![]() Many existing software tools are not well suited for big data nor the wide variety of research questions these data sets allow. Among these are the development of advanced techniques for preparing and staining very large pieces of tissue for electron microscopy ( McIntyre and Fahy, 2015 Mikula and Denk, 2015), block-face cutting and imaging methods like SBEM/SBF-SEM ( Denk and Horstmann, 2004) and FIB-SEM ( Knott et al., 2011 Hayworth et al., 2015), automated collection of sections on tape, for example ATUM ( Hayworth et al., 2006 Kasthuri et al., 2015), and high-speed imaging techniques like TEMCA ( Bock et al., 2011), and the Zeiss mSEM ( Eberle et al., 2015).īecause of the wider availability of these methods, analyzing big data sets poses a challenge for a growing number of researchers. Several experimental techniques have been introduced to enable processing and imaging such large volumes of tissue with electron microscopy. Projects include descriptions of the entire nervous systems of a variety of animals, for example Caenorhabditis elegans ( White et al., 1986 Varshney et al., 2011), Drosophila melanogaster ( Zheng et al., 2017), Zebrafish ( Hildebrand et al., 2017) wiring diagrams of specific parts of larger nervous systems, for example mouse retina ( Helmstaedter et al., 2011, 2013 Kim et al., 2014 Bae et al., 2018), thalamic nuclei ( Morgan et al., 2016), and cortex ( Bock et al., 2011 Kasthuri et al., 2015 Lee et al., 2016) function-structure relationships, for example directional selectivity in the retina ( Briggman et al., 2011 Kim et al., 2014), detection of visual motion in drosophila ( Takemura et al., 2013), learning and plasticity in hippocampus ( Mishchenko et al., 2010 Bartol et al., 2015), synapse elimination in the neuromuscular junction ( Tapia et al., 2012) and many others. In neuroscience acquisition of high-resolution volumetric data sets of the nervous system has become routine, with the goal of addressing a number of long-standing questions ( Briggman and Bock, 2012 Helmstaedter, 2013 Morgan and Lichtman, 2013, 2017 Plaza et al., 2014 Titze and Genoud, 2016). At the same time progress in processing speeds and storage capacity of computer hardware enables imaging scientists to work with big data. ![]() The acquisition of microscopic data is becoming ever faster and more and more automated, leading to the generation of enormous image datasets. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |