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Proceedings Paper

Automatic detection of kidney in 3D pediatric ultrasound images using deep neural networks
Author(s): Pooneh R. Tabrizi; Awais Mansoor; Elijah Biggs; James Jago; Marius George Linguraru
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Paper Abstract

Ultrasound (US) imaging is the routine and safe diagnostic modality for detecting pediatric urology problems, such as hydronephrosis in the kidney. Hydronephrosis is the swelling of one or both kidneys because of the build-up of urine. Early detection of hydronephrosis can lead to a substantial improvement in kidney health outcomes. Generally, US imaging is a challenging modality for the evaluation of pediatric kidneys with different shape, size, and texture characteristics. The aim of this study is to present an automatic detection method to help kidney analysis in pediatric 3DUS images. The method localizes the kidney based on its minimum volume oriented bounding box) using deep neural networks. Separate deep neural networks are trained to estimate the kidney position, orientation, and scale, making the method computationally efficient by avoiding full parameter training. The performance of the method was evaluated using a dataset of 45 kidneys (18 normal and 27 diseased kidneys diagnosed with hydronephrosis) through the leave-one-out cross validation method. Quantitative results show the proposed detection method could extract the kidney position, orientation, and scale ratio with root mean square values of 1.3 ± 0.9 mm, 6.34 ± 4.32 degrees, and 1.73 ± 0.04, respectively. This method could be helpful in automating kidney segmentation for routine clinical evaluation.

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751Z (27 February 2018); doi: 10.1117/12.2295206
Show Author Affiliations
Pooneh R. Tabrizi, Children’s National Health System (United States)
Awais Mansoor, Children’s National Health System (United States)
Elijah Biggs, Children’s National Health System (United States)
James Jago, Philips Healthcare (United States)
Marius George Linguraru, Children’s National Health System (United States)
George Washington Univ. (United States)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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