Abstract #3184

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

Fuliang Lin^1,2, 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

Synopsis

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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