Glencoe has released a statement regarding the conversion of complex formats to pyramidal ome.tiff == The full post is here == https://www.glencoesoftware.com/blog/2019/12/09/converting-whole-slide-images-to-OME-TIFF.html == Workflow Summary == The Glencoe protocol describes a two-step process. 1. Convert vendor data to 'raw' data using `bioformats2raw` 2. Convert 'raw' data to ome.tiff using `raw2ometiff` The need for the two separate protocols seems to stem from licensing and dependencies on separate vendor-specific libraries. == Advantages == All reading and writing is streamed to the disk in parallel, making this the fastest conversion tool available. On a test with one RGB dataset in .mrxs format, it went from ~4h using [[ https://github.com/BIOP/ijp-kheops | KHEOPS ]] to about 10 minutes using these two tools. == Installation == NOTE: Because this protocol makes use of very modern file storage and compression systems, there is a dependency on a tool called `c-blosc`, and there is some compilation to do. == Installing `c-blosc` == === Dependencies === **Download **[[ https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019 | Build Tools for Visual Studio 2019 ]] Install the packages - `MSVC v142 - VS 2019 C++ x64/x86 build tools` - `Windows 10 SDK (10.0.18362.0)` **Download **[[ https://cmake.org/download/ | CMake ]] (msi installer) and install with defaults. NOTE: If you followed the StarDist with gpu-tools installation, you have all the prerequisites except for [[ https://cmake.org/download/ | CMake ]]. Otherwise please follow the installation of the [[ bioimaging_and_optics_platform_biop/computers-servers/software/gpu-deep-learning/#stardist-prerequisites | StarDist Prerequisites ]] === Installation === 1. Clone https://github.com/Blosc/c-blosc 2. Create a folder in your `C:\` drive called `Dev Tools` 2. Open a command line and do the following ``` cd c-blosc mkdir build cd build cmake -DCMAKE_INSTALL_PREFIX="C:\Dev Tools" cmake --build . --target install ``` === Installing `bioformats2raw` === Unzip this somewhere https://github.com/glencoesoftware/bioformats2raw/releases === Installing `raw2ometiff` === Unzip this somewhere https://github.com/glencoesoftware/raw2ometiff/releases === Adding to the PATH === Add the two unzipped `bin` folders to your path so you can call these two functions from anywhere == Using This workflow == Example: Dataset called `D:\ToConvert\Kidney.mrxs` which is RGB ``` bioformats2raw.bat --resolutions=4 D:\ToConvert\Kidney.mrxs D:\ToConvert\Kidney ``` Other arguments are available with bioformats2raw.bat --help Converting to OME.TIFF ``` raw2ome.tiff --compression="JPEG-2000" --rgb D:\ToConvert\kidney D:\ToConvert\Kidney.ome.tiff ``` IMPORTANT: RGB images must be explictely concerted using the `--rgb` flag. Otherwise it will be a 3 channel image.

Glencoe has released a statement regarding the conversion of complex formats to pyramidal ome.tiff == The full post is here == https://www.glencoesoftware.com/blog/2019/12/09/converting-whole-slide-images-to-OME-TIFF.html == Workflow Summary == The Glencoe protocol describes a two-step process. 1. Convert vendor data to 'raw' data using `bioformats2raw` 2. Convert 'raw' data to ome.tiff using `raw2ometiff` The need for the two separate protocols seems to stem from licensing and dependencies on separate vendor-specific libraries. == Advantages == All reading and writing is streamed to the disk in parallel, making this the fastest conversion tool available. On a test with one RGB dataset in .mrxs format, it went from ~4h using [[ https://github.com/BIOP/ijp-kheops | KHEOPS ]] to about 10 minutes using these two tools. == Installation == NOTE: Because this protocol makes use of very modern file storage and compression systems, there is a dependency on a tool called `c-blosc`, and there is some compilation to do. == Installing `c-blosc` == === Dependencies === **Download **[[ https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019 | Build Tools for Visual Studio 2019 ]] Install the packages - `MSVC v142 - VS 2019 C++ x64/x86 build tools` - `Windows 10 SDK (10.0.18362.0)` **Download **[[ https://cmake.org/download/ | CMake ]] (msi installer) and install with checking `Add CMAKE Path to all users`. NOTE: If you followed the