Building cell models and simulations from microscope images.
The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source software tools provide the ability to use these methods in a wide range of studies, and many molecular and cellular phenotypes can now be automatically distinguished. This article presents the next major challenge in microscopy automation, the creation of accurate models of cell organization directly from images, and reviews the progress that has been made towards this challenge.
Computational Biology Department, Center for Bioimage Informatics, and Departments of Biological Sciences, Biomedical Engineering and Machine Learning, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA; Freiburg Institute for Advanced Studies and Faculty of Biology, Albert Ludwig University of Freiburg, Germany. Electronic address: murphy@cmu.edu.
Robert F
RF
Murphy
D048430:Cell Shape; D019295:Computational Biology / Q000295:instrumentation / Q000379:methods; D003198:Computer Simulation; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D008856:Microscopy, Fluorescence / Q000379:methods / Q000706:statistics & numerical data*; D008954:Models, Biological; D010363:Pattern Recognition, Automated; D012984:Software
Cell organization; Cell shape; Computational biology; Generative models; Image-based modeling