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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