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Assessment of fully-automated atlas-based segmentation of novel oral mucosal surface organ-at-risk.

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Date
2016-04
ICR Author
Dean, Jamie
Harrington, Kevin
Gulliford, Sarah
Author
Dean, JA
Welsh, LC
McQuaid, D
Wong, KH
Aleksic, A
Dunne, E
Islam, MR
Patel, A
Patel, P
Petkar, I
Phillips, I
Sham, J
Newbold, KL
Bhide, SA
Harrington, KJ
Gulliford, SL
Nutting, CM
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Type
Journal Article
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Abstract
Background and purpose Current oral mucositis normal tissue complication probability models, based on the dose distribution to the oral cavity volume, have suboptimal predictive power. Improving the delineation of the oral mucosa is likely to improve these models, but is resource intensive. We developed and evaluated fully-automated atlas-based segmentation (ABS) of a novel delineation technique for the oral mucosal surfaces.Material and methods An atlas of mucosal surface contours (MSC) consisting of 46 patients was developed. It was applied to an independent test cohort of 10 patients for whom manual segmentation of MSC structures, by three different clinicians, and conventional outlining of oral cavity contours (OCC), by an additional clinician, were also performed. Geometric comparisons were made using the dice similarity coefficient (DSC), validation index (VI) and Hausdorff distance (HD). Dosimetric comparisons were carried out using dose-volume histograms.Results The median difference, in the DSC and HD, between automated-manual comparisons and manual-manual comparisons were small and non-significant (-0.024; p=0.33 and -0.5; p=0.88, respectively). The median VI was 0.086. The maximum normalised volume difference between automated and manual MSC structures across all of the dose levels, averaged over the test cohort, was 8%. This difference reached approximately 28% when comparing automated MSC and OCC structures.Conclusions Fully-automated ABS of MSC is suitable for use in radiotherapy dose-response modelling.
URI
https://repository.icr.ac.uk/handle/internal/79
DOI
https://doi.org/10.1016/j.radonc.2016.02.022
Collections
  • Cancer Biology
  • Radiotherapy and Imaging
Subject
Mouth Mucosa
Humans
Head and Neck Neoplasms
Radiotherapy Dosage
Radiometry
Dose-Response Relationship, Radiation
Atlases as Topic
Organs at Risk
Research team
Clinical Academic Radiotherapy (Horwich)
Radiotherapy Physics Modelling
Targeted Therapy
Language
eng
Date accepted
2016-02-09
License start date
2016-04
Citation
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2016, 119 (1), pp. 166 - 171

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