LiverMatch - Learning Feature Descriptors for Pre- and Intra-operative Point Cloud Matching for Laparoscopic Liver Registration

Introduction

In this project, we show promising results of using learning-based descriptors for laparoscopic liver registration (LLS).

Install

conda create -n match python==3.8
conda activate match
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
conda install pytorch
pip install PyYAML
conda install scipy
pip install easydict
pip install tensorboardX
pip install tqdm
pip install -U scikit-learn
pip install mayavi
pip install PyQt5
pip install open3d
cd cpp_wrappers; sh compile_wrappers.sh; cd ..

Run

Please change the paths in the following files and run:

python train.py configs/liver.yaml

python eval.py

python demos/PBSM-inSilicoData_demo.py # The weight is included in the snapshot. 

Dataset

The simulated dataset uses the 3D-IRCADb-01 dataset under the license CC BY-NC-ND 4.0. In this license, we should follow "NoDerivatives".

We will release a larger dataset under the license CC BY-SA 4.0, which allows modifications.

Please feel free to send an email to yy8898@rit.edu for questions.

Citation

@article{yang2023learning,
  title={Learning feature descriptors for pre-and intra-operative point cloud matching for laparoscopic liver registration},
  author={Yang, Zixin and Simon, Richard and Linte, Cristian A},
  journal={International Journal of Computer Assisted Radiology and Surgery},
  pages={1--8},
  year={2023},
  publisher={Springer}
}

Acknowledgements

• Lepard
• PREDATOR
• [V2S-Net] (https://gitlab.com/nct_tso_public/Volume2SurfaceCNN) Deformation simulation.
• Liver registration dataset https://opencas.webarchiv.kit.edu/?q=PhysicsBasedShapeMatching
• Liver segmentation 3D-IRCADb-01 https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01/