Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Moores, Matthew T., Hargrave, Catriona Elizabeth, Harden, Fiona, & Mengersen, Kerrie (2014) Segmentation of cone-beam CT using a hidden Markov random field with informative priors. Journal of Physics : Conference Series, 489.

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Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2\%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

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3 citations in Scopus
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4 citations in Web of Science®

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ID Code: 63428
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Cone-beam computed tomography, Medical image analysis, Markov random field, Bayesian inference, Image-guided radiotherapy
DOI: 10.1088/1742-6596/489/1/012076
ISSN: 1742-6588
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Biostatistics (010402)
Divisions: Current > Schools > School of Clinical Sciences
Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 The Author(s)
Copyright Statement: Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution
of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Published under licence by IOP Publishing Ltd
Deposited On: 17 Oct 2013 04:42
Last Modified: 23 Jul 2014 14:12

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