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.
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). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model.
683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05-329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707-0.971), 0.873 (0-0.997), and 0.894 (0.025-0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001).
Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models.
Abdominal radiology (New York). 2024 Mar 21 [Epub]
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
Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Department of Urology, Hawaii Pacific Health, Honolulu, HI, USA., Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA., Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA., NVIDIA Corporation, Santa Clara, CA, USA., Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Center for Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD, USA., Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. .