Latrice A Johnson, Stephanie A Harmon, Enis C Yilmaz, Yue Lin, Mason J Belue, Katie M Merriman, Nathan S Lay, Thomas H Sanford, Karthik V Sarma, Corey W Arnold, Ziyue Xu, Holger R Roth, Dong Yang, Jesse Tetreault, Daguang Xu, Krishnan R Patel, Sandeep Gurram, Bradford J Wood, Deborah E Citrin, Peter A Pinto, Peter L Choyke, Baris Turkbey
OBJECTIVE: Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters. MATERIALS AND METHODS: A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors)...
March 21, 2024: Abdominal Radiology