Skip to main content
SpringerLink
Log in
Book cover

Computer Optimized Microscopy pp 23–37Cite as

Open Source Tools for Biological Image Analysis

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2040))

Abstract

Visiting the Bio Imaging Search Engine (BISE) (Bio, BISE, Engine, http://biii.eu/, Imaging, Search) website at the time of writing this article, almost 1200 open source assets (components, workflows, collections) were found. This overwhelming range of offer difficults the fact of making a reasonable choice, especially to newcomers. In the following chapter, we briefly sketch the advantages of the open source software (OSS) particularly used for image analysis in the field of life sciences. We introduce both the general OSS idea as well as some programs used for image analysis. Even more, we outline the history of ImageJ as it has served as a role model for the development of more recent software packages. We focus on the programs that are, to our knowledge, the most relevant and widely used in the field of light microscopy, as well as the most commonly used within our facility. In addition, we briefly discuss recent efforts and approaches aimed to share and compare algorithms and introduce software and data sharing good practices as a promising strategy to facilitate reproducibility, software understanding, and optimal software choice for a given scientific problem in the future.

This is a preview of subscription content, log in via an institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Schneider CA, Rasband WS, Eliceiri KW (2012) NIH image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675

    Article  CAS  Google Scholar 

  2. Betzig E, Hell SW, Moerner WE (2014) Nobel prize chemistry. https://www.nobelprize.org/nobel_prizes/chemistry/laureates/2014/

  3. Shimomura O, Chalfie M, Tsien RY (2008) Nobel prize chemistry. https://www.nobelprize.org/nobel_prizes/chemistry/laureates/2008/

  4. Shimomura O, Johnson FH, Saiga Y (1962) Extraction, purification and properties of aequorin, a bioluminescent protein from the luminous hydromedusan, aequorea. J Cell Comp Physiol 59:223–239

    Article  CAS  Google Scholar 

  5. Chalfie M, Tu Y, Euskirchen G et al (1994) Green fluorescent protein as a marker for gene expression. Science 263:802–805

    Article  CAS  Google Scholar 

  6. Heim R, Cubitt AB, Tsien RY (1995) Improved green fluorescence. Nature 373:663–664

    Article  CAS  Google Scholar 

  7. Klar TA, Hell SW (1999) Subdiffraction resolution in far-field fluorescence microscopy. Opt Lett 24:954–956

    Article  CAS  Google Scholar 

  8. Moerner WE, Fromm DP (2003) Methods of single-molecule fluorescence spectroscopy and microscopy. Rev Sci Instrum 74:3597–3619

    Article  CAS  Google Scholar 

  9. Betzig E, Patterson GH, Sougrat R et al (2006) Imaging intracellular fluorescent proteins at nanometer resolution. Science 313:1642–1645

    Article  CAS  Google Scholar 

  10. Ledda P, Santos LP, Chalmers A (2004) In: New York, NY (ed) A local model of eye adaptation for high dynamic range images. ACM Digital Library

    Google Scholar 

  11. Dima AA, Elliott JT, Filliben JJ et al (2011) Comparison of segmentation algorithms for fluorescence microscopy images of cells: comparison of segmentation algorithms. Cytometry A 79A:545–559

    Article  Google Scholar 

  12. Gaudeul A (2007) Do open source developers respond to competition? The LATEX case study. Rev Netw Econ 6:2

    Article  Google Scholar 

  13. The GNU Operating System. https://www.gnu.org/

  14. History of the OSI. https://opensource.org/history

  15. Casson T, Ryan PS (2006) Open standards, open source adoption in the public sector, and their relationship to Microsoft’s market dominance. Social Science Research Network, Rochester, NY

    Google Scholar 

  16. The world’s leading software development platform – GitHub. https://github.com/

  17. Gualda EJ, Vale T, Almada P et al (2013) OpenSpinMicroscopy: an open-source integrated microscopy platform. Nat Methods 10:599–600

    Article  CAS  Google Scholar 

  18. Who has an OpenSPIM. http://openspim.org/Who_has_an_OpenSPIM%3F

  19. ImageJ.net Website. https://ImageJ.net

  20. ImageJ Mailing List. https://ImageJ.nih.gov/ij/list.html

  21. Image.sc Forum. https://forum.image.sc/

  22. Schindelin J, Arganda-Carreras I, Frise E et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682

    Article  CAS  Google Scholar 

  23. Bio7. https://ImageJ.net/Bio7

  24. SalsaJ. http://www.euhou.net/index.php/salsaj-software-mainmenu-9

  25. AstroImageJ. http://www.astro.louisville.edu/software/astroImageJ/

  26. BISE Bio Imaging Search Engine. http://biii.eu/

  27. Carpenter AE, Jones TR, Lamprecht MR et al (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7:R100

    Article  Google Scholar 

  28. Dao D, Fraser AN, Hung J et al (2016) CellProfiler analyst: interactive data exploration, analysis and classification of large biological image sets. Bioinforma Oxf Engl 32:3210–3212

