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20 August 2020 Digital video microscopy with deep learning
Saga Helgadottir, Aykut Argun, Giovanni Volpe
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Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 1146918 (2020) https://doi.org/10.1117/12.2566918
Event: SPIE Nanoscience + Engineering, 2020, Online Only
Abstract
From the start of digital video microscopy over 20 years ago, single particle tracking has been dominated by algorithmic approaches. These methods are successful at tracking well-defined particles in good imaging conditions but their performance degrades severely in more challenging conditions. To overcome the limitations of traditional algorithmic approaches, data-driven methods using deep learning have been introduced. They managed to successfully track colloidal particles as well as non-spherical biological objects, even in unsteady imaging conditions.
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Saga Helgadottir, Aykut Argun, and Giovanni Volpe "Digital video microscopy with deep learning", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 1146918 (20 August 2020); https://doi.org/10.1117/12.2566918
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KEYWORDS
Particles
Video microscopy
Detection and tracking algorithms
Image segmentation
Convolutional neural networks
Digital imaging
Computer simulations
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