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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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Updated: Mar 16, 2026

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
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Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study.

Daniel F Polan1, Samuel L Brady, Robert A Kaufman

  • 1Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, USA. Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis TN, USA.

Physics in Medicine and Biology
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Summary
This summary is machine-generated.

This study developed a fully automated Random Forest algorithm for organ segmentation in diagnostic CT scans, achieving high accuracy in pediatric and adult patients for improved radiomics and dose calculations.

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Radiology

Background:

  • Automated organ segmentation in diagnostic CT is crucial for advanced applications like radiomics and personalized radiation dose calculations.
  • Current CT innovations necessitate precise knowledge of anatomical structures within images.
  • Existing segmentation methods often lack full automation or robustness across diverse patient demographics and scan parameters.

Purpose of the Study:

  • To develop and optimize a fully automated Random Forest classifier for segmenting neck-chest-abdomen-pelvis CT examinations.
  • To investigate the performance and limitations of this algorithm in both pediatric and adult patient populations.
  • To establish a trainable segmentation tool applicable to diagnostic CT environments.

Main Methods:

  • Utilized a Random Forest algorithm with 200 trees and 2 randomly selected features per node.
  • Employed the trainable Weka segmentation (TWS) plugin in FIJI/Matlab for feature extraction, including filters like Gaussian and Kuwahara.
  • Classified seven material types: background, air/gas, fat, muscle, solid organ parenchyma, blood/contrast fluid, and bone.
  • Optimized the algorithm using 16 image features derived from various filters and evaluated performance using Dice Similarity Coefficient (DSC).

Main Results:

  • Achieved a median DSC of 0.86 ± 0.03 for pediatric and 0.85 ± 0.04 for adult patient protocols.
  • Demonstrated a mean sensitivity of 0.91, specificity of 0.89, and accuracy of 0.90 across 100 segmented patient examinations.
  • The automated algorithm proved effective for segmenting neck and trunk regions across varied patient sizes and scan parameters.

Conclusions:

  • The developed Random Forest-based automated segmentation tool provides fast and accurate organ segmentation in diagnostic CT.
  • This method shows significant potential for enhancing radiomics analysis and patient-specific dosimetry.
  • The algorithm's robustness across pediatric and adult patients marks a significant advancement in automated medical image analysis.