Diseased Region Detection of Longitudinal Knee MRI Data


Statistical analysis of longitudinal cartilage changes in osteoarthritis (OA) is of great importance and still a challenge in knee MRI data analysis. A major challenge is to establish a reliable correspondence across subjects within the same latent subpopulations. We develop a novel Gaussian hidden Markov model (GHMM) to establish spatial correspondence of cartilage thinning across both time and subjects within the same latent subpopulations and make statistical inference on the detection of diseased regions in each OA patient. A hidden Markov random filed (HMRF) is proposed to extract such latent subpopulation structure. The EM algorithm and pseudolikelihood method are both considered in making statistical inference. The proposed model can effectively detect diseased regions and present a localized analysis of longitudinal cartilage thickness within each latent subpopulation. Simulation studies and diseased regions detection of 2D thickness map extracted from full 3D longitudinal knee MRI Data for Pfizer Longitudinal Dataset are performed, which shows that our proposed model outperforms standard voxel-based analysis.

Information Processing in Medical Imaging - 23rd International Conference, IPMI 2013, Asilomar, CA, USA, June 28-July 3, 2013. Proceedings
Liang Shan
Liang Shan
Ph.D. in Computer Science

My research interests include image registration for images with pathologies and machine learning.

Marc Niethammer
Marc Niethammer
Professor of Computer Science

My research interests include image registration, image segmentation, shape analysis, machine learning, and biomedical applications.