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Diffeomorphic brain shape modelling using Gauss-Newton optimisation

Balbastre, Y; Brudfors, M; Bronik, K; Ashburner, J; (2018) Diffeomorphic brain shape modelling using Gauss-Newton optimisation. In: Frangi, AF and Schnabel, JA and Davatzikos, C and Alberola-López, C, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. (pp. pp. 862-870). Springer: Cham, Switzerland. Green open access

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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.

Type: Proceedings paper
Title: Diffeomorphic brain shape modelling using Gauss-Newton optimisation
Event: MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, 16-20 September 2018, Granada, Spain
ISBN-13: 978-3-030-00927-4
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-00928-1_97
Publisher version: https://doi.org/10.1007/978-3-030-00928-1_97
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10060553
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