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About

Codes for paper Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach.

Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., & Saarakkala, S. (2018). Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach. Scientific reports, 8(1), 1727.

Disclaimer

This branch is only for inference purposes. Re-training is possible only in the master branch!!!

Running the software

This code requires the fresh-most docker and docker compose installed.

Execute sh deploy.sh cpu to deploy the app on CPU. If you have installed nvidia-docker, you can also deploy on GPU. The inference is 3 times faster on GPU. To deploy on GPU, run sh deploy.sh gpu.

Be careful, this app carries all the dependencies and weighs around 10GB in total.

Technical documentation

The software is currently composed of four separate loosely-coupled services. Specifically, those are:

  1. KNEEL - Knee joint and landmark localization (https://arxiv.org/abs/1907.12237). REST microservice, port 5000.
  2. DeepKnee - Automatic KL grading (this work, https://www.nature.com/articles/s41598-018-20132-7). REST microservice running on port 5001.
  3. Backend broker - a NodeJS microservice implementing asynchronous communication between microservices and UI (socket.io). It runs on 5002 port.
  4. UI - User Interface implemented in ReactJS. This part runs on 5003.

The platform is designed so that it is possible to use KNEEL and DeepKnee separately. Both microservices expect a JSON with {dicom: <I64>}, where <I64> is the dicom file encoded in base64. If you make a request to either of the services, it needs to be done to /kneel/predict/bilateral or /deepknee/predict/bilateral for KNEEL and DeepKnee, respectively. A Jupyter Notebook request_example.ipynb demonstrates an example of such request.

License

This code is freely available only for research purposes. Commercial use is not allowed by any means. The provided software is not cleared for diagnostic purposes.

How to cite

@article{tiulpin2018automatic,
  title={Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach},
  author={Tiulpin, Aleksei and Thevenot, J{\'e}r{\^o}me and Rahtu, Esa and Lehenkari, Petri and Saarakkala, Simo},
  journal={Scientific reports},
  volume={8},
  number={1},
  pages={1727},
  year={2018},
  publisher={Nature Publishing Group}
}