Barrio: Customizable Spatial Neighborhood Analysis and Comparison for Nanoscale Brain Structures

Information

• Publication Type: Journal Paper with Conference Talk
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: June 2022
• Journal: Computer Graphics Forum
• Volume: 41
• Open Access: yes
• Event: Proceedings Eurographics/IEEE Symposium on Visualization, Eurovis 2022
• Call for Papers: Call for Paper
• Conference date: 11. November 2021 – 15. June 2022

Abstract

High-resolution electron microscopy imaging allows neuroscientists to reconstruct not just entire cells but individual cell sub-structures (i.e., cell organelles) as well. Based on these data, scientists hope to get a better understanding of brain function and development through detailed analysis of local organelle neighborhoods. In-depth analyses require efficient and scalable comparison of a varying number of cell organelles, ranging from two to hundreds of local spatial neighborhoods. Scientists need to be able to analyze the 3D morphologies of organelles, their spatial distributions and distances, and their spatial correlations. We have designed Barrio as a configurable framework that scientists can adjust to their preferred workflow, visualizations, and supported user interactions for their specific tasks and domain questions. Furthermore, Barrio provides a scalable comparative visualization approach for spatial neighborhoods that automatically adjusts visualizations based on the number of structures to be compared. Barrio supports small multiples of spatial 3D views as well as abstract quantitative views, and arranges them in linked and juxtaposed views. To adapt to new domain-specific analysis scenarios, we allow the definition of individualized visualizations and their parameters for each analysis session. We present an in-depth case study for mitochondria analysis in neuronal tissue and demonstrate the usefulness of Barrio in a qualitative user study with neuroscientists.

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BibTeX

@article{Troidl_2022,
  title =      "Barrio: Customizable Spatial Neighborhood Analysis and
               Comparison for Nanoscale Brain Structures",
  author =     "Troidl Jakob and Corrado Cali and Eduard Gr\"{o}ller and
               Hanspeter Pfister and Markus Hadwiger and Johanna Beyer",
  year =       "2022",
  abstract =   "High-resolution electron microscopy imaging allows
               neuroscientists to reconstruct not just entire cells but
               individual cell sub-structures (i.e., cell organelles) as
               well. Based on these data, scientists hope to get a better
               understanding of brain function and development through
               detailed analysis of local organelle neighborhoods. In-depth
               analyses require efficient and scalable comparison of a
               varying number of cell organelles, ranging from two to
               hundreds of local spatial neighborhoods. Scientists need to
               be able to analyze the 3D morphologies of organelles, their
               spatial distributions and distances, and their spatial
               correlations. We have designed Barrio as a configurable
               framework that scientists can adjust to their preferred
               workflow, visualizations, and supported user interactions
               for their specific tasks and domain questions. Furthermore,
               Barrio provides a scalable comparative visualization
               approach for spatial neighborhoods that automatically
               adjusts visualizations based on the number of structures to
               be compared. Barrio supports small multiples of spatial 3D
               views as well as abstract quantitative views, and arranges
               them in linked and juxtaposed views. To adapt to new
               domain-specific analysis scenarios, we allow the definition
               of individualized visualizations and their parameters for
               each analysis session. We present an in-depth case study for
               mitochondria analysis in neuronal tissue and demonstrate the
               usefulness of Barrio in a qualitative user study with
               neuroscientists.",
  month =      jun,
  journal =    "Computer Graphics Forum",
  volume =     "41",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2022/Troidl_2022/",
}