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Sampling and Estimation of Pairwise Similarity in Spatio-Temporal Data Based on Neural Networks

Scalable Interactive Visualization for Connectomics

Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Harvard Brain Science Center, Harvard University, Cambridge, MA 02138, USA
Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
Computer Science Department, Brown University, Providence, RI 02912, USA
Author to whom correspondence should be addressed.
Academic Editors: Achim Ebert and Gunther H. Weber
Informatics 2017, 4(3), 29;
Received: 7 July 2017 / Revised: 22 August 2017 / Accepted: 24 August 2017 / Published: 28 August 2017
(This article belongs to the Special Issue Scalable Interactive Visualization)
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. View Full-Text
Keywords: scientific visualization; connectomics; electron microscopy; registration; segmentation; proofreading; graph analysis scientific visualization; connectomics; electron microscopy; registration; segmentation; proofreading; graph analysis
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MDPI and ACS Style

Haehn, D.; Hoffer, J.; Matejek, B.; Suissa-Peleg, A.; Al-Awami, A.K.; Kamentsky, L.; Gonda, F.; Meng, E.; Zhang, W.; Schalek, R.; Wilson, A.; Parag, T.; Beyer, J.; Kaynig, V.; Jones, T.R.; Tompkin, J.; Hadwiger, M.; Lichtman, J.W.; Pfister, H. Scalable Interactive Visualization for Connectomics. Informatics 2017, 4, 29.

AMA Style

Haehn D, Hoffer J, Matejek B, Suissa-Peleg A, Al-Awami AK, Kamentsky L, Gonda F, Meng E, Zhang W, Schalek R, Wilson A, Parag T, Beyer J, Kaynig V, Jones TR, Tompkin J, Hadwiger M, Lichtman JW, Pfister H. Scalable Interactive Visualization for Connectomics. Informatics. 2017; 4(3):29.

Chicago/Turabian Style

Haehn, Daniel, John Hoffer, Brian Matejek, Adi Suissa-Peleg, Ali K. Al-Awami, Lee Kamentsky, Felix Gonda, Eagon Meng, William Zhang, Richard Schalek, Alyssa Wilson, Toufiq Parag, Johanna Beyer, Verena Kaynig, Thouis R. Jones, James Tompkin, Markus Hadwiger, Jeff W. Lichtman, and Hanspeter Pfister. 2017. "Scalable Interactive Visualization for Connectomics" Informatics 4, no. 3: 29.

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