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Abstract #0112

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

Adamos Hadjivasiliou1, Kelly Hong1, Imaad Zaffar1, Louise Dickinson1, Zafer Tandogdu1, and Ivana Drobnjak1
1UCL, London, United Kingdom

Synopsis

Keywords: Prostate, Prostate, Segmentation

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.

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