COMPUTATIONAL TOOLS 109
- Fluorescent speckle microscopy, a technique for quantitatively tracking the movement, assem-
bly, and disassembly of macromolecules in vivo and in vitro, such as those involved in cytoskeleton
dynamics;^143 and - Establishing metrics of similarity between brain images taken at different times.^144
These applications are only an infinitesimal fraction of those that are possible. Several research
areas associated with increasing the utility of biological images are discussed below. Box 4.7 describes
the open microscopy environment, an effort intended to automate image analysis, modeling, and min-
ing of large sets of biological images obtained from optical microscopy.^145
As a general rule, biologists need to develop better imaging methods that are applicable across the
entire spatial scale of interest, from the subcellular to the organismal. (In this context, “better” means
imaging that occurs in real time (or nearly so) with the highest possible spatial and temporal resolution.)
These methods will require new technologies (such as the multiphoton microscope) and also new
protein and nonprotein reporter molecules that can be expressed or introduced into cells or organisms.(^143) C.M. Waterman-Storer and G. Danuser, “New Directions for Fluorescent Speckle Microscopy,” Current Biology 12(18):R633-
R640, 2002.
(^144) M.I. Miller, A. Trouve, and L. Younes, “On the Metrics and Euler-Lagrange Equations of Computational Anatomy,” Annual
Review of Biomedical Engineering 4:375-405, 2002, available at http://www.cis.jhu.edu/publications/papers_in_database/
EulerLagrangeEqnsCompuAnatomy.pdf.
(^145) J.R. Swedlow, I. Goldberg, E. Brauner, and P.K. Sorger, “Informatics and Quantitative Analysis in Biological Imaging,”
Science 300(5616):100-102, 2003.
Box 4.7
The Open Microscopy Environment^1
Responding to the need to manage a large number of multispectral movies of mitotic cells in the late 1990s,
Sorger and Swedlow began work on the open microscopy environment (OME). The OME is designed as
infrastructure that manages optical microscopy images, storing both the primary image data and appropriate
metadata on those images, including data on the optics of the microscope, the experimental setup and sam-
ple, and information derived by analysis of the images. OME also permits data federation that allows informa-
tion from multiple sources (e.g., genomic or chemical databases) to be linked to image records.
In addition, the OME provides an extensible environment that enables users to write their own applications for
image analysis. Consider, for example, the task of tracking labeled vesicles in a time-lapse movie. As noted by
Swedlow et al., this problem requires the following: a segmentation algorithm to find the vesicles and to
produce a list of centroids, volumes, signal intensities, and so on; a tracker to define trajectories by linking
centroids at different time points according to a predetermined set of rules; and a viewer to display the analytic
results overlaid on the original movie.^2
OME provides a mechanism for linking together various analytical modules by specifying data semantics that
enable the output of one module to be accepted as input to another. These semantic data types of OME
describe analytic results such as “centroid,” “trajectory,” and “maximum signa,” and allow users, rather than
a predefined standard, to define such concepts operationally, including in the machine-readable definition
and the processing steps that produce it (e.g., the algorithm and the various parameter settings used).
(^1) See http://www.openmicroscopy.org.
(^2) J.R. Swedlow, I. Goldberg, E. Brauner, and P.K. Sorger, “Informatics and Quantitative Analysis in Biological Imaging,” Science
300(5616):100-102, 2003.
SOURCE: Based largely on the paper by Swedlow et al. cited in Footnote145 and on the OME Web page at http://www.openmicroscopy.org.