Segmentation of cone-beam CT using a hidden Markov random field with informative priors
Moores, Matthew T., Hargrave, Catriona Elizabeth, Harden, Fiona, & Mengersen, Kerrie (2013) Segmentation of cone-beam CT using a hidden Markov random field with informative priors. In ICCR 2013 International Conference on the Use of Computers in Radiation Therapy, 6-9 May 2013, Melbourne Convention and Exhibition Centre, Australia. (Unpublished)
Purpose: Flat-detector, 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.
Methods: The rich sources of prior information in IGRT are incorporated into a hidden Markov random field (MRF) 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 (OAR). The voxel labels are estimated using the iterated conditional modes (ICM) algorithm.
Results: The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom (CIRS, Inc. model 062). The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue.
Conclusions: 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.
Impact and interest:
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|Item Type:||Conference Item (Poster)|
|Keywords:||Medical image analysis, Cone-beam computed tomography, Hidden Markov random field, Iterated conditional modes, External-beam radiotherapy|
|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 2013 Please consult the authors|
|Deposited On:||27 Aug 2013 01:51|
|Last Modified:||27 Aug 2013 01:51|
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