Computer Vision in Cell Biology

Abstract

Computer vision refers to the theory and implementation of artificial systems that extract information from images to understand their content. Although computers are widely used by cell biologists for visualization and measurement, interpretation of image content, i.e., the selection of events worth observing and the definition of what they mean in terms of cellular mechanisms, is mostly left to human intuition. This Essay attempts to outline roles computer vision may play and should play in image-based studies of cellular life.

Systematic quantification of developmental phenotypes at single-cell resolution during embryogenesis

“…Using automated image analysis, dozens to hundreds of measurements can be made to systematically and objectively extract and analyze complex information. In cell culture, such methods have enabled systems analysis and large-scale screens examining cell shapes, division, death and subcellular textures (Carpenter et al, 2006;Bakal et al, 2007;Neumann et al, 2010;Danuser, 2011). Meanwhile, 3D time-lapse imaging has allowed detailed, cellular resolution recording of extensive time windows of embryogenesis in organisms such as C. elegans (Schnabel et al, 1997;Bao et al, 2006), Drosophila (McMahon et al, 2008;Keller et al, 2010;Truong et al, 2011), zebrafish (Keller et al, 2008;Truong et al, 2011) and mouse (Kwon et al, 2008).…”

Cells on film – the past and future of cinemicroscopy

“…Computational post-processing of time-lapse movies has allowed for an amazing array of image analysis techniques. From image processing algorithms, which automatically segment cells and intracellular structures of interest, to tracking programs that subsequently record the position of these components over time, we can gather vast quantities of data that is impossible (or too time consuming) to do by eye (Danuser, 2011). Abercrombie's original analysis of cell speed involved the arduous task of making a ''series of enlarged prints of individual frames'' at regular intervals (per ''foot of film'') and manually quantifying cellular displacement (Abercrombie and Heaysman, 1953) (Fig.…”

Machine learning in cell biology – teaching computers to recognize phenotypes

“…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.…”

“…A number of recent reviews and textbooks provide extensive theoretical background on different machine-learning algorithms (Hastie et al, 2005;Bishop, 2006;Larrañaga et al, 2006;Tarca et al, 2007;Danuser, 2011;de Ridder et al, 2013). Successful application of machine learning, however, also needs to take into account many practical considerations and it requires knowledge about the specific data type and analysis goals.…”