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

17th International Conference on the Use of Computers in Radiation Therapy

物理学;计算机科学

Moores, M.^1,3 ; Hargrave, C.^1,2,3 ; Harden, F.^1,3 ; Mengersen, K.^1,3

Queensland University of Technology, Brisbane QLD 4000, Australia^1

Radiation Oncology Mater Centre, Queensland Health, South Brisbane QLD 4101, Australia^2

Institute of Health and Biomedical Innovation, Kelvin Grove QLD 4059, Australia^3

关键词: Bayesian statistical model;    Cone-beam computed tomography;    Hidden Markov random field model;    Hidden Markov random fields;    Image guided radiotherapy;    Iterated conditional modes;    Misclassification rates;    Similarity coefficients;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/489/1/012076/pdf
DOI  :  10.1088/1742-6596/489/1/012076

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

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.