Deep Learning for Microscopy

Giovanni Volpe

doi:10.1117/12.2658973

17 March 2023 Deep Learning for Microscopy

Giovanni Volpe

Proceedings Volume PC12436, Complex Light and Optical Forces XVII; PC124360O (2023) https://doi.org/10.1117/12.2658973
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

Video microscopy has a long history of providing insights 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 have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy.

Conference Presentation

Citation Download Citation

Giovanni Volpe "Deep Learning for Microscopy", Proc. SPIE PC12436, Complex Light and Optical Forces XVII, PC124360O (17 March 2023); https://doi.org/10.1117/12.2658973

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