Offical Code Implementation for Forplane and Lerplane

LerPlane

LerPlane: Neural Representations for Fast 4D Reconstruction of Deformable Tissues
Chen Yang, Kailing Wang, Yuehao Wang, Xiaokang Yang, Wei Shen
MICCAI2023, Young Scientist Award

Forplane

Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane
Chen Yang, Kailing Wang, Yuehao Wang, Qi Dou, Xiaokang Yang, Wei Shen
TMI2024

Schedule

• Initial Code Release of Lerplane.
• Further check of the reproducibility.
• Hamlyn Dataset.
• Code clean and release for Forplane.
• All the released code is tested on Ubuntu 22.04, Python 3.9, Pytorch 1.13.1, CUDA 11.7.

Guidelines

This is the code repo for the paper "LerPlane: Neural Representations for Fast 4D Reconstruction of Deformable Tissues" and "Forplane: Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane". Since the Forplane is qualitatively and faster than Lerplane, we recommend you to use Forplane for your research, which is also the default setting in this repo. But if you want to reproduce the performance of Lerplane, you may modify the config file in the forplanes/config folder. For the dataset, please refer to the Instructions-Set up datasets section below. For the evaluation setting, please refer to the Instructions-Evaluation section below.

trainging_speed_vs_endo.mp4

Instructions

Set up the Python environment

Tested with an Ubuntu workstation i9-12900K, 3090GPU.

conda create -n lerplane python=3.9
conda activate lerplane
pip install -r requirements.txt
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch 

We notice tiny-cuda-nn is sometimes not compilable on some latest GPUs like RTX4090(tested 2023.1). If you found OSError while installing tiny-cuda-nn, you can refer to this issue or this article. We've successfully built the same env on an Ubuntu 22.04 workstation i7-13700K, 4090GPU with the commands above.

Set up datasets

Download the datasets

Please download the dataset from EndoNeRF

Download the Hamlyn dataset used in Forplane: hamlyn_forplane

To use the example config, organize your data like:

data
| - endonerf_full_datasets
|   | - cutting_tissues_twice
|   | - pushing_soft_tissues
| - hamlyn_forplane
|   | - hamlyn1
|   | - hamlyn2
| - YourCustomDatasets

If you want to generate your own dataset, we strongly recommend you to undisort the images first since most endoscopic images are distorted. You can use the cv2.undistort function in OpenCV to do this. And we also recommend you to use video-based SAM model to generate the masks of instruments for consistency.

Training

Using configs for training

Ferplane uses configs to control the training process. The example configs are stored in the forplanes/config folder. To run Lerplane, you need to modify the config according to the paper, but we recommend you use Forplane. To train a model, run the following command:

export CUDA_VISIBLE_DEVICES=0
PYTHONPATH='.' python forplanes/main.py --config-path forplanes/config/example-9k.py

Evaluation

We use the same evaluation protocol as EndoNeRF. So please follow the instructions in EndoNeRF.

Acknowledgements

We would like to acknowledge the following inspiring work:

• EDSSR (Long et al.)
• EndoNeRF (Wang et al.)
• K-Planes (Fridovich-Keil et al.)

Big thanks to NeRFAcc (Li et al.) for their efficient implementation, which has significantly accelerated our rendering.

Citation

If you find this code useful for your research, please use the following BibTeX entries:

@inproceedings{yang2023neural,
  title={Neural lerplane representations for fast 4d reconstruction of deformable tissues},
  author={Yang, Chen and Wang, Kailing and Wang, Yuehao and Yang, Xiaokang and Shen, Wei},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={46--56},
  year={2023},
  organization={Springer}
}

and

@article{yang2024efficient,
  title={Efficient deformable tissue reconstruction via orthogonal neural plane},
  author={Yang, Chen and Wang, Kailing and Wang, Yuehao and Dou, Qi and Yang, Xiaokang and Shen, Wei},
  journal={IEEE Transactions on Medical Imaging},
  year={2024},
  publisher={IEEE}
}