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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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,...
Computed Tomography01:10

Computed Tomography

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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Related Experiment Video

Updated: Jun 3, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies.

Alexander Horsch1, Alexander Hapfelmeier, Matthias Elter

  • 1Institute for Medical Statistics and Epidemiology, TU München, Ismaninger Str. 22, 81675 München, Germany. alexander.horsch@tum.de

International Journal of Computer Assisted Radiology and Surgery
|March 31, 2011
PubMed
Summary
This summary is machine-generated.

Evaluating mammography computer-aided detection (CAD) systems using the Digital Database for Screening Mammography (DDSM) shows highly diverse methods. This inconsistency makes comparing CAD system performance results difficult.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Computer-Aided Diagnosis

Background:

  • Breast cancer screening is crucial for reducing mortality.
  • Computer-aided detection (CAD) systems aim to improve mammogram interpretation.
  • Evaluating CAD system performance requires robust databases and statistical methods.

Purpose of the Study:

  • To analyze the characteristics and usage of the Digital Database for Screening Mammography (DDSM) in scientific literature.
  • To assess the evaluation methodologies applied to mammography CAD systems.
  • To identify inconsistencies in database usage and statistical methods for CAD system evaluation.

Main Methods:

  • Literature search of Medline, IEEE Xplore, SpringerLink, and SPIE.
  • Analysis of 59 publications (106 evaluation studies) using the DDSM.
  • Categorization based on CAD type (CADe, CADx), database subset selection, and statistical evaluation methods.

Main Results:

  • 50.9% of studies lacked reproducible selection of test items from DDSM.
  • Only 2 CADx studies, and no CADe studies, utilized the entire DDSM.
  • Diverse statistical methods (train/test, leave-one-out, N-fold cross-validation) were employed, with inconsistent terminology.

Conclusions:

  • The use of DDSM and statistical methods for evaluating mammography CAD systems is highly diverse.
  • Inconsistencies in study design and database usage lead to incomparable results.
  • Recommendations are provided for constructing and using test databases and statistical methods in future evaluations.