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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

195
Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Automatic renal segmentation for MR urography using 3D-GrabCut and random forests.

Umit Yoruk1, Brian A Hargreaves1, Shreyas S Vasanawala1

  • 1Stanford University, Stanford, California, USA.

Magnetic Resonance in Medicine
|June 29, 2017
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Summary
This summary is machine-generated.

A new automated renal segmentation technique accurately assesses glomerular filtration rate (GFR) in children. This method significantly reduces analysis time from hours to seconds, matching manual segmentation performance.

Keywords:
dynamic contrast enhanced MRIglomerular filtration ratemachine learningrenal segmentation

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Area of Science:

  • Medical Imaging
  • Radiology
  • Pediatric Nephrology

Background:

  • Accurate glomerular filtration rate (GFR) assessment is crucial for pediatric kidney health.
  • Manual segmentation of renal structures in dynamic contrast-enhanced MRI is time-consuming and labor-intensive.
  • Developing automated methods can improve efficiency and consistency in GFR estimation.

Purpose of the Study:

  • To introduce and evaluate a fully automated renal segmentation technique for GFR assessment in children.
  • To compare the performance of automated segmentation with manual segmentation.
  • To determine the time efficiency of the automated method.

Main Methods:

  • Modified iterative graph cuts (GrabCut) for 3D dynamic contrast-enhanced MRI segmentation.
  • Random forest classifier to segment renal cortex, medulla, and collecting system.
  • Validation using F1-score against manual segmentation in 26 subjects; GFR estimation via a two-compartment model.

Main Results:

  • High similarity between automated and manual segmentation maps (whole-kidney F1=0.93, cortex F1=0.86).
  • Strong correlation between automated and manual GFR estimations (Spearman's ρ=0.99).
  • Mean GFR error of 2.98±0.66% with an average segmentation time of 45 seconds.

Conclusions:

  • The automated renal segmentation method achieves performance comparable to manual segmentation for GFR estimation.
  • This automated technique drastically reduces segmentation time from hours to 45 seconds.
  • The method offers a viable, efficient alternative for pediatric GFR assessment using MRI.