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Handling and Analyzing Meshed Rendering of Segmented Structures From 3D Image Stacks in Blender
Department of Computational Biology, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden . (Department of Computational Biology. Royal Institute of Technology)ORCID iD: 0000-0003-3522-0196
2015 (English)In: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 13, no 2, 151-152 p.Article, review/survey (Refereed) Published
Abstract [en]

Volume electron microscopy (EM) is an aggregate of techniques (e.g. FIB-SEM, SBF-SEM) which are evolving toward easing the trade-off between resolution and field of view size so that increasingly large volumes of tissue can be imaged at just a few nanometer resolution. These technical developments have been driven by the necessity of characterizing spatially extended structures with functionally relevant supramolecular elements that are several orders of magnitude smaller. Neuronal microcircuits with their intracellular organelles and synaptic specializations stand out among these demanding biological objects.

With the increasing image throughput, the bottleneck in the pipeline is the segmentation of both the neurites (axon and dendrites) and the organelles and synaptic structures from the collected 3D EM image stack. In both cases, iterations between human curation and machine learning procedures are the norm.

Place, publisher, year, edition, pages
2015. Vol. 13, no 2, 151-152 p.
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:uu:diva-245791DOI: 10.1007/s12021-014-9250-5OAI: oai:DiVA.org:uu-245791DiVA: diva2:791525
Available from: 2015-03-01 Created: 2015-03-01 Last updated: 2017-12-04Bibliographically approved

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Gutierrez Arenas, Omar

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