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Related Experiment Videos

Performance of an automated segmentation algorithm for 3D MR renography.

Henry Rusinek1, Yuri Boykov, Manmeen Kaur

  • 1Department of Radiology, New York University School of Medicine, New York, NY 10016, USA. hr18@nyu.edu

Magnetic Resonance in Medicine
|May 31, 2007
PubMed
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An automated graph-cuts (GC) segmentation technique for dynamic contrast-enhanced 3D MR renography (DCE-MRR) offers accurate and precise kidney function analysis. This method significantly reduces processing time compared to manual segmentation, aiding in diagnosing renal insufficiency.

Area of Science:

  • Medical Imaging
  • Nephrology
  • Computational Anatomy

Background:

  • Dynamic contrast-enhanced 3D MR renography (DCE-MRR) is a valuable tool for assessing kidney function.
  • Accurate segmentation of renal structures is crucial for reliable DCE-MRR analysis.
  • Manual segmentation is time-consuming and prone to variability.

Purpose of the Study:

  • To evaluate the accuracy and precision of an automated graph-cuts (GC) segmentation technique for DCE-MRR.
  • To compare the processing time of automated GC segmentation with manual segmentation.
  • To assess the clinical utility of automated DCE-MRR for renal function assessment.

Main Methods:

  • Analysis of 18 simulated and 22 clinical datasets using automated graph-cuts (GC) segmentation.

Related Experiment Videos

  • Quantification of segmentation accuracy and precision for renal cortex and medulla volumes.
  • Compartmental modeling to determine renal plasma flow (RPF) and single-kidney glomerular filtration rate (GFR) errors and precision.

    • Main Results:

    • Automated GC segmentation demonstrated acceptable accuracy and precision for renal volumes (e.g., cortex error: 7.2 ± 6.1 cm³).
    • Renal function parameters showed acceptable errors (RPF: 7.5 ± 4.5%, GFR: 13.5 ± 8.8%).
    • Automated segmentation required 21 minutes per kidney, significantly faster than 2.5 hours for manual segmentation.

    Conclusions:

    • Automated GC segmentation is accurate and precise for DCE-MRR, suitable for clinical applications.
    • The technique significantly reduces processing time, enabling efficient renal function assessment.
    • DCE-MRR with automated segmentation holds potential for expanding knowledge of renal function and diagnosing renal insufficiency noninvasively.