Segmentation of cone-beam CT using a hidden Markov random field with informative priors
Data(s) |
2014
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Resumo |
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. |
Formato |
application/pdf |
Identificador | |
Publicador |
Institute of Physics Publishing Ltd. |
Relação |
http://eprints.qut.edu.au/63428/1/shortpaper21_rev201308.pdf DOI:10.1088/1742-6596/489/1/012076 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. |
Direitos |
Copyright 2014 The Author(s) 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 |
Fonte |
School of Clinical Sciences; Faculty of Health; Institute of Health and Biomedical Innovation; School of Mathematical Sciences; Science & Engineering Faculty |
Palavras-Chave | #010402 Biostatistics #Cone-beam computed tomography #Medical image analysis #Markov random field #Bayesian inference #Image-guided radiotherapy |
Tipo |
Journal Article |