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dc.contributor.authorBeasley, William J
dc.contributor.authorMcWilliam, Alan
dc.contributor.authorAitkenhead, Adam H
dc.contributor.authorMackay, Ranald I
dc.contributor.authorRowbottom, Carl G
dc.date.accessioned2016-05-25T15:29:40Zen
dc.date.available2016-05-25T15:29:40Zen
dc.date.issued2016en
dc.identifier.citationThe suitability of common metrics for assessing parotid and larynx autosegmentation accuracy. 2016, 17 (2):5889 J Appl Clin Med Physen
dc.identifier.issn1526-9914en
dc.identifier.pmid27074471en
dc.identifier.urihttp://hdl.handle.net/10541/610753en
dc.description.abstractContouring structures in the head and neck is time-consuming, and automatic seg-mentation is an important part of an adaptive radiotherapy workflow. Geometric accuracy of automatic segmentation algorithms has been widely reported, but there is no consensus as to which metrics provide clinically meaningful results. This study investigated whether geometric accuracy (as quantified by several commonly used metrics) was associated with dosimetric differences for the parotid and larynx, comparing automatically generated contours against manually drawn ground truth contours. This enabled the suitability of different commonly used metrics to be assessed for measuring automatic segmentation accuracy of the parotid and larynx. Parotid and larynx structures for 10 head and neck patients were outlined by five clinicians to create ground truth structures. An automatic segmentation algorithm was used to create automatically generated normal structures, which were then used to create volumetric-modulated arc therapy plans. The mean doses to the automatically generated structures were compared with those of the corresponding ground truth structures, and the relative difference in mean dose was calculated for each structure. It was found that this difference did not correlate with the geometric accuracy provided by several metrics, notably the Dice similarity coefficient, which is a commonly used measure of spatial overlap. Surface-based metrics provided stronger correlation and are, therefore, more suitable for assessing automatic seg-mentation of the parotid and larynx.
dc.language.isoenen
dc.rightsArchived with thanks to Journal of applied clinical medical physics / American College of Medical Physicsen
dc.titleThe suitability of common metrics for assessing parotid and larynx autosegmentation accuracy.en
dc.typeArticleen
dc.contributor.departmentUniversity of Manchester; The Christie NHS Foundation Trusten
dc.identifier.journalJournal of Applied Clinical Medical Physicsen
html.description.abstractContouring structures in the head and neck is time-consuming, and automatic seg-mentation is an important part of an adaptive radiotherapy workflow. Geometric accuracy of automatic segmentation algorithms has been widely reported, but there is no consensus as to which metrics provide clinically meaningful results. This study investigated whether geometric accuracy (as quantified by several commonly used metrics) was associated with dosimetric differences for the parotid and larynx, comparing automatically generated contours against manually drawn ground truth contours. This enabled the suitability of different commonly used metrics to be assessed for measuring automatic segmentation accuracy of the parotid and larynx. Parotid and larynx structures for 10 head and neck patients were outlined by five clinicians to create ground truth structures. An automatic segmentation algorithm was used to create automatically generated normal structures, which were then used to create volumetric-modulated arc therapy plans. The mean doses to the automatically generated structures were compared with those of the corresponding ground truth structures, and the relative difference in mean dose was calculated for each structure. It was found that this difference did not correlate with the geometric accuracy provided by several metrics, notably the Dice similarity coefficient, which is a commonly used measure of spatial overlap. Surface-based metrics provided stronger correlation and are, therefore, more suitable for assessing automatic seg-mentation of the parotid and larynx.


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