Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators

@article{Egger2019PatientspecificCJ,
  title={Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators},
  author={B. Egger and M. Schirmer and F. Dubost and Marco J. Nardin and N. Rost and P. Golland},
  journal={Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2019},
  volume={11767},
  pages={
          93-101
        }
}
  • B. Egger, M. Schirmer, +3 authors P. Golland
  • Published 2019
  • Computer Science, Engineering, Medicine
  • Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
  • We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis. [...] Key Method 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.Expand Abstract

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 15 REFERENCES
    Gaussian Processes for Machine Learning
    • 7,389
    • PDF
    Extending the rank likelihood for semiparametric copula estimation
    • 213
    • PDF
    Posterior shape models
    • 36
    • PDF
    Gaussian Process Morphable Models
    • 76
    • PDF
    Semiparametric estimation in copula models
    • 260
    White matter hyperintensity volume is increased in small vessel stroke subtypes
    • 125
    • Highly Influential
    Semiparametric Principal Component Analysis
    • 23
    • PDF