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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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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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Computed Tomography (CT) scan:
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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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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
Fundamental Principles of PET
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Related Experiment Video

Updated: Oct 5, 2025

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
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Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform.

Jan Egger1,2,3, Daniel Wild4,5, Maximilian Weber4,5

  • 1Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Australia. egger@tugraz.at.

Journal of Digital Imaging
|January 22, 2022
PubMed
Summary

Studierfenster is a free, open-science framework for biomedical image analysis, offering 2D/3D visualization and advanced AI tools. It received high usability scores in a user study, demonstrating practical potential for medical and non-medical research.

Keywords:
Augmented realityCNNClient/serverCloudDeep learningGANITKMedical image analysisPythonVTKVirtual realityWhitepaper

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging Analysis
  • Computational Science

Background:

  • Computed tomography (CT) and magnetic resonance imaging (MRI) are crucial for medical diagnostics and treatment planning.
  • Automatic algorithms significantly enhance medical image analysis, aiding in segmentation, registration, and visualization.
  • There is a need for accessible, versatile tools for processing complex biomedical imaging data.

Purpose of the Study:

  • Introduce Studierfenster, a free, non-commercial, open-science client-server framework for biomedical image analysis.
  • Showcase its capabilities in 2D/3D visualization, segmentation, landmarking, and advanced AI-driven analyses.
  • Evaluate the usability and practical performance of Studierfenster through a user study.

Main Methods:

  • Developed a client-server framework enabling web browser-based visualization of medical data (CT, MRI).
  • Implemented functionalities including metric calculation (Dice, Hausdorff), manual outlining, landmark placement, VR/AR integration.
  • Integrated advanced AI models: CNNs for cranial implant design and aortic landmark detection, GANs for aortic dissection inpainting.
  • Conducted a user study with medical and non-medical experts to assess usability and manual functionalities.

Main Results:

  • Studierfenster provides comprehensive 2D/3D visualization of medical data in common web browsers.
  • User study participants rated Studierfenster highly (mean 6.3/7.0) for overall impression and usability.
  • Achieved practical results comparable to physician-performed ground truth segmentations.
  • Demonstrated potential for AI-driven tasks like automatic implant design and landmark detection.

Conclusions:

  • Studierfenster offers a powerful, versatile online environment for biomedical image analysis, processing 3D volumes effectively.
  • Its client-server architecture and broad functionalities support diverse applications in medicine and beyond.
  • The framework's open-science approach and potential for extension make it valuable for researchers in various fields, including veterinary medicine and non-medical imaging analysis.