Boundary mapping through manifold learning for connectivity-based cortical parcellation
Arslan S, Parisot S, Rueckert D (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: Springer Verlag
Book Volume: 9900 LNCS
Pages Range: 115-122
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Athens, GRC
ISBN: 9783319467191
DOI: 10.1007/978-3-319-46720-7_14
Abstract
The study of the human connectome is becoming more popular due to its potential to reveal the brain function and structure. A critical step in connectome analysis is to parcellate the cortex into coherent regions that can be used to build graphical models of connectivity. Computing an optimal parcellation is of great importance,as this stage can affect the performance of the subsequent analysis. To this end,we propose a new parcellation method driven by structural connectivity estimated from diffusion MRI. We learn a manifold from the local connectivity properties of an individual subject and identify parcellation boundaries as points in this low-dimensional embedding where the connectivity patterns change. We compute spatially contiguous and non-overlapping parcels from these boundaries after projecting them back to the native cortical surface. Our experiments with a set of 100 subjects show that the proposed method can produce parcels with distinct patterns of connectivity and a higher degree of homogeneity at varying resolutions compared to the state-of-the-art methods,hence can potentially provide a more reliable set of network nodes for connectome analysis.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Arslan, S., Parisot, S., & Rueckert, D. (2016). Boundary mapping through manifold learning for connectivity-based cortical parcellation. In Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 115-122). Athens, GRC: Springer Verlag.
MLA:
Arslan, Salim, Sarah Parisot, and Daniel Rueckert. "Boundary mapping through manifold learning for connectivity-based cortical parcellation." Proceedings of the 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, GRC Ed. Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Springer Verlag, 2016. 115-122.
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