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Abstract #3527

A two-stage deep learning method for the identification of rectal cancer lesions in MR images

Jiaxin Li1, Cheng Li2, Xiran Jiang1, Zhenkun Peng2, Chaohe Zhang3, Qiegen Liu4, and Shanshan Wang2
1China Medical University, Shenyang, China, 2Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 3Cancer Hospital of China Medical University, Shenyang, China, 4Department of Electronic Information Engineering, Nanchang University, Nanchang, China

End-to-end deep learning methods, such as the well-known U-Net, have achieved great successes in biomedical image segmentation tasks. These models are often fed with the full field of view images which may contain irrelevant organs or tissues influencing the segmentation performance. In this study, targeting at the accurate segmentation of rectal cancer lesions in T1-weighted MR images, we propose a two-stage deep learning method that is composed of a detection stage and a segmentation stage. Experimental results show that under the guidance of the detected bounding boxes, better segmentation performance is achieved.

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