Abstract
Many methods have been developed to spatially normalize a population of brain images for estimating a mean image as a populationaverage atlas. However, methods for deriving a network atlas from a set of brain networks sitting on a complex manifold are still absent. Learning how to average brain networks across subjects constitutes a key step in creating a reliable mean representation of a population of brain networks, which can be used to spot abnormal deviations from the healthy network atlas. In this work, we propose a novel network atlas estimation framework, which guarantees that the produced network atlas is clean (for tuning down noisy measurements) and well-centered (for being optimally close to all subjects and representing the individual traits of each subject in the population). Specifically, for a population of brain networks, we first build a tensor, where each of its frontal-views (i.e., frontal matrices) represents a connectivity network matrix of a single subject in the population. Then, we use tensor robust principal component analysis for jointly denoising all subjects’ networks through cleaving a sparse noisy network population tensor from a clean low-rank network tensor. Second, we build a graph where each node represents a frontal-view of the unfolded clean tensor (network), to leverage the local manifold structure of these networks when fusing them. Specifically, we progressively shrink the graph of networks towards the centered mean network atlas through non-linear diffusion along the local neighbors of each of its nodes. Our evaluation on the developing functional and morphological brain networks at 1, 3, 6, 9 and 12 months of age has showed a better centeredness of our network atlases, in comparison with the baseline network fusion method. Further cleaning of the population of networks produces even more centered atlases, especially for the noisy functional connectivity networks.
Original language | English |
---|---|
Title of host publication | Information Processing in Medical Imaging |
Subtitle of host publication | 25th International Conference, IPMI 2017 Boone, NC, USA, June 25-30, 2017, Proceedings |
Editors | Marc Niethammer, Martin Styner, Stephen Aylward, Hongtu Zhu, Ipek Oguz, Pew-Thian Yap, Dinggang Shen |
Place of Publication | Switzerland |
Publisher | Springer |
Pages | 385-397 |
Number of pages | 13 |
ISBN (Electronic) | 9783319590509 |
ISBN (Print) | 9783319590493 |
DOIs | |
Publication status | Published - 2017 |
Event | Information Processing in Medical Imaging - Appalachian State University, Boone, North Carolina, United States Duration: 25 Jun 2017 → 30 Jun 2017 http://www.ipmi2017.org/ |
Publication series
Name | Lecture Notes in Computer Science |
---|---|
Publisher | Springer |
Volume | 10265 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Information Processing in Medical Imaging |
---|---|
Abbreviated title | IPMI |
Country/Territory | United States |
City | Boone, North Carolina |
Period | 25/06/17 → 30/06/17 |
Internet address |
Keywords
- Cluster analysis
- computer vision
- computer graphics
- computational geometry
- topology
- artificial intelligence
- image processing
- image segmentation
- image reconstruction