This is the official released code for A Deep Reinforced Tree-traversal Agent for Coronary Artery Centerline Extraction. Here we released detailed codes and also a set of toy models in order to visualize the result. Please check the original paper (https://doi.org/10.1007/978-3-030-87240-3_40) for detailed ideas.
We only test codes under the following environment, other reasonable environment settings should work as well.
- Ubuntu 16.04
- CUDA 10.1
- Python 3.8
- Pytorch 1.6.0
- .....
pip install all other required libaries.
Download the example_data from the link: https://drive.google.com/file/d/1yeJIoBALUGasHyFHAijkNILTtjhwfGXx/view?usp=sharing. Then substitute the place-holder folder with the one you downloaded.
- To check the effect of the proposed method, run the inference through:
python tracer/inference.py- One can also run the train code with the toy data. However it's not likely to get any reasonbale result or weight:
python tracer/main.py- Train the discriminator with the following command. Still no sensable result is guaranteed:
python discriminator/main.pyFor those who are truly interested in DRL, please reference https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch for more details. And honestly speaking, this code is a little bit messy and surely there are more elegent ways to organize the code as well as data structure. However, due to many reasons (mainly because I am too lazy :ghost: :ghost: :ghost:) here we are. So try not to stuck in detailed codes. Feel free to contact me (lzvv123456@icloud.com) if you have any confusion.