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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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Imaging Studies VI: Voiding Cystourethrography and Cystography01:22

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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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Imaging Studies V: Intravenous Urography and Retrograde Pyelography01:22

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IntroductionIntravenous Urography (IVU) and Retrograde Pyelography (RP) are important diagnostic imaging techniques used to evaluate the urinary system. These methods help identify structural abnormalities, obstructions, and functional issues in the kidneys, ureters, and bladder. Both procedures use iodine-based contrast media to enhance the visibility of urinary tract structures on X-ray images, though they differ in their methods and indications.1. Intravenous Urography (IVU)Intravenous...
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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Urethra contours on MRI: Multidisciplinary consensus educational atlas and reference standard for artificial

Yuze Song1, Lily Nguyen2, Anna M Dornisch3

  • 1Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.

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A new AI model accurately segments the urethra in prostate cancer treatment planning, outperforming physicians in key metrics. This tool offers reliable urethra segmentation for improved radiotherapy planning and dose-toxicity studies.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate urethra identification is crucial for prostate cancer treatment but challenging for physicians.
  • Existing automated segmentation tools lack reliable validation due to insufficient ground truth and evaluation standards.

Purpose of the Study:

  • Establish a reference-standard dataset for urethra segmentation using expert consensus.
  • Define clinically relevant evaluation metrics for segmentation accuracy.
  • Assess the performance and generalizability of a deep-learning-based segmentation model.

Main Methods:

  • A multidisciplinary panel created consensus urethra contours on MRI data from 71 patients across 6 centers.
  • A deep-learning AI model was developed using an independent training dataset (n=151).
  • AI performance was evaluated against the reference standard and human performance using Dice, coverage, and Hausdorff Distance (HD).

Main Results:

  • The AI model achieved a median Dice of 0.40 and 89% coverage, outperforming the average physician in a subset of cases.
  • AI performance remained consistent across the full reference dataset, demonstrating generalizability.
  • The AI model showed a Max 2D HD of 2.0 mm, indicating high accuracy.

Conclusions:

  • A consensus benchmark for urethra segmentation was established.
  • The deep-learning model demonstrates comparable performance to specialist physicians.
  • The AI tool shows potential as a clinical decision-support system for precise urethra segmentation in prostate cancer radiotherapy.