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Deep learning-based point-scanning super-resolution imaging.
Fang, Linjing; Monroe, Fred; Novak, Sammy Weiser; Kirk, Lyndsey; Schiavon, Cara R; Yu, Seungyoon B; Zhang, Tong; Wu, Melissa; Kastner, Kyle; Latif, Alaa Abdel; Lin, Zijun; Shaw, Andrew; Kubota, Yoshiyuki; Mendenhall, John; Zhang, Zhao; Pekkurnaz, Gulcin; Harris, Kristen; Howard, Jeremy; Manor, Uri.
Afiliación
  • Fang L; Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Monroe F; Wicklow AI Medical Research Initiative, San Francisco, CA, USA.
  • Novak SW; Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Kirk L; Department of Neuroscience, Center for Learning and Memory, Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA.
  • Schiavon CR; Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Yu SB; Neurobiology Section, Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA.
  • Zhang T; Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Wu M; Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Kastner K; Montreal Institute for Learning Algorithms, Université de Montréal, Montréal, Canada.
  • Latif AA; Fast.AI, University of San Francisco Data Institute, San Francisco, CA, USA.
  • Lin Z; Fast.AI, University of San Francisco Data Institute, San Francisco, CA, USA.
  • Shaw A; Fast.AI, University of San Francisco Data Institute, San Francisco, CA, USA.
  • Kubota Y; Division of Cerebral Circuitry, National Institute for Physiological Sciences, Okazaki, Japan.
  • Mendenhall J; Department of Neuroscience, Center for Learning and Memory, Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA.
  • Zhang Z; Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, USA.
  • Pekkurnaz G; Neurobiology Section, Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA.
  • Harris K; Department of Neuroscience, Center for Learning and Memory, Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA.
  • Howard J; Fast.AI, University of San Francisco Data Institute, San Francisco, CA, USA.
  • Manor U; Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, USA. umanor@salk.edu.
Nat Methods ; 18(4): 406-416, 2021 04.
Article en En | MEDLINE | ID: mdl-33686300
Point-scanning imaging systems are among the most widely used tools for high-resolution cellular and tissue imaging, benefiting from arbitrarily defined pixel sizes. The resolution, speed, sample preservation and signal-to-noise ratio (SNR) of point-scanning systems are difficult to optimize simultaneously. We show these limitations can be mitigated via the use of deep learning-based supersampling of undersampled images acquired on a point-scanning system, which we term point-scanning super-resolution (PSSR) imaging. We designed a 'crappifier' that computationally degrades high SNR, high-pixel resolution ground truth images to simulate low SNR, low-resolution counterparts for training PSSR models that can restore real-world undersampled images. For high spatiotemporal resolution fluorescence time-lapse data, we developed a 'multi-frame' PSSR approach that uses information in adjacent frames to improve model predictions. PSSR facilitates point-scanning image acquisition with otherwise unattainable resolution, speed and sensitivity. All the training data, models and code for PSSR are publicly available at 3DEM.org.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Prognostic_studies Idioma: En Revista: Nat Methods Asunto de la revista: TECNICAS E PROCEDIMENTOS DE LABORATORIO Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Prognostic_studies Idioma: En Revista: Nat Methods Asunto de la revista: TECNICAS E PROCEDIMENTOS DE LABORATORIO Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos