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Quantitative digital microscopy with deep learning
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AI
DOI：10.1063/5.0034891.png)
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Abstract
En 中文
Video microscopy has a long history of providing insight and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time-consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we introduce software, DeepTrack 2.0, to design, train, and validate deep-learning solutions for digital microscopy. We use this software to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking, and characterization, to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and thanks to its open-source, object-oriented programing, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.
Keywords:
ARTIFICIAL NEURAL-NETWORKS
SINGLE-MOLECULE
PARTICLE TRACKING
CELL SEGMENTATION
VIDEO MICROSCOPY
QUANTUM-DOT
LOCALIZATION
RECEPTORS
ALGORITHM
ACCURACY
Journal
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11.6
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1.2K
Citations:
1.3W
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CELL SYSTEMSOAAI
CELL SYSTEMSIF7.7
OAAIShareSaveFast Fourier domain localization algorithm of a single molecule with nanometer precisionOPTICS LETTERS
OPTICS LETTERSIF3.3
PREAI
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