Lili Xu, Gumuyang Zhang, Daming Zhang, Jiahui Zhang, Xiaoxiao Zhang, Xin Bai, Li Chen, Qianyu Peng, Ru Jin, Li Mao, Xiuli Li, Zhengyu Jin, Hao Sun
OBJECTIVES: To automatically segment prostate central gland (CG) and peripheral zone (PZ) on T2-weighted imaging using deep learning and assess the model's clinical utility by comparing it with a radiologist annotation and analyzing relevant influencing factors, especially the prostate zonal volume. METHODS: A 3D U-Net-based model was trained with 223 patients from one institution and tested using one internal testing group (n = 93) and two external testing datasets, including one public dataset (ETDpub , n = 141) and one private dataset from two centers (ETDpri , n = 59)...
March 16, 2023: Insights Into Imaging