Pancreas Segmentation Optimization Based on Coarse-to-Fine Scheme

As the pancreas only occupies a small region in the whole abdominal computed tomography(CT)scans and has high variability in shape,location and size,deep neural networks in automatic pancreas segmentation task can be easily confused by the complex and variable background.To alleviate these issues,this paper proposes a novel pancreas segmentation optimization based on the coarse-to-fine structure,in which the coarse stage is responsible for increasing the proportion of the target region in the input image through the minimum bounding box,and the fine is for improving the accuracy of pancreas segmentation by enhancing the data diversity and by introducing a new segmentation model,and reducing the running time by adding a total weights constraint.This optimization is evaluated on the public pancreas segmentation dataset and achieves 87.87%average Dice-Sørensen coefficient(DSC)accuracy,which is 0.94%higher than 86.93%,result of the state-of-the-art pancreas segmentation methods.Moreover,this method has strong generalization that it can be easily applied to other coarse-to-fine or one step organ segmentation tasks.