Abstract
Congenital hydronephrosis, which is the dilatation of the renal collecting system, is common in children, resolving in most and treatable in the remaining 25%. Sonography is routinely used for hydronephrosis detection and longitudinal evaluation but lacks standardization in acquisition and provides no information about kidney function. These facts make the visual assessment of hydronephrosis from ultrasound subjective and variable. In this paper, we present an automatic method to standardize the analysis of the kidney regions in sonograms for the quantification of hydronephrosis severity as well as the prediction of obstruction. First, the field-of-view in images is standardized by segmenting the kidney regions using convolutional neural networks and reorienting them along their longest axes in the coronal view. Then, the core areas of the kidney containing the pelvis and calyces are identified by correlation analysis. Each standardized kidney image slice is evaluated using a deep learning-based approach to predict the obstruction severity, and the slice-based predictive scores are fused based on a weighted-voting technique to determine the final risk score. The performance of the method was evaluated on 54 hydronephrotic kidneys with known clinical outcome. Results show that our method could automatically predict the obstruction severity with an average accuracy of 0.83, a significant improvement over the common clinical approach (p-value < 0.001). Our method has the potential to predict kidney function from routine ultrasound evaluation.
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Roshanitabrizi, P. et al. (2021). Standardized Analysis of Kidney Ultrasound Images for the Prediction of Pediatric Hydronephrosis Severity. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_38
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