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Related Concept Videos

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...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Brain Imaging01:14

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

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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.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
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Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Positron Emission Tomography01:29

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
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Updated: Sep 17, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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[AI-based applications in medical image computing].

Timo Kepp1, Hristina Uzunova1, Jan Ehrhardt1,2

  • 1DFKI Forschungsbereich KI in der medizinischen Bild- und Signalverarbeitung, Deutsches Forschungszentrum für Künstliche Intelligenz, Ratzeburger Allee 160, 23562, Lübeck, Deutschland.

Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz
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PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI), particularly deep learning, is revolutionizing medical image analysis for diagnostics and therapy. AI enhances segmentation, registration, and synthesis, improving clinical workflows and patient care.

Keywords:
Convolutional neural networksDeep learningGenerative AIImage registrationImage segmentation

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Area of Science:

  • Medical image analysis
  • Artificial intelligence in healthcare
  • Deep learning applications

Context:

  • Medical image processing is crucial for diagnostics and therapy.
  • High variability and complexity of medical images present challenges.
  • Artificial intelligence (AI) has driven significant progress in medical image analysis.

Purpose:

  • To highlight the role of AI, specifically deep learning, in advancing medical image analysis.
  • To showcase AI applications in segmentation, registration, and image synthesis.
  • To illustrate the practical relevance and potential of AI in various medical fields.

Summary:

  • Deep learning methods are successfully applied in medical image analysis tasks like segmentation, registration, and synthesis.
  • AI-based segmentation precisely delineates anatomical structures and pathologies.
  • AI-based registration accelerates the creation of 3D surgical planning models.
  • Generative AI can create synthetic data to improve AI model training.

Impact:

  • AI accelerates clinical workflows and enhances diagnostic and therapeutic capabilities.
  • AI applications in radiology, ophthalmology, dermatology, and surgery demonstrate practical relevance.
  • AI offers new opportunities for improved patient care through advanced medical imaging.