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Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

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Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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Level-Set-Based Kidney Segmentation from DCE-MRI Using Fuzzy Clustering with Population-Based and Subject-Specific

Moumen El-Melegy1, Rasha Kamel2, Mohamed Abou El-Ghar3

  • 1Electrical Engineering Department, Assiut University, Assiut 71515, Egypt.

Bioengineering (Basel, Switzerland)
|November 10, 2022
PubMed
Summary

A new method accurately segments kidneys in dynamic contrast-enhanced MRI (DCE-MRI) for detecting acute renal allograft rejection. This approach combines fuzzy clustering with level sets and shape statistics, outperforming existing methods.

Keywords:
DCE-MRIU-Netfuzzy c-meanskidney segmentationlevel setstatistical shape models

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Radiology

Background:

  • Accurate kidney segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is crucial for noninvasive detection of acute renal allograft rejection.
  • Existing segmentation methods may lack accuracy, especially with challenging image qualities.

Purpose of the Study:

  • To propose a novel and accurate DCE-MRI kidney segmentation method.
  • To improve early and noninvasive detection of acute renal allograft rejection.

Main Methods:

  • Integration of fuzzy c-means (FCM) clustering within a level set framework with iterative fuzzy membership updates.
  • Utilizing both population-based shape (PB-shape) and subject-specific shape (SS-shape) statistics for enhanced contour evolution.
  • Offline training of PB-shape models and on-the-fly training of SS-shape models.

Main Results:

  • Achieved high segmentation accuracy on real medical datasets (n=45) with a Dice Similarity Coefficient (DSC) of 0.953 ± 0.018 and Intersection-over-Union (IoU) of 0.91 ± 0.033.
  • Demonstrated superior performance over state-of-the-art level set methods, showing an average Hausdorff distance 95 (HD95) improvement of 0.7.
  • Outperformed U-Net based deep learning methods with HD95 improvements of 9.5 and 3.8, particularly on low-contrast and noisy images.

Conclusions:

  • The proposed method offers a significant advancement in DCE-MRI kidney segmentation accuracy.
  • This enhanced accuracy aids in the reliable early detection of acute renal allograft rejection.
  • The method shows particular promise for improving segmentation in challenging, low-quality medical images.