Literature DB >> 31366471

Artificial intelligence for microscopy: what you should know.

Lucas von Chamier1, Romain F Laine2,3,4, Ricardo Henriques2,3,4,5.   

Abstract

Artificial Intelligence based on Deep Learning (DL) is opening new horizons in biomedical research and promises to revolutionize the microscopy field. It is now transitioning from the hands of experts in computer sciences to biomedical researchers. Here, we introduce recent developments in DL applied to microscopy, in a manner accessible to non-experts. We give an overview of its concepts, capabilities and limitations, presenting applications in image segmentation, classification and restoration. We discuss how DL shows an outstanding potential to push the limits of microscopy, enhancing resolution, signal and information content in acquired data. Its pitfalls are discussed, along with the future directions expected in this field.
© 2019 The Author(s).

Keywords:  artificial intelligence; classification; live-cell imaging; machine learning; segmentation; super-resolution microscopy

Year:  2019        PMID: 31366471     DOI: 10.1042/BST20180391

Source DB:  PubMed          Journal:  Biochem Soc Trans        ISSN: 0300-5127            Impact factor:   5.407


  16 in total

Review 1.  Paving the Way: Contributions of Big Data to Apicomplexan and Kinetoplastid Research.

Authors:  Robyn S Kent; Emma M Briggs; Beatrice L Colon; Catalina Alvarez; Sara Silva Pereira; Mariana De Niz
Journal:  Front Cell Infect Microbiol       Date:  2022-06-06       Impact factor: 6.073

2.  DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.

Authors:  Christoph Spahn; Estibaliz Gómez-de-Mariscal; Romain F Laine; Pedro M Pereira; Lucas von Chamier; Mia Conduit; Mariana G Pinho; Guillaume Jacquemet; Séamus Holden; Mike Heilemann; Ricardo Henriques
Journal:  Commun Biol       Date:  2022-07-09

3.  Predict Ki-67 Positive Cells in H&E-Stained Images Using Deep Learning Independently From IHC-Stained Images.

Authors:  Yiqing Liu; Xi Li; Aiping Zheng; Xihan Zhu; Shuting Liu; Mengying Hu; Qianjiang Luo; Huina Liao; Mubiao Liu; Yonghong He; Yupeng Chen
Journal:  Front Mol Biosci       Date:  2020-08-04

Review 4.  Between life and death: strategies to reduce phototoxicity in super-resolution microscopy.

Authors:  Kalina L Tosheva; Yue Yuan; Pedro Matos Pereira; Siân Culley; Ricardo Henriques
Journal:  J Phys D Appl Phys       Date:  2020-02-14       Impact factor: 3.207

5.  Avoiding a replication crisis in deep-learning-based bioimage analysis.

Authors:  Romain F Laine; Ignacio Arganda-Carreras; Ricardo Henriques; Guillaume Jacquemet
Journal:  Nat Methods       Date:  2021-10       Impact factor: 28.547

6.  Democratising deep learning for microscopy with ZeroCostDL4Mic.

Authors:  Lucas von Chamier; Romain F Laine; Johanna Jukkala; Christoph Spahn; Daniel Krentzel; Elias Nehme; Martina Lerche; Sara Hernández-Pérez; Pieta K Mattila; Eleni Karinou; Séamus Holden; Ahmet Can Solak; Alexander Krull; Tim-Oliver Buchholz; Martin L Jones; Loïc A Royer; Christophe Leterrier; Yoav Shechtman; Florian Jug; Mike Heilemann; Guillaume Jacquemet; Ricardo Henriques
Journal:  Nat Commun       Date:  2021-04-15       Impact factor: 14.919

Review 7.  Application of Super-Resolution and Advanced Quantitative Microscopy to the Spatio-Temporal Analysis of Influenza Virus Replication.

Authors:  Emma Touizer; Christian Sieben; Ricardo Henriques; Mark Marsh; Romain F Laine
Journal:  Viruses       Date:  2021-02-02       Impact factor: 5.048

8.  On the objectivity, reliability, and validity of deep learning enabled bioimage analyses.

Authors:  Dennis Segebarth; Matthias Griebel; Christoph M Flath; Robert Blum; Nikolai Stein; Cora R von Collenberg; Corinna Martin; Dominik Fiedler; Lucas B Comeras; Anupam Sah; Victoria Schoeffler; Teresa Lüffe; Alexander Dürr; Rohini Gupta; Manju Sasi; Christina Lillesaar; Maren D Lange; Ramon O Tasan; Nicolas Singewald; Hans-Christian Pape
Journal:  Elife       Date:  2020-10-19       Impact factor: 8.140

9.  A fluorescent reporter system enables spatiotemporal analysis of host cell modification during herpes simplex virus-1 replication.

Authors:  Katharina M Scherer; James D Manton; Timothy K Soh; Luca Mascheroni; Vivienne Connor; Colin M Crump; Clemens F Kaminski
Journal:  J Biol Chem       Date:  2021-01-07       Impact factor: 5.157

10.  Arrayed CRISPRi and quantitative imaging describe the morphotypic landscape of essential mycobacterial genes.

Authors:  Timothy J de Wet; Kristy R Winkler; Musa Mhlanga; Valerie Mizrahi; Digby F Warner
Journal:  Elife       Date:  2020-11-06       Impact factor: 8.713

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