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A machine learning algorithm for identifying and tracking bacteria in three dimensions using Digital Holographic Microscopy

  • Published:2018 Issue:1 Volume:5 Page:36-49
  • ISSN:2377-9098
  • Container-title:AIMS Biophysics
  • language:en
  • Short-container-title:

Author:

Bedrossian Manuel, ,El-Kholy Marwan,Neamati Daniel,Nadeau Jay, , ,

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Molecular Biology,Biochemistry,Structural Biology,Biophysics

Reference29 articles.

1. High-throughput 3D tracking of bacteria on a standard phase contrast microscope;Taute KM, Gude S, Tans SJ, et al.;Nat Commun

2. Failed escape: Solid surfaces prevent tumbling of Escherichia coli;Molaei M, Barry M, Stocker R, et al.;Phys Rev Lett

3. Reverse and flick: Hybrid locomotion in bacteria;Stocker R;Proc Natl Acad Sci USA

4. Direct recording of holograms by a CCD target and numerical reconstruction;Schnars U, Jüptner W;Appl Optics

5. Digital holography for quantitative phase-contrast imaging;Cuche E, Bevilacqua F, Depeursinge C;Opt Lett
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