ConnectomeExplorer : query-guided visual analysis of large volumetric neuroscience data

This paper presents ConnectomeExplorer, an application for the interactive exploration and query-guided visual analysis of large volumetric electron microscopy (EM) data sets in connectomics research. Our system incorporates a knowledge-based query algebra that supports the interactive specification...

Description complète

Détails bibliographiques
Publié dans:IEEE transactions on visualization and computer graphics. - 1996. - 19(2013), 12 vom: 13. Dez., Seite 2868-77
Auteur principal: Beyer, Johanna (Auteur)
Autres auteurs: Al-Awami, Ali, Kasthuri, Narayanan, Lichtman, Jeff W, Pfister, Hanspeter, Hadwiger, Markus
Format: Article en ligne
Langue:English
Publié: 2013
Accès à la collection:IEEE transactions on visualization and computer graphics
Sujets:Journal Article Research Support, Non-U.S. Gov't
Description
Résumé:This paper presents ConnectomeExplorer, an application for the interactive exploration and query-guided visual analysis of large volumetric electron microscopy (EM) data sets in connectomics research. Our system incorporates a knowledge-based query algebra that supports the interactive specification of dynamically evaluated queries, which enable neuroscientists to pose and answer domain-specific questions in an intuitive manner. Queries are built step by step in a visual query builder, building more complex queries from combinations of simpler queries. Our application is based on a scalable volume visualization framework that scales to multiple volumes of several teravoxels each, enabling the concurrent visualization and querying of the original EM volume, additional segmentation volumes, neuronal connectivity, and additional meta data comprising a variety of neuronal data attributes. We evaluate our application on a data set of roughly one terabyte of EM data and 750 GB of segmentation data, containing over 4,000 segmented structures and 1,000 synapses. We demonstrate typical use-case scenarios of our collaborators in neuroscience, where our system has enabled them to answer specific scientific questions using interactive querying and analysis on the full-size data for the first time
Description:Date Completed 02.05.2014
Date Revised 10.11.2023
published: Print
Citation Status MEDLINE
ISSN:1941-0506
DOI:10.1109/TVCG.2013.142