Intelligent Acquisition and Learning of Fluorescence Microscope Data Models

• Published in: IEEE transactions on image processing Vol. 18; no. 9; pp. 2071 - 2084
• Main Authors: Jackson, C., Murphy, R.F., Kovacevic, J.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acquisitions; Active learning; Algorithm design and analysis; Algorithms; Applied sciences; Artificial Intelligence; Biological system modeling; Buildings; Construction; Data models; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Fluorescence; fluorescence microscopy; image modeling; Image processing; Information, signal and communications theory; Inspection; intelligent acquisition; Intelligent systems; Marketing; Mathematical model; Mathematical models; Microscopy; Microscopy, Fluorescence - methods; Models, Theoretical; particle filter; Photobleaching; Reproducibility of Results; Signal and communications theory; Signal processing; Signal, noise; Studies; Telecommunications and information theory; Time series; Performance evaluation; Microscope; intelligent acquisition; Active learning; Time series; Information rate; image modeling; Algorithm; Modeling; Information transmission; fluorescence microscopy; Learning; Microscopy; Particle filter; Intelligent system; Visual control; Photobleaching
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2009.2024580

We propose a mathematical framework and algorithms both to build accurate models of fluorescence microscope time series, as well as to design intelligent acquisition systems based on these models. Model building allows the information contained in the 2-D and 3-D time series to be presented in a more useful and concise form than the raw image data. This is particularly relevant as the trend in biology tends more and more towards high-throughput applications, and the resulting increase in the amount of acquired image data makes visual inspection impractical. The intelligent acquisition system uses an active learning approach to choose the acquisition regions that let us build our model most efficiently, resulting in a shorter acquisition time, as well as a reduction of the amount of photobleaching and phototoxicity incurred during acquisition. We validate our methodology by modeling object motion within a cell. For intelligent acquisition, we propose a set of algorithms to evaluate the information contained in a given acquisition region, as well as the costs associated with acquiring this region in terms of the resulting photobleaching and phototoxicity and the amount of time taken for acquisition. We use these algorithms to determine an acquisition strategy: where and when to acquire, as well as when to stop acquiring. Results, both on synthetic as well as real data, demonstrate accurate model building and large efficiency gains during acquisition.