Abstract

Info

Title:: Improving auto-segmentation accuracy for online magnetic resonance imaging-guided prostate radiotherapy by registration-based deep learning method

Author(s):: WANG Yunxiang; YANG Bining; LIU Yuxiang; ZHU Ji; LU Ning-Ning; DAI Jianrong; MEN Kuo; National Cancer Center/National Clinical Research Center for Cancer/Department of Radiation Oncology, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

Keywords:: Keywords: prostate cancer online magnetic resonance imaging-guided adaptive radiotherapy image registration deep learning auto-segmentation

Abstract:: Abstract: Objective To improve the performance of auto-segmentation of prostate target area and organs-at-risk in online magnetic resonance image and enhance the efficiency of magnetic resonance imaging-guided adaptive radiotherapy (MRIgART) for prostate cancer. Methods A retrospective study was conducted on 40 patients who underwent MRIgART for prostate cancer, including 25 in the training set, 5 in the validation set, and 10 in the test set. The planning CT images and corresponding contours, along with online MR images, were registered and input into a deep learning network for online MR image auto-segmentation. The proposed method was compared with deformable image registration (DIR) method and single-MR-input deep learning (SIDL) method. Results The overall accuracy of the proposed method for auto-segmentation was superior to those of DIR and SIDL methods, with average Dice similarity coefficients of 0.896 for clinical target volume, 0.941 for bladder, 0.840 for rectum, 0.943 for left femoral head and 0.940 for right femoral head, respectively. Conclusion The proposed method can effectively improve the accuracy and efficiency of auto-segmentation in MRIgART for prostate cancer.