Informative Priors for Segmentation of Medical Images
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This document summarizes two Bayesian approaches to medical image segmentation - k-means with posterior diffusion and hidden Markov random field models. It discusses potential extensions, such as using an external field to incorporate organ size and position variability, or a hybrid level set model. The conclusions discuss references for further information on these methods.
![Motivation Method 1 Method 2 Extensions Conclusion
external field II
Prior probabilities αi (zi ) for each pixel can be generated by
simulation, based on:
geometry of each organ, from the treatment plan
variability in size and position, from published studies
Axis prostate seminal vesicles
Ant-Post x = 0.1, sd = 4.1 mm x = 1.2, sd = 7.3 mm
Sup-Inf x = −0.5, sd = 2.9 mm x = −0.7, sd = 4.5 mm
Left-Right x = 0.2, sd = 0.9 mm x = −0.9, sd = 1.9 mm
Table: Mean x and standard deviation sd of observed [5] variability in
position, along three axes: anteroposterior (Ant-Post); superoinferior
(Sup-Inf); & lateral (Left-Right) relative to the patient.](https://image.slidesharecdn.com/springbayes2011-120216205121-phpapp01/85/Informative-Priors-for-Segmentation-of-Medical-Images-14-320.jpg)
![Motivation Method 1 Method 2 Extensions Conclusion
hybrid model
Chen & Metaxas [6, 7] define the object boundary implicitly as the
zero level set of a cost function:
∂φi φi φi
= λ1 M i + λ 2 Pi · − (λ2 Pi + λ3 ) ·
∂t φi φi
(5)
where:
Mi is the inflation force (total gradient magnitude)
Pi is the local image force at each pixel
(probability of pixel j belonging to object i)
non-overlapping constraint
φi
· φi is the local curvature
(surface smoothness constraint)](https://image.slidesharecdn.com/springbayes2011-120216205121-phpapp01/85/Informative-Priors-for-Segmentation-of-Medical-Images-16-320.jpg)
Informative Priors for Segmentation of Medical Images
- 1. Motivation Method 1 Method 2 Extensions Conclusion Informative Priors for Segmentation of Medical Images Matt Moores1,2 , Cathy Hargrave3 , Fiona Harden2 & Kerrie Mengersen1 1 Discipline of Mathematical Sciences, Queensland University of Technology 2 Discipline of Medical Radiation Sciences, Queensland University of Technology 3 Radiation Oncology Mater Centre, Queensland Health Bayes on the Beach, 2011
- 2. Motivation Method 1 Method 2 Extensions Conclusion Outline 1 Motivation Cone-Beam Computed Tomography 2 Method 1 k-means with posterior diffusion 3 Method 2 hidden Markov random field 4 Extensions 5 Conclusion
- 3. Motivation Method 1 Method 2 Extensions Conclusion X-Ray Computed Tomography (a) Fan-Beam CT (b) Cone-Beam CT
- 4. Motivation Method 1 Method 2 Extensions Conclusion Distribution of Pixel Intensity 15000 15000 10000 10000 Frequency Frequency 5000 5000 0 −1000 −800 −600 −400 −200 0 200 0 −1000 −800 −600 −400 −200 0 200 Hounsfield unit pixel intensity (a) Fan-Beam CT (b) Cone-Beam CT
- 5. Motivation Method 1 Method 2 Extensions Conclusion itkBayesianClassifierImageFilter 1 estimate µ using k-means 2 estimate σ 2 for each cluster (mixing proportions are assumed equal) 3 create a matrix y∗ : for each pixel yi and each cluster Ck ∼ N(µk , σk ), yik = p(yi |µk , σk ) 6 5 4 classify each pixel yi according to the largest value of yik
- 6. Motivation Method 1 Method 2 Extensions Conclusion itkBayesianClassifierImageFilter 1 estimate µ using k-means 1 select initial values for µ 2 assign each pixel y to the nearest µk 3 recalculate each µk by averaging over the members of k 4 repeat steps 2 & 3 until none of the pixel assignments change 2 estimate σ 2 for each cluster (mixing proportions are assumed equal) 3 create a matrix y∗ : for each pixel yi and each cluster Ck ∼ N(µk , σk ), yik = p(yi |µk , σk ) 6 5 4 classify each pixel yi according to the largest value of yik
- 7. Motivation Method 1 Method 2 Extensions Conclusion Result (k-means GMM) (a) Fan-Beam CT (b) Cone-Beam CT
- 8. Motivation Method 1 Method 2 Extensions Conclusion Prior 4 matrix pik representing the prior probability of pixel i belonging to cluster k then pixel classification is based on the posterior pik × yik but: this has no effect on the number of clusters, nor on their parameters µk and σk can’t use the posterior from one classification as the prior for another, unless the clusters are the same
- 9. Motivation Method 1 Method 2 Extensions Conclusion Result (with prior) (a) Prior (b) Likelihood (c) Posterior
- 10. Motivation Method 1 Method 2 Extensions Conclusion Result (with diffusion) (a) 5 iterations (b) 10 iterations (c) 50 iterations (d) 1000 iterations
- 11. Motivation Method 1 Method 2 Extensions Conclusion hidden Markov random field Joint distribution of observed intensities y and unobserved labels z: p(y, z|µ, τ ) ∝ p(y|µ, τ , z)p(z) (1) 1 yi |µj , τj , zi = j ∼ N µj , (2) τj N p(z) = C(β)−1 exp αi (zi ) + β wij f (zi , zj ) (3) i=1 i∼j simple Potts model (without external field): p(z) = C(β)−1 exp β I(zi = zj ) (4) i∼j
- 12. Motivation Method 1 Method 2 Extensions Conclusion informative prior for µ and τ 200 200 0 0 −200 −200 Hounsfield unit pixel intensity −400 −400 −600 −600 −800 −800 −1000 −1000 0 1 2 3 4 0 1 2 3 4 Electron Density Electron Density (a) Fan-Beam CT (b) Cone-Beam CT
- 13. Motivation Method 1 Method 2 Extensions Conclusion external field N In equation (3) earlier, the term exp i=1 αi (zi ) defines an external field. Figure: manual contours of the organs of interest.
