Abstract
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.
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Notes
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Scalismo - A Scalable Image Analysis and Shape Modeling Software Framework https://github.com/unibas-gravis/scalismo.
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Aknowledgments
This research was funded by SNSF P2BSP2_178643, NIH NIBIB NAC P41EB015902, NIH NINDS R01NS086905, Horizon2020 753896, De Drie Lichten 24/18, ZonMw 104003005, Wistron Corporation, AWS, SIP and NVIDIA.
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Egger, B., Schirmer, M.D., Dubost, F., Nardin, M.J., Rost, N.S., Golland, P. (2019). Patient-Specific Conditional Joint Models of Shape, Image Features and Clinical Indicators. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_11
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