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30 September 2024 Deep learning for imaging and microscopy
Giovanni Volpe
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Proceedings Volume PC13126, Molecular and Nanophotonic Machines, Devices, and Applications VII; PC1312603 (2024) https://doi.org/10.1117/12.3028561
Event: Organic Photonics + Electronics, 2024, San Diego, 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.
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Giovanni Volpe "Deep learning for imaging and microscopy", Proc. SPIE PC13126, Molecular and Nanophotonic Machines, Devices, and Applications VII, PC1312603 (30 September 2024); https://doi.org/10.1117/12.3028561
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KEYWORDS
Deep learning
Microscopy
Biological imaging
Video microscopy
Image analysis
Biology
Design
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