StarDist with gpu-tools installation, you have all the prerequisites except for [[ https://cmake.org/download/ | CMake ]]. Otherwise please follow the installation of the [[ bioimaging_and_optics_platform_biop/computers-servers/software/gpu-deep-learning/#stardist-prerequisites | StarDist Prerequisites ]] === Installation === 1. Clone https://github.com/Blosc/c-blosc 2. Create a folder in your `C:\` drive called `Dev Tools` 2. Open a command line and do the following ``` cd c-blosc mkdir build cd build cmake -DCMAKE_INSTALL_PREFIX="C:\Dev Tools" cmake --build . --target install ``` === Installing `bioformats2raw` === Unzip this somewhere https://github.com/glencoesoftware/bioformats2raw/releases === Installing `raw2ometiff` === Unzip this somewhere https://github.com/glencoesoftware/raw2ometiff/releases === Adding to the PATH === Add the two unzipped `bin` folders to your path so you can call these two functions from anywhere == Using This workflow == Example: Dataset called `D:\ToConvert\Kidney.mrxs` which is RGB ``` bioformats2raw.bat --resolutions=4 D:\ToConvert\Kidney.mrxs D:\ToConvert\Kidney ``` Other arguments are available with bioformats2raw.bat --help Converting to OME.TIFF ``` raw2ome.tiff --compression="JPEG-2000" --rgb D:\ToConvert\kidney D:\ToConvert\Kidney.ome.tiff ``` IMPORTANT: RGB images must be explictely concerted using the `--rgb` flag. Otherwise it will be a 3 channel image.

Glencoe has released a statement regarding the conversion of complex formats to pyramidal ome.tiff == The full post is here == https://www.glencoesoftware.com/blog/2019/12/09/converting-whole-slide-images-to-OME-TIFF.html == Workflow Summary == The Glencoe protocol describes a two-step process. 1. Convert vendor data to 'raw' data using `bioformats2raw` 2. Convert 'raw' data to ome.tiff using `raw2ometiff` The need for the two separate protocols seems to stem from licensing and dependencies on separate vendor-specific libraries. == Advantages == All reading and writing is streamed to the disk in parallel, making this the fastest conversion tool available. On a test with one RGB dataset in .mrxs format, it went from ~4h using [[ https://github.com/BIOP/ijp-kheops | KHEOPS ]] to about 10 minutes using these two tools. == Installation == NOTE: Because this protocol makes use of very modern file storage and compression systems, there is a dependency on a tool called `c-blosc`, and there is some compilation to do. == Installing `c-blosc` == === Dependencies === **Download **[[ https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019 | Build Tools for Visual Studio 2019 ]] Install the packages - `MSVC v142 - VS 2019 C++ x64/x86 build tools` - `Windows 10 SDK (10.0.18362.0)` **Download **[[ https://cmake.org/download/ | CMake ]] (msi installer) and install with defaultschecking `Add CMAKE Path to all users`. NOTE: If you followed the StarDist with gpu-tools installation, you have all the prerequisites except for [[ https://cmake.org/download/ | CMake ]]. Otherwise please follow the installation of the [[ bioimaging_and_optics_platform_biop/computers-servers/software/gpu-deep-learning/#stardist-prerequisites | StarDist Prerequisites ]] === Installation === 1. Clone https://github.com/Blosc/c-blosc 2. Create a folder in your `C:\` drive called `Dev Tools` 2. Open a command line and do the following ``` cd c-blosc mkdir build cd build cmake -DCMAKE_INSTALL_PREFIX="C:\Dev Tools" cmake --build . --target install ``` === Installing `bioformats2raw` === Unzip this somewhere https://github.com/glencoesoftware/bioformats2raw/releases === Installing `raw2ometiff` === Unzip this somewhere https://github.com/glencoesoftware/raw2ometiff/releases === Adding to the PATH === Add the two unzipped `bin` folders to your path so you can call these two functions from anywhere == Using This workflow == Example: Dataset called `D:\ToConvert\Kidney.mrxs` which is RGB ``` bioformats2raw.bat --resolutions=4 D:\ToConvert\Kidney.mrxs D:\ToConvert\Kidney ``` Other arguments are available with bioformats2raw.bat --help Converting to OME.TIFF ``` raw2ome.tiff --compression="JPEG-2000" --rgb D:\ToConvert\kidney D:\ToConvert\Kidney.ome.tiff ``` IMPORTANT: RGB images must be explictely concerted using the `--rgb` flag. Otherwise it will be a 3 channel image.