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Deep Learning‐Based Point‐Scanning Super‐Resolution Imaging
Author(s) -
Manor Uri,
Fang Linjing,
Howard Jeremy,
Monroe Fred,
Weiser Sammy,
Kastner Kyle,
Kirk Lyndsey,
Harris Kristen,
Pekkurnaz Gulcin,
Yoon Blenda,
Schiavon Cara,
Zhang Tong
Publication year - 2020
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2020.34.s1.02496
Subject(s) - laser scanning , image resolution , resolution (logic) , scanning electron microscope , computer science , confocal , microscope , artificial intelligence , optics , computer vision , laser , physics
Point scanning imaging systems (e.g. scanning electron or laser scanning confocal microscopes) are perhaps the most widely used tools for high resolution cellular and tissue imaging. Like all other imaging modalities, the resolution, speed, sample preservation, and signal‐to‐noise ratio (SNR) of point scanning systems are difficult to optimize simultaneously. In particular, point scanning systems are uniquely constrained by an inverse relationship between imaging speed and pixel resolution. Here we show these limitations can be mitigated via the use of deep learning‐based super‐sampling of undersampled images acquired on a point‐scanning system, which we termed point‐scanning super‐resolution (PSSR) imaging. Oversampled, high SNR ground truth images acquired on scanning electron or Airyscan laser scanning confocal microscopes were ‘crappified’ to generate semi‐synthetic training data for PSSR models that were then used to restore real‐world undersampled images. Remarkably, our EM PSSR model could restore undersampled images acquired with different optics, detectors, samples, or sample preparation methods in other labs. PSSR enabled previously unattainable 2 nm resolution images with our serial block face scanning electron microscope system. For fluorescence, we show that undersampled confocal images combined with a multiframe PSSR model trained on Airyscan timelapses facilitates Airyscan‐equivalent spatial resolution and SNR with ~100x lower laser dose and 16x higher frame rates than corresponding high‐resolution acquisitions. In conclusion, PSSR facilitates point‐scanning image acquisition with otherwise unattainable resolution, speed, and sensitivity. Linjing Fang 1 Fred Monroe 2 Sammy Weiser Novak 1 Lyndsey Kirk 3 Cara R. Schiavon 1 Seungyoon B. Yu 4 Tong Zhang 1 Melissa Wu 1 Kyle Kastner 5 Yoshiyuki Kubota 6 Zhao Zhang 7 Gulcin Pekkurnaz 4 John Mendenhall 3 Kristen Harris 3 Jeremy Howard 8 Uri Manor 1 1. Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, USA 2. Wicklow AI Medical Research Initiative, San Francisco, CA, USA 3. Department of Neuroscience, Center for Learning and Memory, Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA 4. Neurobiology Section, Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA 5. Montreal Institute for Learning Algorithms, Université de Montréal, Canada 6. Division of Cerebral Circuitry, National Institute for Physiological Sciences, Okazaki, 444‐8787 Japan 7. Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, USA 8. Fast.AI, University of San Francisco Data Institute, San Francisco, CA, USA