Quantitative Microscopy with Deep Learning

Explore the untapped potential of deep learning in video microscopy with our cutting-edge software, DeepTrack 2.2. Overcoming the steep learning curve, this innovative application empowers users to effortlessly design, train, and validate custom deep-learning solutions for diverse digital microscopy applications, revolutionizing particle localization, tracking, characterization, cell counting, and classification.

This webinar was originally held on February 13th, 2024, and is now available for on demand viewing. 

Duration: 1 hour

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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 have introduced a software application, currently at version DeepTrack 2.2, to design, train and validate deep-learning solutions for digital microscopy. We use it 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.


Giovanni Volpe
Professor, Dept. of Physics
University of Gothenburg

Sponsored by:

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