Deep Learning-enabled Prostate Segmentation: Large Cohort Evaluation with Inter-Reader Variability Analysis
Yongkai Liu1, Miao Qi1,2, Chuthaporn Surawech1,3, Haoxin Zheng1, Dan Nguyen4, Guang Yang5, Steven Raman1, and Kyunghyun Sung1
1Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, 2Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China, 3Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand, 4Department of Radiation Oncology, UT Southwestern Medical Center, Los Angeles, CA, United States, 5National Heart and Lung Institute, Imperial College London, London, United Kingdom
Whole-prostate gland (WPG) segmentation plays a significant role in prostate volume measurement, treatment, and biopsy planning. This study evaluated a previously developed automatic WPG segmentation, deep attentive neural network (DANN), on a large, continuous patient cohort to test its feasibility in a clinical setting.
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