@inproceedings{ffb89d990fdf43ba81cd0dbc0f0b23e6,
title = "Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms",
abstract = "We present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffeomorphisms as a kernel in a machine learning context. For simplicity, we choose the kernel Fischer Linear Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters in an Expectation-Maximization framework, we define model fidelity via the hinge loss of the decision function. The resulting algorithm optimizes the parameters of the LDDMM norm-inducing differential operator as a solution to a group-wise registration and classification problem. In practice, this may lead to a biology-aware registration, focusing its attention on the predictive task at hand such as identifying the effects of disease. We first tested our algorithm on a synthetic dataset, showing that our parameter selection improves registration quality and classification accuracy. We then tested the algorithm on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our Schizophrenia-Control predictive model showed significant improvement in ROC AUC compared to baseline parameters.",
keywords = "Expectation Maximization, Image registration, LDDMM, Machine learning, Metric learning, Subcortical shape",
author = "Ayagoz Mussabayeva and Alexey Kroshnin and Anvar Kurmukov and Yulia Denisova and Li Shen and Shan Cong and Lei Wang and Gutman, {Boris A.}",
note = "Funding Information: Acknowledgements. This work was funded in part by the Russian Science Foundation grant 17-11-01390. Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; International Workshop on Shape in Medical Imaging, ShapeMI 2018 held in conjunction with 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 ; Conference date: 20-09-2018 Through 20-09-2018",
year = "2018",
doi = "10.1007/978-3-030-04747-4_15",
language = "English (US)",
isbn = "9783030047467",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "160--168",
editor = "Herv{\'e} Lombaert and Beatriz Paniagua and Bernhard Egger and Marcel L{\"u}thi and Martin Reuter and Christian Wachinger",
booktitle = "Shape in Medical Imaging - International Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Proceedings",
}