@inproceedings{discovery10060553,
           title = {Diffeomorphic brain shape modelling using Gauss-Newton optimisation},
            year = {2018},
          series = {Lecture Notes in Computer Science},
          volume = {11070},
          editor = {AF Frangi and JA Schnabel and C Davatzikos and C Alberola-L{\'o}pez},
         journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
           pages = {862--870},
         address = {Cham, Switzerland},
       booktitle = {Medical Image Computing and Computer Assisted Intervention - MICCAI 2018},
       publisher = {Springer},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
        abstract = {Shape modelling describes methods aimed at capturing the natural variability of shapes and commonly relies on probabilistic interpretations of dimensionality reduction techniques such as principal component analysis. Due to their computational complexity when dealing with dense deformation models such as diffeomorphisms, previous attempts have focused on explicitly reducing their dimension, diminishing de facto their flexibility and ability to model complex shapes such as brains. In this paper, we present a generative model of shape that allows the covariance structure of deformations to be captured without squashing their domain, resulting in better normalisation. An efficient inference scheme based on Gauss-Newton optimisation is used, which enables processing of 3D neuroimaging data. We trained this algorithm on segmented brains from the OASIS database, generating physiologically meaningful deformation trajectories. To prove the model's robustness, we applied it to unseen data, which resulted in equivalent fitting scores.},
          author = {Balbastre, Y and Brudfors, M and Bronik, K and Ashburner, J},
             url = {https://doi.org/10.1007/978-3-030-00928-1\%5f97},
            issn = {1611-3349}
}