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dc.contributor.authorBeyer, Johanna
dc.contributor.authorMohammed, Haneen
dc.contributor.authorAgus, Marco
dc.contributor.authorAl-Awami, Ali K.
dc.contributor.authorPfister, Hanspeter
dc.contributor.authorHadwiger, Markus
dc.date.accessioned2018-12-31T13:39:48Z
dc.date.available2018-12-31T13:39:48Z
dc.date.issued2018-08-22
dc.identifier.citationBeyer J, Mohammed H, Agus M, Al-Awami AK, Pfister H, et al. (2019) Culling for Extreme-Scale Segmentation Volumes: A Hybrid Deterministic and Probabilistic Approach. IEEE Transactions on Visualization and Computer Graphics 25: 1132–1141. Available: http://dx.doi.org/10.1109/TVCG.2018.2864847.
dc.identifier.issn1077-2626
dc.identifier.issn1941-0506
dc.identifier.issn2160-9306
dc.identifier.doi10.1109/TVCG.2018.2864847
dc.identifier.urihttp://hdl.handle.net/10754/630554
dc.description.abstractWith the rapid increase in raw volume data sizes, such as terabyte-sized microscopy volumes, the corresponding segmentation label volumes have become extremely large as well. We focus on integer label data, whose efficient representation in memory, as well as fast random data access, pose an even greater challenge than the raw image data. Often, it is crucial to be able to rapidly identify which segments are located where, whether for empty space skipping for fast rendering, or for spatial proximity queries. We refer to this process as culling. In order to enable efficient culling of millions of labeled segments, we present a novel hybrid approach that combines deterministic and probabilistic representations of label data in a data-adaptive hierarchical data structure that we call the label list tree. In each node, we adaptively encode label data using either a probabilistic constant-time access representation for fast conservative culling, or a deterministic logarithmic-time access representation for exact queries. We choose the best data structures for representing the labels of each spatial region while building the label list tree. At run time, we further employ a novel query-adaptive culling strategy. While filtering a query down the tree, we prune it successively, and in each node adaptively select the representation that is best suited for evaluating the pruned query, depending on its size. We show an analysis of the efficiency of our approach with several large data sets from connectomics, including a brain scan with more than 13 million labeled segments, and compare our method to conventional culling approaches. Our approach achieves significant reductions in storage size as well as faster query times.
dc.description.sponsorshipWe thank John Keyser for the ‘KESM Mouse Brain’ data set [34]. This work is partially supported by King Abdullah University of Science and Technology (KAUST) and the KAUST Office of Sponsored Research (OSR) award OSR-2015-CCF-2533-01.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8444102/
dc.subjectBloom Filter
dc.subjectData structures
dc.subjectData visualization
dc.subjectEncoding
dc.subjectHierarchical Culling
dc.subjectImage segmentation
dc.subjectMice
dc.subjectProbabilistic logic
dc.subjectRendering (computer graphics)
dc.subjectSegmented Volume Data
dc.subjectSpatial Queries
dc.subjectVolume Rendering
dc.titleCulling for Extreme-Scale Segmentation Volumes: A Hybrid Deterministic and Probabilistic Approach
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.identifier.journalIEEE Transactions on Visualization and Computer Graphics
dc.contributor.institutionHarvard University, Cambridge, MA, USA
dc.contributor.institutionSaudi Aramco, Dhahran, Saudi Arabia
kaust.personMohammed, Haneen
kaust.personAgus, Marco
kaust.personAl-Awami, Ali K.
kaust.personHadwiger, Markus
kaust.grant.numberOSR-2015-CCF-2533-01
refterms.dateFOA2019-04-03T13:02:16Z
dc.date.published-online2018-08-22
dc.date.published-print2019-01


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