Automated Measurement of Membranous Urethral Length (MUL) on MRI Images Using Deep Learning

Motivation: Measuring membranous urethral length (MUL) on MRI can predict urinary continence outcomes after prostate cancer surgery, but requires expert radiologists - a resource many hospitals lack. Manual measurements are time-consuming and subjective, creating treatment planning delays. Goal(s): Develop an automated deep learning system to identify, segment, and measure MUL from prostate MRI scans with accuracy comparable to expert radiologists. Approach: Created an AI pipeline using a modified U-Net architecture: optimal slice selection, MU segmentation via bounding box prediction, and automated MUL calculation. Results: System achieved sub-millimeter accuracy (mean difference 0.9±1.5mm) from expert measurements, with consistent performance across anatomical variations and validation by senior radiologists. Impact: By eliminating the need for specialized radiologist expertise, this automated system could enable widespread adoption of MUL-based surgical planning in resource-limited settings, helping surgeons optimize their approach to preserve urinary continence for prostate cancer patients.