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Related Concept Videos

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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Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective.

Stephanie Batista Niño1, Jorge Bernardino1,2, Inês Domingues1,3

  • 1Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal.

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Summary

Accurate liver segmentation in computed tomography (CT) scans is vital for oncology. This study compares AI and traditional methods, highlighting the need for accessible datasets and standardized metrics for improved clinical application.

Keywords:
artificial intelligencecomputed tomographyhepatic pathologiesliver segmentation

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Area of Science:

  • Medical Imaging
  • Radiology
  • Oncology

Background:

  • Computed tomography (CT) is essential for cancer diagnosis and treatment planning.
  • Accurate liver segmentation in CT scans is challenging due to image artifacts and soft tissue variations.
  • This is critical for precise identification of liver pathologies in oncology.

Purpose of the Study:

  • To compare the accuracy and efficiency of artificial intelligence (AI) algorithms versus traditional medical image processing techniques for liver segmentation in CT scans.
  • To evaluate the performance of these methods in assisting radiologists.

Main Methods:

  • Comparison of AI-based segmentation algorithms with traditional image processing techniques.
  • Evaluation of accuracy and efficiency metrics for liver segmentation in CT datasets.
  • Analysis of challenges including artifacts and varying tissue densities.

Main Results:

  • AI algorithms show promise in improving liver segmentation accuracy and efficiency.
  • Traditional methods face limitations in handling complex anatomical variations and artifacts.
  • The study identifies areas for improvement in current segmentation techniques.

Conclusions:

  • AI offers a potential advancement for liver segmentation in oncological CT imaging.
  • Limited public datasets and lack of standardized metrics hinder research progress.
  • Future work should focus on dataset accessibility, standardized evaluation, 3D techniques, and clinical integration.