Automated Bladder Segmentation of 3D Dynamic MRI For Urodynamic Analysis Using Deep Learning

Motivation: Bladder dysfunction is assessed using catheterization which are invasive and provides insufficient biomechanical information. MRI urodynamics is tedious because segmentation of bladders over numerous time steps during voiding. Goal(s): Implement automated segmentation using deep learning for accelerating the workflow of MRI-based urodynamic assessment. Approach: Train a U-Net using 3D dynamic images and manually segmented masks. Use time and dice score to assess the performance of the network. Results: Images of bladder voiding from five subjects were used to train the network and can segment one bladder in <3 minutes, compared to 20 minutes for manual segmentation. Dice score was 0.99 showing excellent performance. Impact: Urodynamic assessment using MRI is a tedious process due to segmentation of the bladder from 3D dynamic image datasets. We automated segmentation using deep learning to accelerate the workflow. Our automated process reduced time sixfold and produces excellent segmentation.