Creating montages from whole slide images (WSIs)

For working with large amounts of histology data, it is often useful to create montages of the data. Montages are a collection of images that are stitched together to form a single image. Montages are useful for visualizing large amounts of data at once, and can be used to create a single image that can be used for analysis. The hi-ml-cpath toolbox contains scripts that help with the creation of montages from whole slide images (WSIs).

Creating montages can be very time-consuming. It can hence be helpful to run the process in the cloud. The montage creation code provided here can be run in AzureML very easily.

Types of data for montage creation

  1. Montages can be created from a folder of images, by specifying the name of the folder and a glob pattern, like **/foo_*.tiff.

  2. Montages can be created by first reading a file called dataset.csv located in a folder. dataset.csv is effectively a Pandas DataFrame, with each row corresponding to a single image. More details on the format of dataset.csv can be found below.

Montage creation works for all WSI image formats that are supported by either of the two possible backends:

  • openslide supports .tif(f), .ndpi, .scn and others

  • cucim supports .svs, .tiff and others


  • Check out the hi-ml repository via git clone

  • Run the following commands:

cd hi-ml-cpath
make env
conda activate HimlHisto
make pip_local

All the commands listed below assume that

  • you have activated the Conda environment HimlHisto

  • your current working directory is <repo root>/hi-ml-cpath

Creating montages from a folder with files

The following command will create a montage from all files in the folder /data that match the pattern **/*.tiff.

python src/health_cpath/scripts/ --dataset /data --image_glob_pattern '**/*.tiff' --level 2 --width 1000 --output_path montage1

This will create a montage from all TIFF files in folder /data. Each TIFF file is read as a multi-level image, and level 2 is read for creating the montage.

The --width argument determines the width in pixel of the output image. The height of the output image is determined automatically. Note that the width given here, 1000, is suitable only for montages from a very small number of files (say, 10). See below for more details on this commandline option.

This will create two images in the folder specified by the --output_path montage1 argument, hence outputting the files montage1/montage.jpg and montage1/montage.png.

Here’s an example how this could look like for a folder with 6 images, 0.tiff through 5.tiff:


Creating montages from a dataset.csv file

If the montage creation script is only pointed to a folder, without providing a glob pattern, it assumes that a file dataset.csv is present. A montage will be created from only the images listed in dataset.csv. In addition, an optional label column will be added to the text that is overlayed onto the images itself.

The dataset file should be a CSV file, with each row corresponding to a single image. When working with a dataset.csv file, the following columns are handled:

Column name




The path of the image that should be loaded



A unique identifier for the slide



An additional string that will be placed on the montage, This could be 0, 1, tumour, …



The path of an additional image that will rendered next to the image given in image


Consider this example dataset file:

2.tiff,ID 2,Label 2
3.tiff,ID 3,Label 3
4.tiff,ID 4,Label 4
5.tiff,ID 5,Label 5

Run montage creation with the following command:

python src/health_cpath/scripts/ --dataset /data --level 2 --width 1000 --output_path montage1

This would produce (assuming that the images 2.tiff, 3.tiff, 4.tiff, and 5.tiff are present in the folder /data) a montage similar to this one:


Using inclusion or exclusion lists

When creating montages from a dataset.csv file, it is possible to create montages from only a specific subset of rows, or all rows apart from those in a given list.

  • Use the --exclude_by_slide_id exclude.txt argument to point to a file with a list of slide IDs that should be excluded from the montage.

  • Use the --include_by_slide_id include.txt argument to point to a file with a list of slide IDs for which the montage should be created.

The files exclude.txt and include.txt should contain one slide ID per line.

Other commandline options

  • Use --width=20000 to set the width of the output montage image. The height of the output image is determined automatically. Mind that the default value is 60_000, suitable for several hundreds of input images. If you want to try out montage creation on a small set of files (say, 10), ensure that you set the width to a reasonably small value, like --width=1000

  • Use --parallel=2 to specify the number of parallel processes that should be used for creating image thumbnails. Thumbnails are created in a first step, using multiple processes, and then the thumbnails are stitched into the final montage in the main process.

  • Use --backend=cucim to switch the image reader backend to CuCIM. The default backend is openslide.

Running in Azure

The script can be run in AzureML by adding 3 commandline arguments.

To set up Azure and AzureML:

  • Follow the steps in the AzureML onboarding.

  • At the end of the onboarding you will download a file config.json from your AzureML workspace to your repository root folder.

  • To understand datasets, please read through the AzureML datasets documentation. Then create an AzureML datastore that points to your Azure Blob Storage account.

  • Upload your WSIs to a folder in Azure Blob Storage. This can be done most efficiently via azcopy. azcopy can also copy directly across cloud providers, for example from AWS to Azure.

The following command will upload all files in the folder my_test_slides to a container datasets in your Azure Blob Storage account called mystorage, creating a folder my_test_slides in the storage account in the process:

azcopy copy my_test_slides --recursive

The following command will then create a run in AzureML that executes montage creation from that folder:

python src/health_cpath/scripts/ --dataset my_test_slides --level 2 --width 1000 --cluster <clustername> --conda_env environment.yml --datastore <datastorename>

In this command, replace the following:

  • Replace my_test_slides with the name of the folder in blob storage where you uploaded your WSIs.

  • clustername is the name of a compute cluster where your job will execute)

  • datastorename is the name of an AzureML datastore, essential a pointer to your blob storage account plus the credentials that are necessary to access it. For the above example, the data store needs to point to storage account mystorage and container datasets.

The command above will only run for a minute or less - it will mostly create a snapshot of the code and send that off to the cloud for execution. At the end you will see a link printed out that takes you to the AzureML portal, where you can monitor the progress of the run.

Once the run is completed, you will find two files montage.jpg and montage.png in the tab “Outputs” of the run, and an option to download it to your machine.