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Abstract #4077

Deep-learning based rectal tumor localization and segmentation on multi-parametric MRI

Yang Zhang1,2, Liming Shi3, Weiwen Zhou3, Xiaonan Sun3, Ning Yue1, Min-Ying Su2, and Ke Nie1
1Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States, 2Department of Radiological Sciences, University of California, Irvine, CA, United States, 3Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China


Two deep learning methods using the convolutional neural network (CNN) was implemented to segment the rectal cancer in 197 LARC patients, with tumor ROI outlined by a radiologist. For each patient, six frames, including T2, 2 DWI sequences and 3 LAVA sequences, were used for training and validation. The Dice similarity coefficient (DSC) value were used to compare the results of the proposed algorithm and the ROI outlined by reader. The mean DSC was 0.67 and 0.78 from each these 2 method respectively. The proposed algorithms especially the combined serials of U-Net showed improved performance compared to prior published work with individual sequence only. Our work showed the deep-learning with combined image sequence can provide as a promising tool for fully automatic tumor localization and segmentation for rectal cancer.

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