- 14. Motivation Method 1 Method 2 Extensions Conclusion external field II Prior probabilities αi (zi ) for each pixel can be generated by simulation, based on: geometry of each organ, from the treatment plan variability in size and position, from published studies Axis prostate seminal vesicles Ant-Post x = 0.1, sd = 4.1 mm x = 1.2, sd = 7.3 mm Sup-Inf x = −0.5, sd = 2.9 mm x = −0.7, sd = 4.5 mm Left-Right x = 0.2, sd = 0.9 mm x = −0.9, sd = 1.9 mm Table: Mean x and standard deviation sd of observed [5] variability in position, along three axes: anteroposterior (Ant-Post); superoinferior (Sup-Inf); & lateral (Left-Right) relative to the patient.
- 15. Motivation Method 1 Method 2 Extensions Conclusion Jacobian matrix 2 Figure: discrete Laplacian
- 16. Motivation Method 1 Method 2 Extensions Conclusion hybrid model Chen & Metaxas [6, 7] define the object boundary implicitly as the zero level set of a cost function: ∂φi φi φi = λ1 M i + λ 2 Pi · − (λ2 Pi + λ3 ) · ∂t φi φi (5) where: Mi is the inflation force (total gradient magnitude) Pi is the local image force at each pixel (probability of pixel j belonging to object i) non-overlapping constraint φi · φi is the local curvature (surface smoothness constraint)
- 17. Motivation Method 1 Method 2 Extensions Conclusion Summary Two Bayesian approaches to medical image segmentation: k-means with posterior diffusion (itkBayesianClassifierImageFilter) hidden Markov random field (PyMCMC) Potential extensions to Potts MRF: external field defined by size and position of objects hybrid Level Set model
- 18. Motivation Method 1 Method 2 Extensions Conclusion References I P. Teo, G. Sapiro and B. Wandell (1997) Creating connected representations of cortical gray matter for functional MRI visualization. IEEE Trans. Med. Imag. 16: 852-863. J. Melonakos, K. Krishnan and A. Tannenbaum (2006) An ITK Filter for Bayesian Segmentation: itkBayesianClassifierImageFilter The Insight Journal http://hdl.handle.net/1926/160 Strickland, C. M., Denham, R. J., Alston, C. L., & Mengersen, K. L. (2011) PyMCMC : a Python package for Bayesian Estimation using Markov chain Monte Carlo. Journal of Statistical Software (In Press) C. Alston, K. Mengersen, C. Robert, J. Thompson, P. Littlefield, D. Perry and A. Ball (2007) Bayesian mixture models in a longitudinal setting for analysing sheep CAT scan images. Computational Statistics & Data Analysis 51(9): 4282-4296.
- 19. Motivation Method 1 Method 2 Extensions Conclusion References II S.J. Frank, L. Dong, R. J. Kudchadker, R. De Crevoisier, A. K. Lee, R. Cheung, S. Choi, J. O’Daniel, S. L. Tucker, H. Wang, et al. (2008) Quantification of Prostate and Seminal Vesicle Interfraction Variation During IMRT. International Journal of Radiation Oncology*Biology*Physics 71(3): 813-820. T. Chen and D. Metaxas (2005) A hybrid framework for 3D medical image segmentation. Medical Image Analysis 9(6): 547-565. T. Chen, S. Kim, J. Zhou, D. Metaxas, G. Rajagopal & N. Yue (2009) 3D Meshless Prostate Segmentation and Registration in Image Guided Radiotherapy. In Proceedings of MICCAI 43-50. P. Th´venaz, T. Blu & M. Unser (2000) Interpolation Revisited. e IEEE Trans. Medical Imaging 19(7): 739–758.
- 20. Motivation Method 1 Method 2 Extensions Conclusion Acknowledgements Bayesian Research & Applications Group at QUT Radiation Oncology Mater Centre: Emmanuel Baveas Rebecca Owen Timothy Deegan Steven Sylvander John Baines Dr. Michael Poulsen