Vision 20/20: Perspectives on automated image segmentation for radiotherapy. Issue 5 (24th April 2014)

• Record Type: Journal Article
• Authors: Sharp, Gregory; Fritscher, Karl D.; Pekar, Vladimir; Peroni, Marta; Shusharina, Nadya; Veeraraghavan, Harini; Yang, Jinzhong
• Is Part Of: Medical physics. Volume 41:Issue 5(2014)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-04-24
• Subjects: Radiation therapy equipment -- Segmentation; biological organs -- image segmentation -- medical image processing -- radiation therapy; segmentation -- radiation therapy -- image processing; Radiation therapy -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; Medical imaging -- Medical image segmentation -- Computed tomography -- Anatomy -- Cancer -- Radiation therapy -- Magnetic resonance imaging -- Databases -- Dosimetry -- Medical image contrast; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4871620
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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.