medicine and Biology - Category: Physics Authors: Source Type: research