Abstract #4492

Deep Learning-enabled Prostate Segmentation: Large Cohort Evaluation with Inter-Reader Variability Analysis

Yongkai Liu¹, Miao Qi^1,2, Chuthaporn Surawech^1,3, 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

Synopsis

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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