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Abstract #1468

Deep-learning based rectal tumor segmentation on multi-parametric MRI using pre-treatment and near-end-treatment data

Yang Zhang1,2, Liming Shi3, Xiaonan Sun3, Min-Ying Su2, Taoran Cui1, Ning J. Yue1, and Ke Nie1
1Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, United States, 2Department of Radiological Sciences, University of California, Irvine, CA, United States, 3Department of Radiation Oncology, Department of Radiation Oncology, Sir Run-run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China

Synopsis

Keywords: Cancer, Machine Learning/Artificial IntelligenceIn this study, the convolutional neural network (CNN) was implemented to segment the rectal cancer in 185 patients, with tumor ROI outlined by a radiologist on multiparametric magnetic resonance images (mp-MRI). The Dice similarity coefficient (DSC) and mean symmetrical surface distance (MSD) values were used to evaluate the results of the proposed algorithm. The mean DSC and MSD were 0.88 and 2.2 cm respectively for pre-treatment MR images. The transfer-learning model using paired pre-treatment and near-end-treatment images could also lead to acceptable result for boost volume segmentation with the mean DSC of 0.83 and MSD of 2.7 cm. Our work showed the deep-learning with combined image sequences was promising for automatic tumor localization and segmentation of locally advanced rectal cancer. After applying transfer learning with the prior information obtained from the pre-treatment images, the proposed method can be further used to identify boost volume for radiation dose escalation which has been demonstrated beneficial in improving pathological complete response rate.

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