title: Seeing Is Believing: Quantifying Is Convincing: Computational Image Analysis in Biology.
abstract: Imaging is center stage in biology. Advances in microscopy and labeling techniques have enabled unprecedented observations and continue to inspire new developments. Efficient and accurate quantification and computational analysis of the acquired images, however, are becoming the bottleneck. We review different paradigms of computational image analysis for intracellular, single-cell, and tissue-level imaging, providing pointers to the specialized literature and listing available software tools. We place particular emphasis on clear categorization of image-analysis frameworks and on identifying current trends and challenges in the field. We further outline some of the methodological advances that are required in order to use images as quantitative scientific measurements.
authors.affiliation: Scientific Computing for Systems Biology, Faculty of Computer Science, TU Dresden, Dresden, Germany. ivos@mpi-cbg.de.|MOSAIC Group, Center for Systems Biology Dresden, Dresden, Germany. ivos@mpi-cbg.de.|Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr. 108, D-01307, Dresden, Germany. ivos@mpi-cbg.de.
authors.forename: Ivo F
authors.initials: IF
authors.lastname: Sbalzarini
mesh_terms: D000818:Animals; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D000069550:Machine Learning; D008432:Mathematical Computing; D008856:Microscopy, Fluorescence / Q000295:instrumentation / Q000379:methods*; D059010:Single-Cell Analysis / Q000295:instrumentation / Q000379:methods; D012984:Software; D013194:Staining and Labeling / Q000379:methods