    Article  CAS  Google Scholar 

  29. HTM Explorer. https://github.com/tischi/HTM_Explorer

  30. Shiny HTM. https://github.com/hmbotelho/shinyHTM

  31. The R Project for Statistical Computing. https://www.r-project.org/

  32. de Chaumont F, Dallongeville S, Chenouard N et al (2012) Icy: an open bioimage informatics platform for extended reproducible research. Nat Methods 9:690–696

    Article  Google Scholar 

  33. Sommer C, Straehle C, Kothe U, et al (2011) Ilastik: interactive learning and segmentation toolkit. In: Presented at the IEEE International Symposium on Biomedical Imaging

    Google Scholar 

  34. Bankhead P, Loughrey MB, Fernández JA et al (2017) QuPath: open source software for digital pathology image analysis. Sci Rep 7(1):16878

    Article  Google Scholar 

  35. Linkert M, Rueden CT, Allan C et al (2010) Metadata matters: access to image data in the real world. J Cell Biol 189:777–782

    Article  CAS  Google Scholar 

  36. Swedlow JR, Goldberg I, Brauner E et al (2003) Informatics and quantitative analysis in biological imaging. Science 300:100–102

    Article  CAS  Google Scholar 

  37. Sage D, Prodanov D, Tinevez J-Y, et al (2012) MIJ: making interoperability between ImageJ and Matlab possible. In: Presented at the ImageJ User & Developer Conference, Luxembourg

    Google Scholar 

  38. Berthold MR, Cebron N, Dill F et al (2008) KNIME: the Konstanz information miner. In: Preisach C, Burkhardt H, Schmidt-Thieme L et al (eds) Data analysis, machine learning and applications. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 319–326

    Chapter  Google Scholar 

  39. Fillbrunn A, Dietz C, Pfeuffer J et al (2017) KNIME for reproducible cross-domain analysis of life science data. J Biotechnol 261:149–156

    Article  CAS  Google Scholar 

  40. NEUBIAS: Network of European BioImage Analysts. http://eubias.org/NEUBIAS/

  41. Software Sustainability Institute. http://software.ac.uk

  42. Research Software Engineer Association. http://rse.ac.uk

  43. Burri O, Wolf B, Seitz A et al (2017) TRACMIT: an effective pipeline for tracking and analyzing cells on micropatterns through mitosis. PLoS One 12:e0179752

    Article  Google Scholar 

  44. Zenodo. http://about.zenodo.org/

  45. Cell image library. http://www.cellimagelibrary.org/home

  46. Second international challenge on 3D Deconvolution microscopy. http://bigwww.epfl.ch/deconvolution/challenge/

  47. Grand Challenges in Biomedical image analysis. https://grand-challenge.org

  48. Rajaram S, Pavie B, Hac NEF et al (2012) SimuCell: a flexible framework for creating synthetic microscopy images. Nat Methods 9:634–635

    Article  CAS  Google Scholar 

  49. Thomas K, Benjamin R-K, Fernando P et al (2016) Jupyter notebooks – a publishing format for reproducible computational workflows. Stand alone. IOS Press, Amsterdam, pp 87–90

    Google Scholar 

Download references

Acknowledgments

We would like to thank the faculty of Life Science (SV) of the EPFL for the continuous support of the bioimaging and optics platform. We are grateful to Peter Bankead and Christian Tischer for the insightful comments on the manuscript.

Author information

Authors and Affiliations

  1. Faculty of Life Sciences (SV), BioImaging and Optics Platform (BIOP), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Romain Guiet, Olivier Burri & Arne Seitz

Authors
  1. Romain Guiet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Olivier Burri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arne Seitz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arne Seitz .

Editor information

Editors and Affiliations

  1. Molecular Imaging Platform, Molecular Biology Institute of Barcelona (CSIC), Barcelona, Spain

    Elena Rebollo

  2. Scientific and Technological Centers (CCiTUB), Universitat de Barcelona, Barcelona, Spain

    Manel Bosch

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Guiet, R., Burri, O., Seitz, A. (2019). Open Source Tools for Biological Image Analysis. In: Rebollo, E., Bosch, M. (eds) Computer Optimized Microscopy. Methods in Molecular Biology, vol 2040. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9686-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9686-5_2

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9685-8

  • Online ISBN: 978-1-4939-9686-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation