Giovanni Volpe
Mini-Symposium with Giovanni Volpe and Pawel Sikorski, Lund, Sweden, 11 January 2024
Date: 11 January 2024
Time: 15:15
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, DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy.