3D PBV-Net: An automated prostate MRI data segmentation method

Yao Jin, Guang Yang, Ying Fang, Ruipeng Li, Xiaomei Xu, Yongkai Liu, Xiaobo Lai

Abstract: Highlights•A novel deep model is proposed for automated prostate MRI data segmentation.•Obtained average accuracy of 97.65% and 98.29% for prostate MRI data segmentation.•Multiple evaluation metrics have been used to validate our proposed method.•The proposed method is robust and can achieve promising results on two datasets.