Abstract

Info

Title:: Feature area refocusing for improving the accuracy of small target area segmentations by fully convolutional networks

Author(s):: JIANG Jialiang; LUO Yong; HE Yisong; YU Hang; FU Yuchuan; Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu 610041, China

Keywords:: Keywords: nasopharyngeal carcinoma; tumor volume; segmentation of target area; convolutional neural network; attention mechanism

Abstract:: Abstract: Objective To propose a feature area refocusing method based on convolutional neural networks (CNN) for improving the accuracy of small target area segmentations. Methods End-to-end fully convolutional networks (FCN) was used to automatically segment nasopharynx gross tumor volume (GTVnx) by conventional segmentation method and feature area refocusing method. Sixty cases of nasopharyngeal carcinoma were analyzed in the study, with 40 cases for training and 20 cases for testing. Dice similarity coefficient (DSC) was used to evaluate the accuracy of automatic segmentation. Results The DSC obtained by conventional segmentation method was 0.352±0.084, lower than 0.524±0.079 which was obtained by feature area refocusing method. Statistical analysis of 20 test cases showed that the P value was less than 0.01. Conclusion Compared with conventional segmentation method, the feature area refocusing method for segmentation can achieve a better GTVnx segmentation result and improve the accuracy of small target area segmentation.