Performance of an automated renal segmentation algorithm based on morphological erosion and connectivity

Benjamin Abiri; Brian Park; Hersh Chandarana; Artem Mikheev; Vivian S. Lee; Henry Rusinek

doi:10.1117/12.2043596

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27 March 2014 Performance of an automated renal segmentation algorithm based on morphological erosion and connectivity

Benjamin Abiri, Brian Park, Hersh Chandarana, Artem Mikheev, Vivian S. Lee, Henry Rusinek

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Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 90352R (2014) https://doi.org/10.1117/12.2043596
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

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Benjamin Abiri, Brian Park, Hersh Chandarana, Artem Mikheev, Vivian S. Lee, and Henry Rusinek "Performance of an automated renal segmentation algorithm based on morphological erosion and connectivity", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352R (27 March 2014); https://doi.org/10.1117/12.2043596

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