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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

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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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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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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based

Seul Bi Lee1,2, Youngtaek Hong3, Yeon Jin Cho1,4

  • 1Department of Radiology, Seoul National University Hospital, Seoul, Korea.

Korean Journal of Radiology
|March 12, 2023
PubMed
Summary

Deep learning-based computed tomography (CT) image standardization significantly improved automated liver segmentation performance across various reconstruction methods. This technique enhances the accuracy and reliability of hepatic segmentation for better clinical applications.

Keywords:
Artificial intelligenceAutomated segmentationImage conversionQuality controlReproducibility

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Automated hepatic segmentation in computed tomography (CT) is crucial for liver disease assessment.
  • Performance of deep learning models for segmentation can be affected by variations in CT image reconstruction methods.

Purpose of the Study:

  • To evaluate if deep learning-based CT image standardization improves automated hepatic segmentation performance.
  • To assess the impact of standardization on segmentation accuracy across diverse CT reconstruction techniques.

Main Methods:

  • Developed a deep learning algorithm for CT image standardization using contrast-enhanced dual-energy CT data.
  • Utilized a commercial 2D U-NET based software for liver segmentation.
  • Compared segmentation performance (Dice Similarity Coefficient, volume difference ratio, Concordance Correlation Coefficient) before and after image standardization using statistical tests.

Main Results:

  • Standardized CT images significantly improved Dice Similarity Coefficients for liver segmentation compared to original images (93.16%-96.74% vs. 5.40%-91.27%).
  • Image standardization led to a significant decrease in the difference ratio of liver volume (1.99%-4.41% vs. 9.84%-91.37%).
  • Concordance Correlation Coefficients showed improved agreement between segmented and ground-truth liver volumes after standardization (0.990-0.998 vs. -0.006-0.964).

Conclusions:

  • Deep learning-based CT image standardization effectively enhances automated hepatic segmentation accuracy.
  • This standardization approach shows potential for improving the generalizability of segmentation networks across different CT reconstruction methods.