Performance of an automated segmentation algorithm for 3D MR renography

• Published in: Magnetic resonance in medicine 2007-06, Vol.57 (6), p.1159-1167
• Main Authors: Rusinek, Henry, Boykov, Yuri, Kaur, Manmeen, Wong, Samson, Bokacheva, Louisa, Sajous, Jan B., Huang, Ambrose J., Heller, Samantha, Lee, Vivian S.
• Format: Article
• Language: English
• Subjects: Adult; Aged; Algorithms; Computer Simulation; Contrast Media; error analysis; Female; Gadolinium DTPA; Humans; image analysis; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional; Kidney Diseases - diagnosis; magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Middle Aged; MR renography; Renal Circulation; renal function
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.21240

The accuracy and precision of an automated graph‐cuts (GC) segmentation technique for dynamic contrast‐enhanced (DCE) 3D MR renography (MRR) was analyzed using 18 simulated and 22 clinical datasets. For clinical data, the error was 7.2 ± 6.1 cm3 for the cortex and 6.5 ± 4.6 cm3 for the medulla. The precision of segmentation was 7.1 ± 4.2 cm3 for the cortex and 7.2 ± 2.4 cm3 for the medulla. Compartmental modeling of kidney function in 22 kidneys yielded a renal plasma flow (RPF) error of 7.5% ± 4.5% and single‐kidney GFR error of 13.5% ± 8.8%. The precision was 9.7% ± 6.4% for RPF and 14.8% ± 11.9% for GFR. It took 21 min to segment one kidney using GC, compared to 2.5 hr for manual segmentation. The accuracy and precision in RPF and GFR appear acceptable for clinical use. With expedited image processing, DCE 3D MRR has the potential to expand our knowledge of renal function in individual kidneys and to help diagnose renal insufficiency in a safe and noninvasive manner. Magn Reson Med 57:1159–1167, 2007. © 2007 Wiley‐Liss, Inc.