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

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 source...

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Bibliographic Details
Published in:Journal of physics. Conference series 2014-01, Vol.489 (1), p.12076-4
Main Authors: Moores, M, Hargrave, C, Harden, F, Mengersen, K
Format: Article
Language:English
Subjects:
Accuracy
Adipose tissue
Breast
Computed tomography
Density
Electron density
Fields (mathematics)
Image analysis
Image segmentation
Labels
Lattice vibration
Medical imaging
Muscles
Organs
Physics
Radiation therapy
Radiotherapy
Segmentation
Statistical analysis
Statistical models
Three dimensional models
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/489/1/012076