Converting Whole Slide Images to OME-TIFF: A New Workflow

Glencoe Software and the OME team are pleased to announce a new collection of command line tools [1,2,3] for converting whole slide images from proprietary file formats into pyramidal OME-TIFF; an open file format. These tools were initially designed to convert Philips’ iSyntax and 3DHISTECH’s .mrxs file formats, but can also be used with any other whole slide format supported by Bio-Formats.

A full writeup can be found on the Glencoe Software blog here:

https://www.glencoesoftware.com/blog/2019/12/09/converting-whole-slide-images-to-OME-TIFF.html

We look forward to your feedback and comments. Our thanks to InnovateUK for the support that made this work possible.

[1] https://github.com/glencoesoftware/isyntax2raw
[2] https://github.com/glencoesoftware/bioformats2raw
[3] https://github.com/glencoesoftware/raw2ometiff

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We have updated the blog post today to acknowledge additional support from iCAIRD and PathLAKE.

Great work, this is very much needed! While the code that comes with the Philips SDK lets us data for AI processing, we had no good solutions for pixel-level annotation of iSyntax-derived files.

As mentioned in the blog post, the isyntax2raw conversion generates a great deal of data. For a 1.3G iSyntax file, the raw output might 20X the original size. I would expect large output for a raw extraction, which is fine if we don’t intend to store the data. I noticed that the raw2ometiff also produces a rather large file compared to the iSyntax, perhaps 10X the size. Is such a large format expected for a OME-TIFF? I understand there is no accepted standard, but how are people storing such large derived files, if this is in fact normal?

Discussion on compression has continued here:

Just to add to what @melissa has already said on the GitHub issue, iSyntax files are lossily compressed using Philips’ proprietary wavelet compression scheme. You can read more about it here:

raw2ometiff is not opinionated on compression choice and there are a variety of compression options available including LZW (default), JPEG, JPEG-2000 lossless and JPEG-2000 lossy. As our original article says:

Tile sizes, compression as well as file format are configurable putting these choices in the hands of clinicians, experimentalists, data scientists, and IT professionals.

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I am closing this topic and I propose we use either new dedicated posts on the image.sc forum or GitHub issues to report issues or bring up questions about the usage of these tools. Thanks for all the feedback so far.