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Related Concept Videos

Computed Tomography01:10

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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.
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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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Related Experiment Video

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Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
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Segmentation of abdominal organs in computed tomography using a generalized statistical shape model.

Agata Krasoń1, Andre Woloshuk1, Dominik Spinczyk2

  • 1Biomedical Engineering, Silesian University of Technology, 40 Roosevelta, 41-800, Zabrze, Poland.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|November 13, 2019
PubMed
Summary

This study introduces a novel statistical shape model for segmenting abdominal organs in computed tomography (CT) scans. The method achieved high accuracy, offering a step towards universal organ segmentation in medical imaging.

Keywords:
Abdominal anatomy segmentationGeneralized statistical shape model

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnostics
  • Biomedical Engineering

Background:

  • Accurate segmentation of anatomical structures in computed tomography (CT) images is crucial for computer-aided diagnostics and therapy.
  • The abdominal cavity presents segmentation challenges due to complex anatomy, variants, and pathologies.

Purpose of the Study:

  • To present a generalized statistical shape model for segmenting abdominal organs in CT images.
  • To evaluate the method's performance on a diverse set of abdominal organs.

Main Methods:

  • Development of a segmentation method based on a generalized statistical shape model.
  • Application of the method to 40 contrast-enhanced CT cases (20 training, 20 testing).
  • Expert delineation of spleen, kidney, liver, pancreas, and duodenum for validation.

Main Results:

  • High average DICE coefficients achieved: spleen (0.96), kidney (0.93), liver (0.88), pancreas (0.86), and duodenum (0.81).
  • The method demonstrated robustness across different parenchymal organs without requiring parameter re-selection.

Conclusions:

  • The developed statistical shape model offers a promising approach for automated organ segmentation in CT imaging.
  • The method shows potential as a universal segmentation tool for normalized, scaled medical images.