TY - JOUR
T1 - Scalable Interactive Visualization for Connectomics
AU - Haehn, Daniel
AU - Hoffer, John
AU - Matejek, Brian
AU - Suissa-Peleg, Adi
AU - Al-Awami, Ali K.
AU - Kamentsky, Lee
AU - Gonda, Felix
AU - Meng, Eagon
AU - Zhang, William
AU - Schalek, Richard
AU - Wilson, Alyssa
AU - Parag, Toufiq
AU - Beyer, Johanna
AU - Kaynig, Verena
AU - Jones, Thouis
AU - Tompkin, James
AU - Hadwiger, Markus
AU - Lichtman, Jeff
AU - Pfister, Hanspeter
N1 - KAUST Repository Item: Exported on 2021-04-06
Acknowledged KAUST grant number(s): OSR-2015-CCF-2533-01
Acknowledgements: This research is supported in part by NSF grants IIS-1447344 and IIS-1607800, by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DoI/IBC) contract number D16PC00002, and by the King Abdullah University of Science and Technology (KAUST) under Award No. OSR-2015-CCF-2533-01.
PY - 2017/8/28
Y1 - 2017/8/28
N2 - Connectomics has recently begun to image brain tissue at nanometer resolution, which produces petabytes of data. This data must be aligned, labeled, proofread, and formed into graphs, and each step of this process requires visualization for human verification. As such, we present the BUTTERFLY middleware, a scalable platform that can handle massive data for interactive visualization in connectomics. Our platform outputs image and geometry data suitable for hardware-accelerated rendering, and abstracts low-level data wrangling to enable faster development of new visualizations. We demonstrate scalability and extendability with a series of open source Web-based applications for every step of the typical connectomics workflow: data management and storage, informative queries, 2D and 3D visualizations, interactive editing, and graph-based analysis. We report design choices for all developed applications and describe typical scenarios of isolated and combined use in everyday connectomics research. In addition, we measure and optimize rendering throughput—from storage to display—in quantitative experiments. Finally, we share insights, experiences, and recommendations for creating an open source data management and interactive visualization platform for connectomics.
AB - Connectomics has recently begun to image brain tissue at nanometer resolution, which produces petabytes of data. This data must be aligned, labeled, proofread, and formed into graphs, and each step of this process requires visualization for human verification. As such, we present the BUTTERFLY middleware, a scalable platform that can handle massive data for interactive visualization in connectomics. Our platform outputs image and geometry data suitable for hardware-accelerated rendering, and abstracts low-level data wrangling to enable faster development of new visualizations. We demonstrate scalability and extendability with a series of open source Web-based applications for every step of the typical connectomics workflow: data management and storage, informative queries, 2D and 3D visualizations, interactive editing, and graph-based analysis. We report design choices for all developed applications and describe typical scenarios of isolated and combined use in everyday connectomics research. In addition, we measure and optimize rendering throughput—from storage to display—in quantitative experiments. Finally, we share insights, experiences, and recommendations for creating an open source data management and interactive visualization platform for connectomics.
UR - http://hdl.handle.net/10754/668547
UR - http://www.mdpi.com/2227-9709/4/3/29
U2 - 10.3390/informatics4030029
DO - 10.3390/informatics4030029
M3 - Article
SN - 2227-9709
VL - 4
SP - 29
JO - Informatics
JF - Informatics
IS - 3
ER -