Motivation Method 1 Method 2 Extensions Conclusion Informative Priors for Segmentation of Medical Images Matt Moores 1,2 , Cathy Hargrave 3 , Fiona Harden 2 & Kerrie Mengersen 1 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
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Informative Priors for Segmentation of Medical Images
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Informative Priors for Segmentationof Medical Images
Matt Moores1,2, Cathy Hargrave3, Fiona Harden2
& Kerrie Mengersen1
1 Discipline of Mathematical Sciences, Queensland University of Technology2 Discipline of Medical Radiation Sciences, Queensland University of Technology
3 Radiation Oncology Mater Centre, Queensland Health
Prior probabilities αi(zi) for each pixel can be generated bysimulation, 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 inposition, along three axes: anteroposterior (Ant-Post); superoinferior(Sup-Inf); & lateral (Left-Right) relative to the patient.
P. Teo, G. Sapiro and B. Wandell (1997) Creating connectedrepresentations of cortical gray matter for functional MRIvisualization. IEEE Trans. Med. Imag. 16: 852-863.
J. Melonakos, K. Krishnan and A. Tannenbaum (2006)An ITK Filter for Bayesian Segmentation:itkBayesianClassifierImageFilter The Insight Journalhttp://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 usingMarkov 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 longitudinalsetting for analysing sheep CAT scan images. ComputationalStatistics & Data Analysis 51(9): 4282-4296.
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 InterfractionVariation During IMRT. International Journal of RadiationOncology*Biology*Physics 71(3): 813-820.
T. Chen and D. Metaxas (2005) A hybrid framework for 3D medicalimage 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 inImage Guided Radiotherapy. In Proceedings of MICCAI 43-50.
P. Thevenaz, T. Blu & M. Unser (2000) Interpolation Revisited.IEEE Trans. Medical Imaging 19(7): 739–758.