Fully end-to-end automated bladder segmentation and risk assessment based on bladder morphology features for predicting upper urinary tract dysfunction.

BACKGROUND

This study aims to analyze bladder morphology features for upper urinary tract dysfunction (UUTD) risk assessment and develop a fully end-to-end (E2E) automated segmentation and risk identification algorithm.

MATERIALS AND

METHODS

A retrospective cohort of 604 neurogenic bladder patients undergoing video-urodynamics was analyzed. Bladder regions of interest (ROIs) were manually and automatically segmented. Thirty-six morphology features, including shape, Laplacian of Gaussian histograms, and fractal characteristics, were extracted from ROIs. After LASSO regression, selected predictive features were integrated into machine learning models. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis.

RESULTS

Based on manually segmented ROI, 28 (77.78%) morphology features exhibited significant differences between different bladder grades. Shape_Perimeter_to_Surface_ratio emerged as the most significant predictor of UUTD (AUC = 0.722, 95% confidence interval [CI]: 0.674-0.769). The support vector machine (SVM) model demonstrated the highest performance, with an AUC of 0.860 (95% CI: 0.820-0.899) in the training cohort and 0.830 (95% CI: 0.744-0.915) in the test cohort, which was significantly higher than the performance of using single features. However, incorporating clinical information did not further enhance the overall predictive performance of the model. To realize a fully E2E approach, a fully convolutional network with a ResNet101 backbone (FCN-ResNet101) was adopted for automated bladder segmentation (dice similarity coefficient [Dice] = 0.9848, mean intersection over union [mIoU] = 0.9705). Morphology features extracted from automated and manual segmentations showed a strong correlation (all r > 0.90). Similarly, machine learning models were developed, with SVM achieving the highest performance (AUC = 0.843, 95% CI: 0.801-0.885 in the training cohort; AUC = 0.815, 95% CI: 0.729-0.900 in the testing cohort).

CONCLUSION

The bladder morphology features are robust predictors of UUTD. This fully automated, E2E bladder segmentation and UUTD risk prediction model demonstrated superior performance compared to traditional clinical parameters, providing a quantitative and objective tool for early risk assessment. Large-scale, multicenter prospective studies are required to validate its clinical applicability and robustness in the future.