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

Imaging Studies I: Kidney, Ureter, and Bladder Studies

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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Style Transfer-assisted Deep Learning Method for Kidney Segmentation at Multiphase MRI.

Junyu Guo1, Manu Goyal1, Yin Xi1

  • 1From the Department of Radiology (J.G., M.G., Y.X., L.H., G.H., E.A., I.P.), Department of Urology (I.P.), and Advanced Imaging Research Center (I.P.), University of Texas Southwestern Medical Center, 2201 Inwood Rd, Suite 202, Dallas, TX 75390-9085.

Radiology. Artificial Intelligence
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

A novel deep learning method accurately segments kidneys in multiphase contrast-enhanced MRI scans. This approach utilizes generative adversarial networks (GANs) and convolutional neural networks (CNNs) for precise kidney segmentation.

Keywords:
Convolutional Neural NetworkCycleGANGenerative Adversarial NetworkKidney SegmentationTransfer Learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Deep Learning for Medical Image Analysis

Background:

  • Accurate kidney segmentation is crucial for diagnosing and monitoring renal diseases.
  • Manual segmentation of kidneys in multiphase contrast-enhanced (MCE) MRI is time-consuming and subject to inter-observer variability.
  • Developing automated segmentation methods can improve efficiency and consistency in clinical practice.

Purpose of the Study:

  • To develop and validate a semisupervised, style transfer-assisted deep learning method for automated kidney segmentation.
  • To utilize multiphase contrast-enhanced (MCE) MRI acquisitions for robust kidney segmentation.
  • To evaluate the performance of the proposed deep learning model against manual segmentation.

Main Methods:

  • A Cycle-Consistent Generative Adversarial Network (CycleGAN) was trained to generate synthetic MCE MRI-like datasets from T2-weighted images.
  • Mask region-based convolutional neural networks (CNNs) were trained on these synthetic datasets for kidney segmentation.
  • The model was trained on 125 patients and validated on a separate cohort of 20 MCE MRI examinations.
  • Segmentation performance was assessed using Dice and Jaccard scores.

Main Results:

  • The CycleGAN successfully generated anatomically coregistered synthetic MCE MRI datasets.
  • The deep learning approach achieved high mean Dice scores across all four MCE MRI phases: 0.91 (precontrast), 0.92 (corticomedullary), 0.91 (early nephrographic), and 0.93 (nephrographic).
  • The method demonstrated high performance in kidney segmentation on diverse MCE MRI acquisitions.

Conclusions:

  • The proposed semisupervised deep learning method, leveraging style transfer with CycleGAN and CNNs, provides accurate and automated kidney segmentation.
  • This approach shows significant potential for improving efficiency and consistency in renal MRI analysis.
  • The technique is effective across various phases of MCE MRI, offering a versatile tool for clinical applications.