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README.md

Pathology Sample Application

A reference app to run inference + train tasks to segment Nuclei. This app works in both Digital Slide Archive (DSA) and QuPath Viewers, but we recommend using DSA as an endpoint for studies; however, you can also use the local datastore as a studies folder.

Table of Contents

Supported Viewers

The Pathology Sample Application supports the following viewers:

For more information on each of the viewers, see the plugin extension folder for the given viewer.

Installation Requirements

MONAI Label for Pathology requires an additional dependency of OpenSlide or CuCIM. Make sure that any .dll or .so files are in the system load path.

For Windows, make sure <openslide_folder>/bin is added in PATH environment.

For Ubuntu: apt install openslide-tools or pip install cucim

Pretrained Models

The following are the models which are currently added into Pathology App:

Name Description
Segmentation Nuclei An example of multi-label segmentation for the following labels: Neoplastic, Inflammatory, Connective/Soft Tissue, Dead, and Epithelial.
DeepEdit Nuclei It is a combination of both Interaction + Auto Segmentation model, trained to segment Nuclei cells that combines all cell types from the standard segmentation model above as Nuclei.
NuClick This is NuClick implementation (UNet model) as provided at: https://github.com/mostafajahanifar/nuclick_torch. Training task for monailabel is not yet supported.
Model Details (dataset, input, outputs)

Dataset

The above Nuclei models are trained on PanNuke Dataset for Nuclei Instance Segmentation and Classification

Inputs

  • WSI Images
  • Region (ROI) of WSI Image

Output

Segmentation Mask is produced in one of the following formats:

How to Use the App

The following commands are examples of how to start the Pathology Sample Application. Make sure when you're running the command that you use the correct app and studies path for your system.

# Download Pathology App (skip this if you have already downloaded the app or using github repository (dev mode))
monailabel apps --download --name pathology --output workspace

# Start MONAI Label Server with all models
monailabel start_server --app workspace/pathology --studies datasets/wsi

# Start MONAI Label Server with all models and use specific bundle versions
monailabel start_server --app workspace/pathology --studies datasets/wsi --conf hovernet_nuclei 0.1.8 --conf nuclick 0.1.0 --conf classification_nuclei 0.1.0

# Start MONAI Label Server with HoVerNet Nuclei model
monailabel start_server --app workspace/pathology --studies datasets/wsi --conf models hovernet_nuclei

# Start MONAI Label Server with multiple models
monailabel start_server --app workspace/pathology --studies datasets/wsi

# Start MONAI Label Server with HoVerNet Nuclei model and preload on GPU
monailabel start_server --app workspace/pathology --studies datasets/wsi --conf models hovernet_nuclei --conf preload true

# Start MONAI Label Server with HoVerNet Nuclei in Inference Only mode
monailabel start_server --app workspace/pathology --studies datasets/wsi --conf models hovernet_nuclei --conf skip_trainers true

Usage (Development Mode)

git clone https://github.com/Project-MONAI/MONAILabel.git
cd MONAILabel
pip install -r requirements.txt

Install Openslide:

Install Openslide binaries manually and make sure .dll or .so files for openslide are in system load path.

  • For windows, make sure <openslide_folder>/bin is added in PATH environment.
  • For ubuntu: apt install openslide-tools

FileSystem as Datastore

# download sample wsi image (skip this if you already have some)
mkdir sample_wsi
cd sample_wsi
wget https://demo.kitware.com/histomicstk/api/v1/item/5d5c07539114c049342b66fb/download
cd -

# run server
./monailabel/scripts/monailabel start_server --app sample-apps/pathology --studies datasets/wsi

Performance Benchmarking

The performance benchmarking is done using the MONAILabel server and DSA client. All the details are captured in a Google Sheet.

The following graph shows a summary of:

  • NucleiDetection (CPU-Based DSA Algorithm)
  • Segmentation/DeepEdit (MONAILabel models)

Digital Slide Arhive (DSA) as Datastore

DSA

You need to install DSA and upload some test images. Refer: https://github.com/DigitalSlideArchive/digital_slide_archive/tree/master/devops/dsa

MONAILabel Server

Following are some config options:

Name Description
preload Preload models into GPU. Default is False.
roi_size Default ROI Size for inference in [x,y] format. Default is [1024,1024].
dsa_folder Optional. Comma seperated DSA Folder IDs. Normally it is <folder_id> of a folder under Collections where Images are stored.
dsa_api_key Optional. API Key helps to query asset store to fetch direct local path for WSI Images.
dsa_asset_store_path Optional. It is the DSA assetstore path that can be shared with MONAI Label server to directly read WSI Images. 
  # run server (Example: DSA API URL is http://0.0.0.0:8080/api/v1)
  ./monailabel/scripts/monailabel start_server --app sample-apps/pathology \
    --studies http://0.0.0.0:8080/api/v1 \

  # run server (Advanced options)
  ./monailabel/scripts/monailabel start_server --app sample-apps/pathology \
    --studies http://0.0.0.0:8080/api/v1 \
    --conf dsa_folder 621e94e2b6881a7a4bef5170 \
    --conf dsa_api_key OJDE9hjuOIS6R8oEqhnVYHUpRpk18NfJABMt36dJ \
    --conf dsa_asset_store_path digital_slide_archive/devops/dsa/assetstore