(c) Aleksei Tiulpin, University of Oulu, 2019-2024

This repo contains an inference package for the models trained in our paper KNEEL. In that paper, we have developed a neural network architecture, which allows to accurately detect knee anatomical landmarks, and have validated the model on several datasets.

In this repo, we have included a web-app, which is dockerized, and can be accessed via http protocol.

The very first step to access our model, is getting access to the HuggingFace repo. The access will be approved automatically. Subsequently, in your settings generate a token. More on this can be found in the HuggingFace documnetation.

The token is required to get the KNEEL app running.

You need to have docker installed. If you want to use GPU, you must have the GPU runtime installed as well. Below is how you can run the code:

On CPU (slow, but works on all )

docker run -it --name kneel_api_cpu --rm \
  -v $(pwd)/tmp:/tmp/:rw -p 5000:5000 --ipc=host \
  imeds/kneel:cpu python -u -m kneel.api.app \
  --refine --jit_trace --deploy --device cpu \
  --hf_token <YOUR_HUGGING_FACE_TOKEN>

On GPU (a lot faster)

docker run -it --name kneel_api_gpu --rm --runtime=nvidia --gpus all\
  -v $(pwd)/tmp:/tmp/:rw -p 5000:5000 --ipc=host \
  imeds/kneel:gpu python -u -m kneel.api.app \
  --refine --jit_trace --deploy --device cuda:0 \
  --hf_token <YOUR_HUGGING_FACE_TOKEN>

Note: If you want to see the full logs, do

tail -f tmp/kneel.log

To make predictions, just send a POST request with a json having {"dicom":<RAW_DICOM_IN_BASE_64>} to /kneel/predict/bilateral. To encode a DICOM image in Python, read it as a binary file and then use standard python base64 library: base64.b64encode(dicom_binary).decode('ascii') to generate a base64 string. You can do this as follows (assuming that the microservice runs on localhost):

import requests
...
...
with open(img_path, "rb") as f:
    data_base64 = base64.b64encode(f.read()).decode('ascii')
response = requests.post("http://localhost:5000/kneel/predict/bilateral", json={'dicom': data_base64})

As a result, you will get an array of 16 anatomical landmarks in (x, y) format. Their meaning can be seen in the paper, Figure 1.

If any new dependencies are added, you can recompile the dockers as follows (from the main repo directory)

docker buildx build -t imeds/kneel:cpu -f docker/Dockerfile.cpu .
docker buildx build -t imeds/kneel:gpu -f docker/Dockerfile.gpu .

You must cite the following paper (Accepted to ICCV 2019 VRMI Workshop).

@inproceedings{9022083,
  author={Tiulpin, Aleksei and Melekhov, Iaroslav and Saarakkala, Simo},
  booktitle={2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)}, 
  title={KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks}, 
  year={2019},
  volume={},
  number={},
  pages={352-361},
  doi={10.1109/ICCVW.2019.00046}
}

The codes and the pre-trained models are not available for any commercial use including research for commercial purposes.