Abstract

Info

Title:: FC_Densenet-based autosegmentation of organs-at-risk in lung cancer radiotherapy

Author(s):: ZHANG Fuli1; YANG Anning2; LU Na1; JIANG Huayong1; CHEN Diandian1; YU Yanjun1; WANG Yadi1; 1. Department of Radiation Oncology, the Seventh Medical Center of Chinese PLA General Hospital, Beijing 100700, China 2. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China

Keywords:: Keywords: lung cancer organs-at-risk medical image segmentation DenseNet deep learning

Abstract:: Abstract: Objective To introduce a method for the end-to-end autosegmentation of organs-at-risk in chest CT images based on dense connection deep learning, and to provide a high-precision autosegmentation model for reducing doctors’ workload. Methods The CT images of 36 lung cancer patients were analyzed in this study. Twenty-seven cases out of 36 cases were randomly selected as training set, 6 cases as validation set for cross validation, and 9 cases as testing set. The training time was about 5 hours, and the segmentations of 4 organs-at-risk including the left and right lungs, spinal cord and heart were completed. The testing set was evaluated by Dice coefficient, HD95 distance and average surface distance. Results Compared with U-Net ResNet50, DenseNet was better in Dice coefficient, HD95 and average surface distance. However, there was no significant difference in segmentation results among 3 networks. The highest Dice coefficient of DenseNet was 0.98 for the left lung, while the lowest was 0.84 for the heart. Conclusion The left and right lungs, spinal cord and heart can be accurately segmented by dense connection deep learning model. The idea of feature map reuse provides a new idea for medical image segmentation based on deep learning.