Scalable Interactive Visualization for Connectomics

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 middl...

Full description

Saved in:
Bibliographic Details
Published in:Informatics (Basel) Vol. 4; no. 3; p. 29
Main Authors: Haehn, Daniel, Hoffer, John, Matejek, Brian, Suissa-Peleg, Adi, Al-Awami, Ali, Kamentsky, Lee, Gonda, Felix, Meng, Eagon, Zhang, William, Schalek, Richard, Wilson, Alyssa, Parag, Toufiq, Beyer, Johanna, Kaynig, Verena, Jones, Thouis, Tompkin, James, Hadwiger, Markus, Lichtman, Jeff, Pfister, Hanspeter
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2017
Subjects:
Brain
connectomics
Data management
electron microscopy
graph analysis
Middleware
proofreading
registration
Rendering
scientific visualization
segmentation
Visualization
Workflow
ISSN:2227-9709, 2227-9709
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2227-9709
2227-9709
DOI:10.3390/informatics4030029