RoCoSDF: Row-Column Scanned Neural Signed Distance Fields for Freehand 3D ultrasound Imaging Shape Reconstruction

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The official implementation code for MICCAI 2024 paper: RoCoSDF: Row-Column Scanned Neural Signed Distance Fields for Freehand 3D Ultrasound Imaging Shape Reconstruction by Hongbo Chen, Yuchong Gao, Shuhang Zhang, Jiangjie Wu, Yuexin Ma and Rui Zheng.

RoCoSDF is a framework built on neural implicit signed distance functions for shape reconstruction of multi-view freehand 3D ultrasound imaging.

Demo

• Thoracic Vertebra T4 from ultrasound transducer 1 (UT1)

• The example mesh results of RoCoSDF are in outs/T4_RoCo/outputs/*.ply.

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Usage

Our code is implemented in NVIDIA 3090, Ubuntu 18/20, Python 3.8, PyTorch 1.12.1 and CUDA 11.6.

Install Dependencies

For 20/30x GPU:

conda create -n rocosdf python=3.8
conda activate rocosdf
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge
pip install tqdm pyhocon==0.3.57 trimesh PyMCubes scipy matplotlib
pip install visdom open3d scikit-image plyfile

For 40x GPU, cuda 11.8:

conda create -n rocosdf python=3.10
conda activate rocosdf
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install tqdm pyhocon==0.3.57 trimesh PyMCubes scipy matplotlib
pip install visdom open3d scikit-image plyfile

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Data Preparation

• Convert the row-scan and column-scan segmented volumetric mask to point clouds file *.ply.

• Both the row-scan and column-scan point clouds should be in a same tracking space or manually aligned in a unified space.

• Put the row-scan and column-scan point clouds data in ./data.

RoCoSDF/
│
├── data/
│   ├── T4_Co.ply            % your own data
|   ├── T4_Ro.ply            % your own data
│   ├── T4_Co_ds.pt          % generated during data preprocessing, downsampled point clouds for training
|   ├── T4_Ro_ds.pt          % generated during data preprocessing, downsampled point clouds for training
│   ├── T4_Co_sampler.pt     % generated during training
|   ├── T4_Ro_sampler.pt     % generated during training
|
|
├── outs/
│   ├── T4_Co/
│       └── outputs/
|           └── *.ply
│   ├── T4_Ro/
│       └── outputs/
|            └── *.ply
│   ├── T4_RoCo/
│       └── outputs/
|            └── *.ply
|

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Run RoCoSDF

In Linux, directly train the model through sh train.sh OR using command as below.

python runRoCoSDF.py --gpu 0  --conf confs/conf.conf --dataname T4_Co --dataname2 T4_Ro  --dir T4_Co --dir2 T4_Ro --dir3 T4_RoCo --mode train

Run SDF Refinement Only

In Linux, directly train the model through sh train_refine_only.sh OR using command as below.

python runRoCoSDF.py --gpu 0  --conf confs/conf.conf --dataname T4_Co --dataname2 T4_Ro --dir T4_Co --dir2 T4_Ro --dir3 T4_RoCo --mode train_refine

Contact

For any queries, please contact chenhb[at]shanghaitech.edu.cn.

Citation

If you use RoCoSDF in your research, please cite the paper:

 @InProceedings{chenRoCoSDF,
   author="Chen, Hongbo
   and Gao, Yuchong
   and Zhang, Shuhang
   and Wu, Jiangjie
   and Ma, Yuexin
   and Zheng, Rui",
   title="RoCoSDF: Row-Column Scanned Neural Signed Distance Fields for Freehand 3D Ultrasound Imaging Shape Reconstruction",
   booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024",
   year="2024",
   publisher="Springer Nature Switzerland",
   address="Cham",
   pages="721--731",
   isbn="978-3-031-72083-3"
   }

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References

The reference codes are from the following links. We appreciate all the contributors.

• FUNSR(UNSR in the proceeding): https://github.com/chenhbo/FUNSR

• DeepSDF: https://github.com/facebookresearch/DeepSDF

• NeuralPull: https://github.com/mabaorui/NeuralPull-Pytorch

• GenSDF: https://github.com/princeton-computational-imaging/gensdf

• CSGSDF: https://github.com/zoemarschner/csg_on_nsdf