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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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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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Voiding Cystourethrography (VCUG) and Cystography are specialized radiographic procedures used to examine the structure and function of the bladder and urethra.Voiding Cystourethrography (VCUG)A Voiding Cystourethrogram (VCUG) is a diagnostic imaging procedure that assesses the anatomy and function of the lower urinary tract. It focuses on the bladder, bladder neck, and urethra, helping detect abnormalities such as vesicoureteral reflux (VUR)—the backward or reverse flow of urine into the...
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
111
Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

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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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MRI and CT bladder segmentation from classical to deep learning based approaches: Current limitations and lessons.

Mark G Bandyk1, Dheeraj R Gopireddy2, Chandana Lall2

  • 1Department of Urology, University of Florida, Jacksonville, FL, USA.

Computers in Biology and Medicine
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Accurate bladder cancer staging requires precise segmentation of tumor invasion. Deep learning shows promise for improving bladder cancer segmentation accuracy, aiding personalized treatment selection.

Keywords:
Bladder cancerBladder segmentationConvolutional neural networksDeep learning

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Area of Science:

  • Medical Imaging
  • Oncology
  • Artificial Intelligence

Background:

  • Accurate staging of bladder cancer (BC) is crucial for risk stratification and personalized therapy.
  • Segmentation of bladder walls and tumors provides essential information for primary tumor staging.
  • Deep learning-based multiregion segmentation offers potential for enhanced staging accuracy and prediction of tumor behavior.

Purpose of the Study:

  • To provide an in-depth analysis of deep learning models for bladder cancer segmentation.
  • To highlight critical factors for differentiating muscle-invasive disease.
  • To review the current status, challenges, and limitations of deep learning in bladder segmentation.

Main Methods:

  • Review of existing deep learning approaches for medical image segmentation.
  • Analysis of challenges specific to bladder cancer segmentation in MRI and CT modalities.
  • Evaluation of prior work and identification of limitations in current methods.

Main Results:

  • Deep learning models are emerging for bladder cancer segmentation, but progress is nascent.
  • Existing methods often adapt approaches from other clinical problems without validating for bladder-specific challenges.
  • Accurate differentiation of muscle invasion remains a key challenge.

Conclusions:

  • Deep learning holds significant potential for improving bladder cancer segmentation and staging.
  • Further research is needed to address the unique challenges of bladder segmentation.
  • Optimized deep learning strategies are essential for advancing bladder cancer diagnosis and treatment.