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dc.contributor.authorKieselmann, JP
dc.contributor.authorKamerling, CP
dc.contributor.authorBurgos, N
dc.contributor.authorMenten, MJ
dc.contributor.authorFuller, CD
dc.contributor.authorNill, S
dc.contributor.authorCardoso, MJ
dc.contributor.authorOelfke, U
dc.date.accessioned2018-06-13T13:54:36Z
dc.date.issued2018-07-11
dc.identifier.citationPhysics in medicine and biology, 2018, 63 (14), pp. 145007 - ?
dc.identifier.issn0031-9155
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/1862
dc.identifier.eissn1361-6560
dc.identifier.doi10.1088/1361-6560/aacb65
dc.description.abstractOwing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously published results and within the range of the IOV. While dosimetric differences could be as large as 23% of the clinical goal, treatment plans fulfilled all imposed clinical goals for the gold standard OARs. Correlations between geometric and dosimetric measures were low with R 2   <  0.5. The geometric accuracy and the ability to achieve clinically acceptable treatment plans indicate the suitability of using atlas-based contours for RT treatment planning purposes. The low correlations between geometric and dosimetric measures suggest that geometric measures alone are not sufficient to predict the dosimetric impact of segmentation inaccuracies on treatment planning for the data utilised in this study.
dc.formatElectronic
dc.format.extent145007 - ?
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectHumans
dc.subjectHead and Neck Neoplasms
dc.subjectObserver Variation
dc.subjectTomography, X-Ray Computed
dc.subjectMagnetic Resonance Imaging
dc.subjectRadiotherapy Dosage
dc.subjectRadiotherapy Planning, Computer-Assisted
dc.subjectRetrospective Studies
dc.subjectRadiometry
dc.subjectAlgorithms
dc.subjectRadiotherapy, Intensity-Modulated
dc.subjectOrgans at Risk
dc.titleGeometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region.
dc.typeJournal Article
dcterms.dateAccepted2018-06-08
rioxxterms.versionofrecord10.1088/1361-6560/aacb65
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2018-07-11
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfPhysics in medicine and biology
pubs.issue14
pubs.notesNo embargo
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Radiotherapy Physics Modelling
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Radiotherapy Physics Modelling
pubs.publication-statusPublished
pubs.volume63
pubs.embargo.termsNo embargo
icr.researchteamRadiotherapy Physics Modellingen_US
dc.contributor.icrauthorKieselmann, Jenniferen
dc.contributor.icrauthorNill, Simeonen
dc.contributor.icrauthorOelfke, Uween
dc.contributor.icrauthorMenten, Martinen


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