Bringing Open Data to Whole Slide Imaging

Besson, Sébastien; Leigh, R. A.; Linkert, Melissa; Allan, Chris; Burel, Jean‐Marie; Carroll, Mark; Gault, David; Gozim, Riad; Li, Simon; Lindner, Dominik; Moore, Josh; Moore, William J.; Walczysko, Petr; Wong, Frances; Swedlow, Jason R.

doi:10.1007/978-3-030-23937-4_1

Cited by 47 publications

(37 citation statements)

References 26 publications

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“…For post-acquisition image processing, the image analysis software Fiji was used 48 . For data handling of whole-slide images, an OME-TIFF workflow was used 49 .…”

Section: Articlesmentioning

confidence: 99%

Olfactory transmucosal SARS-CoV-2 invasion as a port of central nervous system entry in individuals with COVID-19

et al. 2020

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here is increasing evidence that SARS-CoV-2 not only affects the respiratory tract but also impacts the CNS, resulting in neurological symptoms such as loss of smell and taste, headache , fatigue, nausea and vomiting in more than one-third of individuals with COVID-19 (refs. 1,2). Moreover, acute cerebrovascular disease and impaired consciousness have been reported 3. While Olfactory transmucosal SARS-CoV-2 invasion as a port of central nervous system entry in individuals with COVID-19

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“…For post-acquisition image processing, the image analysis software Fiji was used 48 . For data handling of whole-slide images, an OME-TIFF workflow was used 49 .…”

Section: Articlesmentioning

confidence: 99%

Olfactory transmucosal SARS-CoV-2 invasion as a port of central nervous system entry in individuals with COVID-19

et al. 2020

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“…A distinct approach --converting data from PFFs to a common, well-defined format --solves the computational demands of repeated and real-time image translation but requires a format that has broad application and utility, long-term stability and multiple open-source implementations and the support of the community. Some success has been achieved with OME-TIFF, a 2D multi-resolution image format that captures acquisition metadata as OME-XML in the TIFF header 2,7,8 . Reference software implementations are available in Java (https://github.com/ome/bioformats/), C++ (https://gitlab.com/rleigh/ome-files-cpp) and Python (e.g., https://github.com/AllenCellModeling/aicsimageio, https://github.com/apeer-micro/apeer-ometiff-library, https://github.com/cgohlke/tifffile).…”

Section: Permanent Conversionmentioning

confidence: 99%

OME-NGFF: scalable format strategies for interoperable bioimaging data

Moore

Allan

Besson

et al. 2021

Preprint

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Biological imaging is one of the most innovative fields in the modern biological sciences. New imaging modalities, probes, and analysis tools appear every few months and often prove decisive for enabling new directions in scientific discovery. One feature of this dynamic field is the need to capture new types of data and data structures. While there is a strong drive to make scientific data Findable, Accessible, Interoperable and Reproducible (FAIR, 1), the rapid rate of innovation in imaging impedes the unification and adoption of standardized data formats. Despite this, the opportunities for sharing and integrating bioimaging data and, in particular, linking these data to other "omics" datasets have never been greater; therefore, to every extent possible, increasing "FAIRness" of bioimaging data is critical for maximizing scientific value, as well as for promoting openness and integrity. In the absence of a common, FAIR format, two approaches have emerged to provide access to bioimaging data: translation and conversion. On-the-fly translation produces a transient representation of bioimage metadata and binary data but must be repeated on each use. In contrast, conversion produces a permanent copy of the data, ideally in an open format that makes the data more accessible and improves performance and parallelization in reads and writes. Both approaches have been implemented successfully in the bioimaging community but both have limitations. At cloud-scale, those shortcomings limit scientific analysis and the sharing of results. We introduce here next-generation file formats (NGFF) as a solution to these challenges.

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“…DZI can be created from a large number of different scanner formats thanks to the Open Microscopy Environment ( Besson et al , 2019 ) and the efforts of several open tools, specifically: VIPS ( Martinez and Cupitt, 2005 ), OpenSlide and Bio-Formats ( Linkert et al , 2010 ).…”

Section: Implementation Deployment and Usementioning

confidence: 99%

TissUUmaps: interactive visualization of large-scale spatial gene expression and tissue morphology data

2020

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Motivation Visual assessment of scanned tissue samples and associated molecular markers, such as gene expression, requires easy interactive inspection at multiple resolutions. This requires smart handling of image pyramids and efficient distribution of different types of data across several levels of detail. Results We present TissUUmaps, enabling fast visualization and exploration of millions of data points overlaying a tissue sample. TissUUmaps can be used both as a web service or locally in any computer, and regions of interest as well as local statistics can be extracted and shared among users. Availability and implementation TissUUmaps is available on github at github.com/wahlby-lab/TissUUmaps. Several demos and video tutorials are available at http://tissuumaps.research.it.uu.se/howto.html. Supplementary information Supplementary data are available at Bioinformatics online.

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Bringing Open Data to Whole Slide Imaging

Cited by 47 publications

References 26 publications

Olfactory transmucosal SARS-CoV-2 invasion as a port of central nervous system entry in individuals with COVID-19

Olfactory transmucosal SARS-CoV-2 invasion as a port of central nervous system entry in individuals with COVID-19

OME-NGFF: scalable format strategies for interoperable bioimaging data

TissUUmaps: interactive visualization of large-scale spatial gene expression and tissue morphology data

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