Auto-segmentation of pancreatic tumor in multi-modal image using transferred DSMask R-CNN network

Pancreatic tumor segmentation is a difficult task due to the high variable shape, small size and hidden position of organs in patients for adaptive radiation therapy plan. To address the problems of limited labeled data, intra-class inconsistency and inter-class indistinction in pancreas tumor segmentation, a transferred DenseSE-Mask R-CNN (TDSMask R-CNN) Network segmentation model using Dense and SE block embedded is proposed in this paper. The multi-scale features strategy is selected to deal with high variability of pancreas and their tumor. The proposed network can learn complementary information from different modes (PET/MR) images respec-tively by the attention mechanism to get pancreatic tumor regions in different domain. As a result, the irrelevant information for segmenting the tumor area can be suppressed and get low false positives. Furthermore, accurate tumor location from PET image is transferred MRI training model for guide Dense-SE network learning to alleviate the small label samples and reduce network overfitting. Experimental results show that the proposed method achieves average Dice Similarity Coefficient (DSC) of 78.33%, sensitivity (SEN) of 78.56%, and speci-ficity (SPE) of 99.72% on the collected PET/MR data set, which is superior to the existing method of some lit-eratures. This algorithm can improve the accuracy of pancreatic tumor segmentation.

Automated summary

In this paper, a transferred DenseSE-Mask R-CNN (TDSMask R-CNN) Network segmentation model is proposed for pancreatic tumor segmentation. The model utilizes multi-scale features and attention mechanism to accurately obtain tumor regions in PET and MRI images, and alleviates network overfitting. Experimental results show that the proposed method achieves superior segmentation accuracy compared to existing methods.