Ultrasound based computer-aided-diagnosis of kidneys for pediatric hydronephrosis

Juan J. Cerrolaza; Craig A. Peters; Aaron D. Martin; Emmarie Myers; Nabile Safdar; Marius George Linguraru

doi:10.1117/12.2043072

18 March 2014 Ultrasound based computer-aided-diagnosis of kidneys for pediatric hydronephrosis

Juan J. Cerrolaza, Craig A. Peters, Aaron D. Martin, Emmarie Myers, Nabile Safdar, Marius George Linguraru

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 90352T (2014) https://doi.org/10.1117/12.2043072
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Ultrasound is the mainstay of imaging for pediatric hydronephrosis, though its potential as diagnostic tool is limited by its subjective assessment, and lack of correlation with renal function. Therefore, all cases showing signs of hydronephrosis undergo further invasive studies, like diuretic renogram, in order to assess the actual renal function. Under the hypothesis that renal morphology is correlated with renal function, a new ultrasound based computer-aided diagnosis (CAD) tool for pediatric hydronephrosis is presented. From 2D ultrasound, a novel set of morphological features of the renal collecting systems and the parenchyma, is automatically extracted using image analysis techniques. From the original set of features, including size, geometric and curvature descriptors, a subset of ten features are selected as predictive variables, combining a feature selection technique and area under the curve filtering. Using the washout half time (T_1/2) as indicative of renal obstruction, two groups are defined. Those cases whose T_1/2 is above 30 minutes are considered to be severe, while the rest would be in the safety zone, where diuretic renography could be avoided. Two different classification techniques are evaluated (logistic regression, and support vector machines). Adjusting the probability decision thresholds to operate at the point of maximum sensitivity, i.e., preventing any severe case be misclassified, specificities of 53%, and 75% are achieved, for the logistic regression and the support vector machine classifier, respectively. The proposed CAD system allows to establish a link between non-invasive non-ionizing imaging techniques and renal function, limiting the need for invasive and ionizing diuretic renography.

Citation Download Citation

Juan J. Cerrolaza, Craig A. Peters, Aaron D. Martin, Emmarie Myers, Nabile Safdar, and Marius George Linguraru "Ultrasound based computer-aided-diagnosis of kidneys for pediatric hydronephrosis", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352T (18 March 2014); https://doi.org/10.1117/12.2043072

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