27 March 2014 Performance of an automated renal segmentation algorithm based on morphological erosion and connectivity
Author Affiliations +
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.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Benjamin Abiri, Benjamin Abiri, Brian Park, Brian Park, Hersh Chandarana, Hersh Chandarana, Artem Mikheev, Artem Mikheev, Vivian S. Lee, Vivian S. Lee, Henry Rusinek, 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); doi: 10.1117/12.2043596; https://doi.org/10.1117/12.2043596
PROCEEDINGS
5 PAGES


SHARE
Back to Top