An adaptive multirate algorithm for acquisition of fluorescence microscopy data sets

Merryman, T.; Kovačević, Jelena

doi:10.1109/tip.2005.855861

Cited by 18 publications

(13 citation statements)

References 9 publications

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“…Related work on efficient acquisition for fluorescence microscopy was done by Merryman & Kovačević [4]. They presented an algorithm to reduce the number of pixels acquired in a 2-D or 3-D image when using a laser scanning confocal microscope, with the end application being recognition of proteins based on their subcellular location [5]- [8].…”

Section: Related Workmentioning

confidence: 99%

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Intelligent Acquisition and Learning of Fluorescence Microscope Data Models

Jackson

Murphy

Kovačević

2009

IEEE Trans. on Image Process.

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Abstract-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.Index Terms-Active learning, fluorescence microscopy, image modeling, intelligent acquisition, particle filter.

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Section: Related Workmentioning

confidence: 99%

“…1) is more general and applies to other scenarios as well. A simple example was already mentioned in Section I-C where Merryman and Kovačević [4] proposed an intelligent acquisition strategy for the purposes of classification. In that example, the model is the classifier output.…”

Section: A Modelingmentioning

confidence: 99%

Intelligent Acquisition and Learning of Fluorescence Microscope Data Models

Jackson

Murphy

Kovačević

2009

IEEE Trans. on Image Process.

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“…Many computational techniques have been developed to address limitations in the study of biochemical and biological molecules. In paper [46], Merryman and Kovacevic proposed an algorithm for adaptive efficient acquisition of fluorescence microscopy data sets using a multirate approach in the context of classification of proteins based on their subcellular locations. Their algorithm outperforms standard downsampling because it uses an intelligent acquisition scheme and retains high frequencies and saves samples where low frequencies are present [47].…”

Section: Nano-scale Characterization and Analysismentioning

confidence: 99%

Computational Issues in Biomedical Nanometrics and Nano-Materials

Huang

Shen

Ford

et al. 2008

JNanoR

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Abstract. Biomedical Nanotechnology is an emerging area of great scientific and technological opportunity. It is widely recognized as one of the most potentially beneficial applications of nanotechnology to industry and society to date. Work in this area has a number of computational aspects: information technology based tools and measurement techniques are used to study biosystems with micro-and nano-scale physics and chemistry, and computational methods are helping to generate remarkable new insights into how biological systems function, how metabolic processes interrelate, and how new molecular scale machines can operate. This paper reviews current advances in computational algorithms and tools applied to biomedical nanometrics and nano-materials. We categorize algorithms into three general areas, describe representative methods, and conclude with several promising directions of future investigation.

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“…Hence, DS methods have the potential to find a sparse set of measurements that will allow for a high-fidelity reconstruction of the underlying sample. DS methods in the literature include dynamic compressive sensing methods [8,9] which are meant for unconstrained measurements, application specific DS methods [10,11,12], and point-wise DS methods [13,14,15]. In this paper, we use the dynamic sampling method described in [15], Supervised Learning Approach for Dynamic Sampling (SLADS).…”

Section: Introductionmentioning

confidence: 99%

Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling

et al. 2018

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Analytical electron microscopy and spectroscopy of biological specimens, polymers, and other beam sensitive materials has been a challenging area due to irradiation damage. There is a pressing need to develop novel imaging and spectroscopic imaging methods that will minimize such sample damage as well as reduce the data acquisition time. The latter is useful for high-throughput analysis of materials structure and chemistry. In this work, we present a novel machine learning based method for dynamic sparse sampling of EDS data using a scanning electron microscope. Our method, based on the supervised learning approach for dynamic sampling algorithm and neural networks based classification of EDS data, allows a dramatic reduction in the total sampling of up to 90%, while maintaining the fidelity of the reconstructed elemental maps and spectroscopic data. We believe this approach will enable imaging and elemental mapping of materials that would otherwise be inaccessible to these analysis techniques.

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An adaptive multirate algorithm for acquisition of fluorescence microscopy data sets

Cited by 18 publications

References 9 publications

Intelligent Acquisition and Learning of Fluorescence Microscope Data Models

Intelligent Acquisition and Learning of Fluorescence Microscope Data Models

Computational Issues in Biomedical Nanometrics and Nano-Materials

Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling

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