Abstract
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.
Zusammenfassung
Neue bildgebende Verfahren ermöglichen eine detaillierte Analyse und Visualisierung verschiedenster unbekannter Phänomene in vielen wissenschaftlichen Bereichen mit hoher räumlicher und zeitlicher Auflösung. Die stetig wachsende Bilddatenmenge kann hierbei nur unzureichend manuell untersucht werden, weswegen künftig vermehrt automatische Auswertungsroutinen benötigt werden, um relevante Informationen zuverlässig aus großen, mehrdimensionalen Bilddaten zu extrahieren. Beginnend mit den infrastrukturellen Herausforderungen werden im Rahmen dieser Veröffentlichung neue Softwaretools, ein Validierungsbenchmark sowie verschiedene Verarbeitungsstrategien vorgestellt, um die Analyse großer Bilddaten zu ermöglichen und zu vereinfachen. Die präsentierten Methoden werden anhand von typischen Problemen aus der Entwicklungsbiologie veranschaulicht, die mittels zeitaufgelöster 3D Mikroskopie untersucht werden.
About the authors
Institute for Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany; and HeiKa – Heidelberg Karlsruhe Research Partnership, Heidelberg University, Karlsruhe Institute of Technology (KIT), Germany
Institute for Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
Institute for Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
Institute for Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Institute of Applied Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Karlsruhe, Germany
Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Karlsruhe, Germany
Institute of Nanotechnology, Karlsruhe Institute of Technology, Karlsruhe, Germany; and Institute of Applied Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany; and Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Institute for Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany; and HeiKa – Heidelberg Karlsruhe Research Partnership, Heidelberg University, Karlsruhe Institute of Technology (KIT), Germany
Institute for Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
Acknowledgement
We are grateful for funding by the Helmholtz Association in the programs BioInterfaces (BS, EH, MT, MS, MT, AK, US, MR, RM), Science and Technology of Nanosystems (GUN), Supercomputing and Big Data (VH, RS, JW, AS), the German Research Foundation DFG (JS, Grant No MI 1315/4, associated with SPP 1736 Algorithms for Big Data) and the HeiKa Initiative (JS, MR).
©2016 Walter de Gruyter Berlin/Boston
