Abstract
Urodynamic tests are used to assess bladder function by measuring detrusor pressure, which requires invasive catheterization. Ultrasound bladder vibrometry offers a non-invasive approach to evaluate bladder compliance for diagnostic purposes by estimating detrusor pressure from ultrasound-generated Lamb waves in the bladder wall. Therefore, it is crucial to precisely segment the anterior portion of bladder wall prior to these assessments. Traditional segmentation methods are time-consuming and require manual intervention, while deep learning offers a promising alternative. Existing deep learning approaches are limited and often lack clinical validation. Therefore, we propose a novel deep learning network for precise segmentation of anterior bladder wall. It comprises blueprint separable convolutional layers in an encoder-decoder structure with adaptive attention-based skip connections. Performance evaluation on 8592 distinct images acquired from 64 patients using 5-fold cross-validation demonstrates that it achieves a mean Dice score and sensitivity of 0.82 and 0.85 respectively, along with a mean root mean square error of $ 0.67 \pm 0.35 \;\text{mm}$ between the thickness of predicted and ground truth portions of the bladder wall. Blueprint separable convolutions and adaptive attention-skip connections improve segmentation performance with fewer computations compared to respective standard counterparts. A comparative analysis demonstrates improvements between 2-20% in the dice score with respect to some but one of the existing networks and reductions in computational complexity by $94-96\%$ with respect to all existing networks analyzed in this work. Therefore, the proposed method can be effective for accurate bladder wall segmentation and demonstrates potential for real-time application in clinical settings.