Pathology Tiger Algorithm Example

Example algorithm and docker for the TIGER challenge

Requirements

• Ubuntu software

  • Ubuntu20.04
  • ASAP 2.0

• Python packages

  • numpy==1.20.2
  • tqdm==4.62.3

Summary of the files in package

The packages consist of the following python files.

__init__

This is an empty file used for the initialization of the package directory.

__main__

Contains code for calling the package as a module. Runs the process function from the processing file.

gcio

Contains code that deals with grand challenge input and output. It includes predefined input and output paths.

rw

Contains code for reading and writing. Includes function for reading a multi resolution image. Furthermore, it includes classes for writing required files for the challenge, namely: segmentation mask file, detection JSON file, and TILS score file.

processing

Main processing file. Includes code for processing a slide and applies process functions to generate a segmentation mask, detections, and a TILS score. Note that the processing functions for each task are only made for illustration purposes and should not be taken as valid processing steps.

Setup

A simple and minimal setup file is included to install the package via pip. Note that the package is not in the PyPI repository.

Dockerfile

Dockerfile to be build and uploaded to grand-challenge. It installs

• Ubuntu20.04,
• python3.8-venv,
• ASAP2.0,
• tigeralgorithmexample + requirements

As an entry point, the __main__ file will be run; hence process function from the processing file will be called.

If you want to use a GPU, please change in the Dockerfile:

• FROM ubuntu:20.04 -> FROM nvidia/cuda:11.1-runtime-ubuntu20.04

Include your own code

If you use this repository as a starting point. Please change the following three functions and implement your own models/pipeline

• segmentation:

  pathology-tiger-algorithm-example/tigeralgorithmexample/processing.py

  Line 26 in f1e098c

  def process_image_tile_to_segmentation(

• detection:

  pathology-tiger-algorithm-example/tigeralgorithmexample/processing.py

  Line 48 in f1e098c

  def process_image_tile_to_detections(

• tils-score:

  pathology-tiger-algorithm-example/tigeralgorithmexample/processing.py

  Line 74 in f1e098c

  def process_segmentation_detection_to_tils_score(

Depending on the type of model and settings you are using, you might want or need to change the processing function.

Test and Export

To test if your algorithm works and (still) produces the correct outputs you add an image to ./testinput/ and a corresponding tissue mask in ./testinput/images/

After the image and the tissue background are present in the test and test/images folder, you can run the following command to build and test the docker:

./test.sh

If you want to test with gpus, please add --gpus all to the docker run command in ./test.sh

This will build the docker, run the docker and check if the required output is present. Furthermore, it will check if the detected_lymphocytes.json is in valid json format. When there are no complaints in the output you can export the algorithm to an .tar.xz file with the following command:

./export.sh

The resulting .tar.xz file can be uploaded to the grand-challenge platform