Skip to main content

Bringing Open Data to Whole Slide Imaging

  • Conference paper
  • First Online:
Book cover Digital Pathology (ECDP 2019)

Abstract

Faced with the need to support a growing number of whole slide imaging (WSI) file formats, our team has extended a long-standing community file format (OME-TIFF) for use in digital pathology. The format makes use of the core TIFF specification to store multi-resolution (or “pyramidal”) representations of a single slide in a flexible, performant manner. Here we describe the structure of this format, its performance characteristics, as well as an open-source library support for reading and writing pyramidal OME-TIFFs.

S. Besson, R. Leigh and M. Linkert—These authors contributed equally to this work.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

References

  1. Udall, M., et al.: PD-L1 diagnostic tests: a systematic literature review of scoring algorithms and test-validation metrics. Diagn. Pathol. 13, 12 (2018)

    Article  Google Scholar 

  2. Lin, J.-R., et al.: Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. Elife 7, 31657 (2018)

    Article  Google Scholar 

  3. Goltsev, Y., et al.: Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018)

    Article  Google Scholar 

  4. Leo, P., et al.: Stable and discriminating features are predictive of cancer presence and Gleason grade in radical prostatectomy specimens: a multi-site study. Sci. Rep. 8, 14918 (2018)

    Article  Google Scholar 

  5. Beig, N., et al.: Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology 290, 783–792 (2018). https://doi.org/10.1148/radiol.2018180910

    Article  Google Scholar 

  6. Awan, R., et al.: Glandular morphometrics for objective grading of colorectal adenocarcinoma histology images. Sci. Rep. 7, 16852 (2017)

    Article  Google Scholar 

  7. Sirinukunwattana, K., et al.: Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer. Sci. Rep. 8, 13692 (2018)

    Article  Google Scholar 

  8. Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inform. 7, 29 (2016)

    Article  Google Scholar 

  9. Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318, 2199–2210 (2017)

    Article  Google Scholar 

  10. Goldberg, I.G., et al.: The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging. Genome Biol. 6, R47 (2005)

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Allan, C., et al.: OMERO: flexible, model-driven data management for experimental biology. Nat. Methods 9, 245–253 (2012)

    Article  Google Scholar 

  13. Burel, J.-M., et al.: Publishing and sharing multi-dimensional image data with OMERO. Mamm. Genome 26, 441–447 (2015)

    Article  Google Scholar 

  14. Williams, E., et al.: The image data resource: a bioimage data integration and publication platform. Nat. Methods 14, 775–781 (2017)

    Article  Google Scholar 

  15. Wilkinson, M.D., et al.: The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016)

    Article  Google Scholar 

  16. Goode, A., Gilbert, B., Harkes, J., Jukic, D., Satyanarayanan, M.: OpenSlide: a vendor-neutral software foundation for digital pathology. J. Pathol. Inform. 4, 27 (2013)

    Article  Google Scholar 

  17. Singh, R., Chubb, L., Pantanowitz, L., Parwani, A.: Standardization in digital pathology: supplement 145 of the DICOM standards. J. Pathol. Inform. 2, 23 (2011)

    Article  Google Scholar 

  18. Marques Godinho, T., Lebre, R., Silva, L.B., Costa, C.: An efficient architecture to support digital pathology in standard medical imaging repositories. J. Biomed. Inform. 71, 190–197 (2017)

    Article  Google Scholar 

  19. Li, S., et al.: Metadata management for high content screening in OMERO. Methods 96, 27–32 (2016)

    Article  Google Scholar 

  20. Leigh, R., et al.: OME Files-an open source reference library for the OME-XML metadata model and the OME-TIFF file format. bioRxiv, 088740 (2016)

    Google Scholar 

  21. Bankhead, P., et al.: QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017)

    Article  Google Scholar 

  22. Uhlén, M., et al.: Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015)

    Article  Google Scholar 

  23. Iudin, A., Korir, P.K., Salavert-Torres, J., Kleywegt, G.J., Patwardhan, A.: EMPIAR: a public archive for raw electron microscopy image data. Nat. Methods 13, 387–388 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

This work was funded by grants from the BBSRC (Ref: BB/P027032/1, BB/R015384/1) and the Wellcome Trust (Ref: 202908/Z/16/Z).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jason R. Swedlow .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Besson, S. et al. (2019). Bringing Open Data to Whole Slide Imaging. In: Reyes-Aldasoro, C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwattana, K. (eds) Digital Pathology. ECDP 2019. Lecture Notes in Computer Science(), vol 11435. Springer, Cham. https://doi.org/10.1007/978-3-030-23937-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-23937-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23936-7

  • Online ISBN: 978-3-030-23937-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics