Abstract
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.
Highlights
Manual analysisDigital microscopy (b) (d)High-resolution Brownian motionSingle particle trackingDeep learning (f) U-Net (h) Detection Segmentation ClassificationAtomic theory confirmation (a)
We provide a brief review of the applications of deep learning to digital microscopy and introduce comprehensive software [DeepTrack 2.0, Fig. 1(h)] to design, train, and validate deeplearning solutions for quantitative digital microscopy
DeepTrack 2.0 builds on the particle-tracking software package DeepTrack, which we introduced in 2019,7 and greatly expands it beyond particle tracking toward a whole new range of quantitative microscopy applications, such as classification, segmentation, and cell counting
Summary
During the 1950s and in earnest in the 1960s, researchers started employing digital computers to add speed and functionalities to microscopic image analysis, with a growing focus on biomedical applications. Hannel et al employed deep learning to track and measure colloids from their holographic images,[39] Newby et al demonstrated how deep learning can be used for the simultaneous tracking of multiple particles,[40] and Helgadottir et al achieved tracking accuracy surpassing standard methods7 [Fig. 1(g)] These early successes clearly demonstrate the potential of deep learning to analyze microscopy data.
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