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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