Leon Lenchik1, Laura Heacock2, Ashley A Weaver3, Robert D Boutin4, Tessa S Cook5, Jason Itri6, Christopher G Filippi7, Rao P Gullapalli8, James Lee9, Marianna Zagurovskaya9, Tara Retson10, Kendra Godwin11, Joey Nicholson12, Ponnada A Narayana13. 1. Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157. Electronic address: llenchik@wakehealth.edu. 2. Department of Radiology, NYU Langone, New York, New York. 3. Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, North Carolina. 4. Department of Radiology, University of California Davis School of Medicine, Sacramento, California. 5. Department of Radiology, University of Pennsylvania, Philadelphia Pennsylvania. 6. Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157. 7. Department of Radiology, Donald and Barbara School of Medicine at Hofstra/Northwell, Lenox Hill Hospital, NY, New York. 8. Department of Radiology, University of Maryland School of Medicine, Baltimore, Maryland. 9. Department of Radiology, University of Kentucky, Lexington, Kentucky. 10. Department of Radiology, University of California San Diego, San Diego, California. 11. Medical Library, Memorial Sloan Kettering Cancer Center, New York, New York. 12. NYU Health Sciences Library, NYU School of Medicine, NYU Langone Health, New York, New York. 13. Department of Diagnostic and Interventional Imaging, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas.
Abstract
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.
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.
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