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Proceedings Paper

Performance of an automated renal segmentation algorithm based on morphological erosion and connectivity
Author(s): Benjamin Abiri; Brian Park; Hersh Chandarana; Artem Mikheev; Vivian S. Lee; Henry Rusinek
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Paper Abstract

The precision, accuracy, and efficiency of a novel semi-automated segmentation technique for VIBE MRI sequences was analyzed using clinical datasets. Two observers performed whole-kidney segmentation using EdgeWave software based on constrained morphological growth, with average inter-observer disagreement of 2.7% for whole kidney volume, 2.1% for cortex, and 4.1% for medulla. Ground truths were prepared by constructing ROI on individual slices, revealing errors of 2.8%, 3.1%, and 3.6%, respectfully. It took approximately 7 minutes to perform one segmentation. These improvements over our existing graph-cuts segmentation technique make kidney volumetry a reality in many clinical applications.

Paper Details

Date Published: 27 March 2014
PDF: 5 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352R (27 March 2014); doi: 10.1117/12.2043596
Show Author Affiliations
Benjamin Abiri, New York Univ. School of Medicine (United States)
Brian Park, New York Univ. School of Medicine (United States)
Hersh Chandarana, New York Univ. School of Medicine (United States)
Artem Mikheev, New York Univ. School of Medicine (United States)
Vivian S. Lee, Univ. of Utah Health Systems (United States)
Henry Rusinek, New York Univ. School of Medicine (United States)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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