Performance of an automated renal segmentation algorithm based on morphological erosion and connectivity
Benjamin Abiri, Brian Park, Hersh Chandarana, Artem V. Mikheev, Vivian S. Lee, Henry Rusinek · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
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.