24 March 2016 Semi-automatic assessment of pediatric hydronephrosis severity in 3D ultrasound
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Abstract
Hydronephrosis is the most common abnormal finding in pediatric urology. Thanks to its non-ionizing nature, ultrasound (US) imaging is the preferred diagnostic modality for the evaluation of the kidney and the urinary track. However, due to the lack of correlation of US with renal function, further invasive and/or ionizing studies might be required (e.g., diuretic renograms). This paper presents a computer-aided diagnosis (CAD) tool for the accurate and objective assessment of pediatric hydronephrosis based on morphological analysis of kidney from 3DUS scans. The integration of specific segmentation tools in the system, allows to delineate the relevant renal structures from 3DUS scans of the patients with minimal user interaction, and the automatic computation of 90 anatomical features. Using the washout half time (T1/2) as indicative of renal obstruction, an optimal subset of predictive features is selected to differentiate, with maximum sensitivity, those severe cases where further attention is required (e.g., in the form of diuretic renograms), from the noncritical ones. The performance of this new 3DUS-based CAD system is studied for two clinically relevant T1/2 thresholds, 20 and 30 min. Using a dataset of 20 hydronephrotic cases, pilot experiments show how the system outperforms previous 2D implementations by successfully identifying all the critical cases (100% of sensitivity), and detecting up to 100% (T1/2 = 20 min) and 67% (T1/2 = 30 min) of non-critical ones for T1/2 thresholds of 20 and 30 min, respectively.
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Juan J. Cerrolaza, Juan J. Cerrolaza, Hansel Otero, Hansel Otero, Peter Yao, Peter Yao, Elijah Biggs, Elijah Biggs, Awais Mansoor, Awais Mansoor, Roberto Ardon, Roberto Ardon, James Jago, James Jago, Craig A. Peters, Craig A. Peters, Marius George Linguraru, Marius George Linguraru, "Semi-automatic assessment of pediatric hydronephrosis severity in 3D ultrasound", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851Q (24 March 2016); doi: 10.1117/12.2216830; https://doi.org/10.1117/12.2216830
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