Automation strategies for large-scale 3D image analysis

New imaging techniques enable visualizing and analyzing a multitude of unknown phenomena in many areas of science at high spatio-temporal resolution. The rapidly growing amount of image data, however, can hardly be analyzed manually and, thus, future research has to focus on automated image analysis...

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Published inAutomatisierungstechnik : AT Vol. 64; no. 7; pp. 555 - 566
Main Authors Stegmaier, Johannes, Schott, Benjamin, Hübner, Eduard, Traub, Manuel, Shahid, Maryam, Takamiya, Masanari, Kobitski, Andrei, Hartmann, Volker, Stotzka, Rainer, van Wezel, Jos, Streit, Achim, Nienhaus, G. Ulrich, Strähle, Uwe, Reischl, Markus, Mikut, Ralf
Format Journal Article
LanguageEnglish
Published De Gruyter Oldenbourg 28.07.2016
Subjects
Big Data
Bildanalyse
developmental biology
Entwicklungsbiologie
Image analysis
microscopy
Mikroskopie
parallel computing
Paralleles Rechnen
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Summary:New imaging techniques enable visualizing and analyzing a multitude of unknown phenomena in many areas of science at high spatio-temporal resolution. The rapidly growing amount of image data, however, can hardly be analyzed manually and, thus, future research has to focus on automated image analysis methods that allow one to reliably extract the desired information from large-scale multidimensional image data. Starting with infrastructural challenges, we present new software tools, validation benchmarks and processing strategies that help coping with large-scale image data. The presented methods are illustrated on typical problems observed in developmental biology that can be answered, e.g., by using time-resolved 3D microscopy images.
ISSN:0178-2312
2196-677X
DOI:10.1515/auto-2016-0019