Congratulations to Arif Ahmed and Ida Opstad on their recently accepted paper in CVPR2020, a world-leading conference in the field of computer vision and pattern recognition ranking among the top 10 publication avenues across all disciplines according to the h5 index.
The manuscript, titled “learning nanoscale motion patterns of vesicles in living cells”, combines the fluctuation-based super-resolution algorithm MUSICAL along with machine learning to classify the motion of vesicles at the nanometric scale.
Congratulations to the authors and the rest of the team!
Abstract:
Detecting and analyzing nanoscale motion patterns of vesicles, smaller than the microscope resolution (<250 nm), inside living biological cells is a challenging problem. Stateof-the-art CV approaches based on detection, tracking, optical flow or deep learning perform poorly for this problem. We propose an integrative approach, built upon physics based simulations, nanoscopy algorithms, and shallow residual attention network to make it possible for the first time to analysis
sub-resolution motion patterns in vesicles that may also be of sub-resolution diameter. Our results show state-of-the-art performance, 88% validation accuracy on simulated dataset and 82% testing accuracy on an experimental dataset of living heart muscle cells imaged under three different pathological conditions. We demonstrate automated analysis of the motion states and changed in them for over 9000 vesicles. Such analysis will enable large scale biological studies of vesicle
transport and interaction in living cells in the future.
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