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Abstract #3184

3D Automatic segmentation of breast lesion in dynamic contrast enhanced MRI using deep convolutional neural network

Fuliang Lin1,2, Zhou Liu3, Qinglei Zhou2, Pengyu Gao4, Jie Wen3, Meng Wang3, Ya Ren3, Dehong Luo3, Ye Li1, Dong Liang1, Xin Liu1, Hairong Zheng1, and Na Zhang1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Zhengzhou University, Zhengzhou, China, 3Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China, 4Henan University, Kaifeng, China


Breast cancer is the most common cancer in women with the highest incidence. Dynamic contrast enhanced MRI is one of the backbone sequences for breast cancer diagnosis. Accurate segmentation of breast lesions based on DCE-MRI images is helpful for clinically objective and quantitative evaluation of breast lesions. However, the commonly used manual segmentation method is subject to high inter-observer variability. In this study, a 3D automatic algorithm is proposed for segmentation of breast lesions in DCE-MRI. The results show that the proposed network can obtain accurate and automatic 3D segmentation of breast lesions and achieves better segmentation results than VNet.

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