García, H.F., Álvarez, M.A. orcid.org/0000-0002-8980-4472 and Orozco, Á.A. (2017) Bayesian optimization for fitting 3D morphable models of brain structures. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2016. 21st Iberoamerican Congress, CIARP 2016, 08/11/2016-11/11/2016, Lima, Peru. Lecture Notes in Computer Science, 10125 . Springer Verlag , pp. 291-299. ISBN 9783319522760
Abstract
Localize target areas in deep brain stimulation is a difficult task, due to the shape variability that brain structures exhibit between patients. The main problem in this process is that the fitting procedure is carried out by a registration method that lacks of accuracy. In this paper we proposed a novel method for 3D brain structure fitting based on Bayesian optimization. We use a morphable model in order to capture the shape variability in a given set of brain structures. Then from the trained model, we perform a Bayesian optimization task with the aim to find the best shape parameters that deform the trained model, and fits accurately to a given brain structure. The experimental results show that by using an optimization framework based on Bayesian optimization, the model performs an accurate fitting over cortical brain structures (thalamus, amygdala and ventricle) in comparison with common fitting methods, such as iterative closest point.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Springer. This is an author produced version of a paper subsequently published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Bayesian optimization; 3D brain structures; Shape fitting; Morphable model |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 May 2017 10:25 |
Last Modified: | 26 Mar 2018 15:28 |
Published Version: | https://doi.org/10.1007/978-3-319-52277-7_36 |
Status: | Published |
Publisher: | Springer Verlag |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-52277-7_36 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:116581 |