Patient-Specific Conditional Joint Models of Shape, Image Features and Clinical Indicators
Name
1907.07783.pdf
Description
Submitted version
Size
2.62 MB
Format
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Checksum (MD5)
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Author(s)
Egger, Bernhard
Schirmer, Markus
Dubost, Florian
Nardin, Marco J.
Rost, Natalia S.
Golland, Polina
Date Issued
October 2019
Journal
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Citation
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
Version
Original manuscript
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.
Description
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11767).
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1007/978-3-030-32251-9_11
