Machine learning in cell biology – teaching computers to recognize phenotypes

Commercially available motorized microscopes can yield data at a throughput of 105 images per day, raising a strong need for automated data analysis (Conrad and Gerlich, 2010; Lock and Strömblad, 2010). Computational data analysis not only reduces the workload for the experimentalist, but also ensures objectivity and consistency in the annotation of large data sets (Danuser, 2011). The complexity and diversity in microscopic image data, however, poses challenges for developing suitable data analysis workflows. Bioimage informatics methods offer powerful solutions for specific image analysis tasks, such as object detection, motion analysis or measurements of morphometric features (Danuser, 2011; Murphy, 2011; Eliceiri et al., 2012; Myers, 2012). Most image analysis algorithms, however, have been developed for specific biological assays.