Biological Imaging Software Tools Pu to K.W.E
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FOCUS ON BIOIMAGE INFORMATICS reVieW Biological imaging software tools Kevin W Eliceiri1, Michael R Berthold2, Ilya G Goldberg3, Luis Ibáñez4, B S Manjunath5, Maryann E Martone6, Robert F Murphy7, Hanchuan Peng8, Anne L Plant9, Badrinath Roysam10, Nico Stuurman11, Jason R Swedlow12, Pavel Tomancak13 & Anne E Carpenter14 Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options. The last 20 years have seen great advances in opti- to the more established hardware-development side cal imaging with the introduction of the ability to of the optical microscopy community. But with the monitor biological phenomena with unprecedented increased mainstream adoption of advanced optical- resolution, specificity, dimensionality, complexity imaging approaches by biologists and the commitment and scale, all while maintaining viability and biologi- by funding agencies to prioritize bioimage informat- cal relevance. These imaging modalities, which are ics, there has been a great increase in the number of increasingly multiparametric, rely heavily on compu- bioimage informatics1 tools over the last 5 years. tational approaches. In fact, in many cases the compu- For this Review, representative members of the bio- tational technology is just as important as the optics, image informatics community collaborated to review not just for the digital capture that all systems now use each computational step that biologists encounter but in many cases also for visualizing and properly when dealing with digital images (Fig. 1), the chal- © All rights reserved. 2012 Inc. Nature America, interpreting the data. In the quest for breakthroughs, lenges in that domain and the overall status of avail- biologists are often confronted with the challenge of able software for bioimage informatics, focusing on processing digital data of increasing complexity and open-source options. Our goal is to provide an over- richness, which demands an informatics infrastruc- view of how open-source imaging software can be ture with tools to collect, store, manipulate, analyze, used to provide an end-to-end laboratory solution interpret and visualize vast amounts of imaging data from data acquisition and storage to data analysis in a reproducible manner with the flexibility to refine and mining. aspects of their experimental and imaging techniques in a tight iterative loop. IMAGE ACQUISITION Despite this great need, the bioimage informatics In biological laboratories images are usually acquired field is still a fairly nascent community compared by measuring photon flux in parallel (using a camera) 1Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA. 2Department of Computer and Information Science, Universität Konstanz, Konstanz, Germany. 3National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA. 4Kitware Inc., New York, New York, USA. 5Department of Electrical and Computer Engineering, Center for Bio-image Informatics, University of California Santa Barbara, Santa Barbara, California, USA. 6National Center for Microscopy and Imaging Research, University of California San Diego, La Jolla, California, USA. 7Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA. 8Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA. 9Biochemical Science Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA. 10Department of Electrical and Computer Engineering, University of Houston, Houston, Texas, USA. 11Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, California, USA. 12Wellcome Trust Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK. 13Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany. 14Imaging Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. Correspondence should be addressed to K.W.E. ([email protected]) or A.E.C. ([email protected]). PUbLISHED ONLINE 28 JUNE 2012; CORRECTED AFTER PRINT 20 JULY 2012; cOrrEctED aftEr PrINt 29 aUGUSt 2012; DOI:10.1038/NMEtH.2084 nature metHoDs | VOL.9 NO.7 | JULY 2012 | 697 reVieW FOCUS ON BIOIMAGE INFORMATICS Image acquisition developed in individual research laboratories and were commer- Workflow cialized later. In addition, each of the major microscope compa- systems nies have their own software packages, such as AxioVision (Zeiss), NIS-Elements (Nikon) and cellSens (Olympus). The obvious advantage of commercial image-acquisition pack- ages is that they provide a turnkey solution to all ‘standard’ image- analysis strategies (acquiring individual images, taking time-lapse Image storage Public image series, collecting three-dimensional (3D) stacks at multiple x-y repositories positions and so on). Moreover, when such packages are pur- chased as part of an imaging system, there is less danger they will Ontologies not be compatible with the system’s configuration. It is virtually impossible, however, for individual researchers to substantially Image extend any of these software packages or make substantial custom annotation hardware changes to the imaging system. This reduces the rate at which new imaging techniques can be transferred to laboratories Image analysis and outside that of the inventor. visualization Researchers that have nonstandard or frequently chang- ing needs and equipment must often write their own code. Machine learning Software development is facilitated by toolkit environments such as LabView (National Instruments) and Matlab (Mathworks), Bioimaging which provide interfaces to a subset of available equipment and libraries and toolkits can be used to create a graphical user interface. Developing new Katie Vicari imaging technologies necessitates writing instrument-control Figure 1 | Overview of imaging workflow. Modern bioimaging requires the code, as the needs for these new techniques simply could not use of software tools for most stages of the workflow. be anticipated in existing software packages. Examples of devel- opments enabled by software written in research laboratories include structured-illumination microscopy2, super-resolution or sequentially (using a point detector and equipment that microscopy3–5 and Bessel-beam microscopy6. Although toolkit scans the area of interest). Although capturing an image from a environments provide high flexibility and are well suited for tools camera into the computer is straightforward, in most cases intended for the group that wrote them, they are less appropri- image acquisition needs to be tightly synchronized with other ate for distributing these tools to others primarily because the computer-controllable equipment such as shutters, filter wheels, infrastructure back-ends are costly and the distribution channels x-y stages, z-axis focus drives and autofocus mechanisms (imple- are not sufficiently developed. mented in software or hardware). This automation is necessary Two open-source software projects, µManager and ScanImage, to gather desired multiparametric information from a sample or whose development is driven by researchers, are intended to pro- to allow the unattended acquisition of large numbers of images vide tools with more flexibility than commercial tools and greater © All rights reserved. 2012 Inc. Nature America, in time-lapse series, z-stacks, multiple spatial locations in a large ease of use than the toolkit environments in terms of micro- sample or multiple samples in a large-scale experiment. Image- scope control. µManager mainly targets camera-based imaging, acquisition software is therefore needed to communicate with although it is also used with scanning systems7. It includes an these various components and coordinate their actions such that easy-to-use interface that runs as an ImageJ plug-in and enables the hardware functions as quickly and flawlessly as possible while researchers to design and execute common microscopy func- permitting the researcher to easily design and execute the desired tions as well as customized image-acquisition routines. The solu- sequence of acquisition events (Fig. 2). tions can be easily distributed as scripts or plug-ins. µManager’s Confocal microscope systems that require computer control to hardware abstraction layer can also be used without its ImageJ acquire an image are almost always bundled with control software, user interface in environments such as Icy, Matlab, LabView and whereas typical wide-field microscope systems are not, requiring Python, facilitating the development and transfer of software for the researcher to choose between several different independent new imaging approaches. The software framework enables any software packages for microscope control and automation. But entity, academic or commercial, to write and contribute its own not all software packages support all microscopy-related hard- device adapters for hardware components. µManager