Bioimage informatics: a new area of engineering biology

By Hanchuan Peng. Bioinform., 2008

382 citations recorded by the source corpus.

In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called ‘bioimage informatics’. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources. Contact: pengh@janelia.hhmi.org Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/65ff54147ae4c53bc3223e48dddd0604450263bc

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Citations in the corpus

Neuron tracing in perspective (2010, background): “Since research in the various areas of neuroscience is increasingly relying on imaging, giving rise to massive amounts of heterogeneous and complex data collected at multiple scales of observation, the need for advanced bioimage informatics (5) and neuroinformatics (6–8) solutions for integrating an”
Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluorescence Microscopy (2010, methodology): “ANY cell biological studies rely on the analysis of large cell populations by means of microscopy imaging [1]–[ 6 ].”
Cell Segmentation: 50 Years Down the Road [Life Sciences] (2012, methodology): “Consequently, computerized techniques for performing these tasks have become of key importance for further progress in cell biology [3]–[6].”
V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets (2010, background): “1612" language="eng" relation="no"> Quantitative image analysis is rapidly becoming the bottleneck in high-throughput experiments that seek to gain new insights from advanced 3D microscopic digital images collected using multiple fluorescent probes of different colors, and optionally also f”