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Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.

Leon Lenchik1, Laura Heacock2, Ashley A Weaver3

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Automated segmentation using medical imaging is advancing rapidly. This review helps radiologists understand these tools for improved diagnosis and treatment monitoring in clinical practice.

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Area of Science:

  • Radiology
  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine

Background:

  • Automated segmentation of organs and tissues using computed tomography (CT) and magnetic resonance imaging (MRI) is increasingly utilized in medical research.
  • Quantitative imaging markers derived from automated segmentation aid in disease diagnosis, prognosis, patient selection, and therapy response assessment.
  • As these technologies move from research to clinical settings, radiologists need to be familiar with automated segmentation methods.

Purpose of the Study:

  • To conduct a systematic review of the peer-reviewed literature on automated segmentation techniques in medical imaging.
  • To provide radiologists with an overview of various automated segmentation approaches and their applications.

Main Methods:

  • A systematic review of 408 peer-reviewed studies was performed.
  • The review focused on automated segmentation using CT and MRI.

Main Results:

  • The review covers applications in neurologic, thoracic, abdominal, musculoskeletal, and breast imaging.
  • Various automated segmentation approaches were discussed across the included studies.

Conclusions:

  • The findings aim to equip radiologists to evaluate automated segmentation tools effectively.
  • This knowledge will facilitate the application of automated segmentation in both research and clinical practice.