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

Leon Lenchik, Laura Heacock, Ashley A. Weaver, Robert D Boutin, Tessa S. Cook, Jason Itri, Christopher G. Filippi, Rao P. Gullapalli, James Lee, Marianna Zagurovskaya, Tara Retson, Kendra Godwin, Joey Nicholson, Ponnada A. Narayana

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Rationale and Objectives: The automated segmentation of organs and tissues throughout the body using computed tomography and magnetic resonance imaging has been rapidly increasing. Research into many medical conditions has benefited greatly from these approaches by allowing the development of more rapid and reproducible quantitative imaging markers. These markers have been used to help diagnose disease, determine prognosis, select patients for therapy, and follow responses to therapy. Because some of these tools are now transitioning from research environments to clinical practice, it is important for radiologists to become familiar with various methods used for automated segmentation. Materials and Methods: The Radiology Research Alliance of the Association of University Radiologists convened an Automated Segmentation Task Force to conduct a systematic review of the peer-reviewed literature on this topic. Results: The systematic review presented here includes 408 studies and discusses various approaches to automated segmentation using computed tomography and magnetic resonance imaging for neurologic, thoracic, abdominal, musculoskeletal, and breast imaging applications. Conclusion: These insights should help prepare radiologists to better evaluate automated segmentation tools and apply them not only to research, but eventually to clinical practice.

Original languageEnglish (US)
JournalAcademic radiology
StateAccepted/In press - Jan 1 2019


  • CT
  • Machine learning
  • MRI
  • Quantitative imaging
  • Segmentation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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