Abstract

Info

Title:: Automatic segmentation of the stomach and pancreas using cascaded deep convolutional neural network

Author(s):: CAO Yangsen1; ZHU Xiaofei1; HAN Miaofei2; LU Mingzhi1; GAO Yaozong2; GU Lei1; YU Chunshan1; SUN Yongjian1; ZHANG Huojun1; 1. Department of Radiation Oncology, Changhai Hospital of Naval Medical University, Shanghai 200433, China 2. Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China

Keywords:: Keywords: deep learning convolutional neural network automatic segmentation artificial intelligence stomach pancreas

Abstract:: Abstract: Objective To analyze the accuracy and efficiency of self-developed cascaded deep convolutional neural network (VB-Net) in the automatic segmentation of the stomach and pancreas. Methods The clinical data of 150 patients with pancreatic cancer were analyzed retrospectively. The non-enhanced CT data of 132 cases and the pancreas contrast-enhanced CT and structure data of 116 cases among the 132 cases were randomly chosen as training set for training the model for segmentation of the stomach and pancreas. The non-enhanced CT data and pancreas contrast-enhanced CT data of the other 18 cases were regarded as test set for evaluating the performance of the proposed method. Finally, Dice similarity coefficient was used to quantitatively analyze segmentation accuracy and segmentation efficiency was also evaluated. Results The average Dice similarity coefficients of the automatic segmentation of the stomach and pancreas were 87.93% and 80.05% on non-enhanced CT, 89.71% and 84.79% on pancreas contrast-enhanced CT, respectively. The average automatic segmentation time of the stomach and pancreas was 1.22 s and 0.84 s, while the average manual segmentation time was 158.7 s and 115.52 s. Conclusion The VB-Net based method for the automatic segmentation of the stomach and pancreas can achieve an accurate segmentation results, and significantly improve organs segmentation efficiency.