Phriction Projects Wikis Bioimaging And Optics Platform Image Processing QuPath Optimized Pyramidal OME.TIFF Conversion for Whole Slide Images History Version 10 vs 21
Version 10 vs 21
Version 10 vs 21
Content Changes
Content Changes
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
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
```
raw2ometiff.bat --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
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
```
raw2ometiff.bat --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.
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