A system and methodology for the automated localization, extraction, and analysis of MRI-based features with clinical information to improve the diagnosis of pelvic organ prolapse (POP). The system can automatically identify reference points for pelvic floor measurements on MRI rapidly and consistent. It provides a prediction model that analyzes the correlation between current and new MRI-based features with clinical information to differentiate patients with and without POP. This system will enable the high throughput analysis of MR images for their correlation with clinical information to better detect POP. The presented system can also be applied to the automated localization and extraction of MRI features for the diagnosis of other diseases where clinical examination is not adequate.
Onal, Sinan; Lai-Yuen, Susana Karina; Bao, Paul; Weitzenfeld, Alfredo; and Hart, Stuart Richard, "Image-based automated measurement model to predict pelvic organ prolapse" (2020). USF Patents. 1138.
University of South Florida