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.
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References
Udall, M., et al.: PD-L1 diagnostic tests: a systematic literature review of scoring algorithms and test-validation metrics. Diagn. Pathol. 13, 12 (2018)
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)
Goltsev, Y., et al.: Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018)
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)
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
Awan, R., et al.: Glandular morphometrics for objective grading of colorectal adenocarcinoma histology images. Sci. Rep. 7, 16852 (2017)
Sirinukunwattana, K., et al.: Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer. Sci. Rep. 8, 13692 (2018)
Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inform. 7, 29 (2016)
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)
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)
Linkert, M., et al.: Metadata matters: access to image data in the real world. J. Cell Biol. 189, 777–782 (2010)
Allan, C., et al.: OMERO: flexible, model-driven data management for experimental biology. Nat. Methods 9, 245–253 (2012)
Burel, J.-M., et al.: Publishing and sharing multi-dimensional image data with OMERO. Mamm. Genome 26, 441–447 (2015)
Williams, E., et al.: The image data resource: a bioimage data integration and publication platform. Nat. Methods 14, 775–781 (2017)
Wilkinson, M.D., et al.: The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016)
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)
Singh, R., Chubb, L., Pantanowitz, L., Parwani, A.: Standardization in digital pathology: supplement 145 of the DICOM standards. J. Pathol. Inform. 2, 23 (2011)
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)
Li, S., et al.: Metadata management for high content screening in OMERO. Methods 96, 27–32 (2016)
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)
Bankhead, P., et al.: QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017)
Uhlén, M., et al.: Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015)
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)
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).
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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
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DOI: https://doi.org/10.1007/978-3-030-23937-4_1
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