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Abstract #4108

A mutual communicated model based on multi-parametric MRI for automated prostate cancer segmentation and classification

Piqiang Li1, Zhao Li2, Qinjia Bao2, Kewen Liu1, Xiangyu Wang3, Guangyao Wu4, and Chaoyang Liu2
1School of Information Engineering, Wuhan University of Technology, Wuhan, China, 2State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathmatics, Innovation Academy for Precision Measurement Science and Technology, Wuhan, China, 3Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen, China, 4Department of Radiology, Shenzhen University General Hospital, Shenzhen, China

We proposed a Mutual Communicated Deep learning Segmentation and Classification Network (MC-DSCN) for prostate cancer based on multi-parametric MRI. The network consists of three mutual bootstrapping components: the coarse segmentation component provides coarse-mask information for the classification component, the mask-guided classification component based on multi-parametric MRI generates the location maps, and the fine segmentation component guided by the located maps. By jointly performing segmentation based on pixel-level information and classification based on image-level information, both segmentation and classification accuracy are improved simultaneously.

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