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Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Imaging Studies III: Computed Tomography01:27

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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...
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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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GANs for medical image analysis.

Salome Kazeminia1, Christoph Baur2, Arjan Kuijper3

  • 1Department of Computer Science, TU Darmstadt, Germany.

Artificial Intelligence in Medicine
|November 10, 2021
PubMed
Summary
This summary is machine-generated.

Generative adversarial networks (GANs) offer advanced solutions for medical image analysis tasks, including de-noising and segmentation. These deep learning models also show promise in overcoming data scarcity by synthesizing realistic medical images.

Keywords:
Deep learningGenerative adversarial networksMedical imagingSurvey

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Deep Learning

Background:

  • Generative adversarial networks (GANs) are powerful deep learning models.
  • GANs have shown potential in various medical image analysis tasks.
  • A significant challenge in medical AI is the scarcity of labeled data.

Purpose of the Study:

  • To provide a comprehensive review of recent literature on GANs for medical applications.
  • To discuss the limitations and potential of current GAN-based methods.
  • To outline future research directions in this field.

Main Methods:

  • Systematic review of published research papers on GANs in medical imaging.
  • Tabulation of essential details including methods, datasets, and performance metrics.
  • Development of an interactive visualization tool for categorizing reviewed papers.

Main Results:

  • GANs are effective for medical image de-noising, reconstruction, segmentation, data simulation, detection, and classification.
  • The realistic image synthesis capabilities of GANs offer a solution to medical data scarcity.
  • A curated overview of state-of-the-art GAN applications in medicine is presented.

Conclusions:

  • GANs represent a significant advancement in medical image analysis.
  • Further research is needed to address the shortcomings and fully leverage the opportunities of GANs in medicine.
  • The review provides a valuable resource for researchers and practitioners in the field.