Patient-Specific Conditional Joint Models of Shape, Image Features and Clinical Indicators
Author(s)Egger, Bernhard; Schirmer, Markus; Dubost, Florian; Nardin, Marco J.; Rost, Natalia S.; Golland, Polina; ... Show more Show less
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We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis. The key idea is to employ a copula model to separate the joint dependency structure from the marginal distributions of variables of interest. This separation provides flexibility on the assumptions made during the modeling process. The proposed method can handle binary, discrete, ordinal and continuous variables. We demonstrate a simple and efficient way to include binary, discrete and ordinal variables into the modeling. We build Bayesian conditional models based on observed partial clinical indicators, features or shape based on Gaussian processes capturing the dependency structure. We apply the proposed method on a stroke dataset to jointly model the shape of the lateral ventricles, the spatial distribution of the white matter hyperintensity associated with periventricular white matter disease, and clinical indicators. The proposed method yields interpretable joint models for data exploration and patient-specific statistical shape models for medical image analysis.
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11767).
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Lecture Notes in Computer Science
Springer International Publishing
Egger, Bernhard et al. "Patient-Specific Conditional Joint Models of Shape, Image Features and Clinical Indicators." International Conference on Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science, 11767, Springer Nature, 2019, 93-101. © 2019 Springer Nature