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Vision 20/20: Perspectives on Automated Image Segmentation for Radiotherapy

Institution:
1Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA.
2Philips Healthcare, Markham, Ontario, Canada.
3Center for Proton Therapy, Paul Scherrer Institut, Switzerland.
4Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA.
5Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
6Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, USA.
Publisher:
American Institute of Physics
Publication Date:
May-2014
Journal:
Med Phys
Volume Number:
41
Issue Number:
5
Pages:
050902
Citation:
Med Phys. 2014 May;41(5):050902.
PubMed ID:
24784366
PMCID:
PMC4000389
Keywords:
egmentation, radiation therapy, image processing
Appears in Collections:
NA-MIC
Sponsors:
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Sharp G., Fritscher K.D., Pekar V., Peroni M., Shusharina N., Veeraraghavan H., Yang J. Vision 20/20: Perspectives on Automated Image Segmentation for Radiotherapy. Med Phys. 2014 May;41(5):050902. PMID: 24784366. PMCID: PMC4000389.
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Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra- and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition. In this paper, the authors review the current autosegmentation methods particularly relevant for applications in RT. The authors outline the methods' strengths and limitations and propose strategies that could lead to wider acceptance of autosegmentation in routine clinical practice. The authors conclude that currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment. Modern hardware platforms including GPUs allow most of the autosegmentation tasks to be done in a range of a few minutes. In the nearest future, improvements in CT-based autosegmentation tools will be achieved through standardization of imaging and contouring protocols. In the longer term, the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology.

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