We suggest that wndchrm can be effectively used for a wide range of biological image analysis tasks. Using wndchrm can allow scientists to perform automated biological image analysis while avoiding the costly challenge of implementing computer vision and pattern recognition algorithms.
Many behavioral responses require the coordination of sensory inputs with motor outputs. Aging is associated with progressive declines in both motor function and muscle structure. However, the consequences of age-related motor deficits on behavior have not been clearly defined. Here, we examined the effects of aging on behavior in the nematode, Caenorhabditis elegans. As animals aged, mild locomotory deficits appeared that were sufficient to impair behavioral responses to sensory cues. In contrast, sensory ability appeared well maintained during aging. Age-related behavioral declines were delayed in animals with mutations in the daf-2/insulin-like pathway governing longevity. A decline in muscle tissue integrity was correlated with the onset of age-related behavioral deficits, although significant muscle deterioration was not. Treatment with a muscarinic agonist significantly improved locomotory behavior in aged animals, indicating that improved neuromuscular signaling may be one strategy for reducing the severity of age-related behavioral impairments.
The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.
The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. OME is designed to support high-content cell-based screening as well as traditional image analysis applications. The OME Data Model, expressed in Extensible Markup Language (XML) and realized in a traditional database, is both extensible and self-describing, allowing it to meet emerging imaging and analysis needs.
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