Estimation of Brain Network Atlases using Diffusive-Shrinking Graphs: Application to Developing Brains

Islem Rekik (Lead / Corresponding author), Gang Li, Weili Lin, Dinggang Shen (Lead / Corresponding author)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)
119 Downloads (Pure)

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 languageEnglish
Title of host publicationInformation Processing in Medical Imaging
Subtitle of host publication25th International Conference, IPMI 2017 Boone, NC, USA, June 25-30, 2017, Proceedings
EditorsMarc Niethammer, Martin Styner, Stephen Aylward, Hongtu Zhu, Ipek Oguz, Pew-Thian Yap, Dinggang Shen
Place of PublicationSwitzerland
PublisherSpringer
Pages385-397
Number of pages13
ISBN (Electronic)9783319590509
ISBN (Print)9783319590493
DOIs
Publication statusPublished - 2017
EventInformation Processing in Medical Imaging - Appalachian State University, Boone, North Carolina, United States
Duration: 25 Jun 201730 Jun 2017
http://www.ipmi2017.org/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10265
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInformation Processing in Medical Imaging
Abbreviated titleIPMI
CountryUnited States
CityBoone, North Carolina
Period25/06/1730/06/17
Internet address

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Keywords

  • Cluster analysis
  • computer vision
  • computer graphics
  • computational geometry
  • topology
  • artificial intelligence
  • image processing
  • image segmentation
  • image reconstruction

Cite this

Rekik, I., Li, G., Lin, W., & Shen, D. (2017). Estimation of Brain Network Atlases using Diffusive-Shrinking Graphs: Application to Developing Brains. In M. Niethammer, M. Styner, S. Aylward, H. Zhu, I. Oguz, P-T. Yap, & D. Shen (Eds.), Information Processing in Medical Imaging: 25th International Conference, IPMI 2017 Boone, NC, USA, June 25-30, 2017, Proceedings (pp. 385-397). (Lecture Notes in Computer Science ; Vol. 10265). Springer . https://doi.org/10.1007/978-3-319-59050-9_31