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--- a/README.md +++ b/README.md @@ -1,145 +1,145 @@ -## RoCoSDF: Row-Column Scanned Neural Signed Distance Fields for Freehand 3D ultrasound Imaging Shape Reconstruction --------------------------------------- - -The official implementation code for MICCAI 2024 paper: -[RoCoSDF: Row-Column Scanned Neural Signed Distance Fields for Freehand 3D Ultrasound Imaging Shape Reconstruction](https://chenhbo.github.io/RoCoSDF/) -by [Hongbo Chen](https://chenhbo.github.io/), Yuchong Gao, Shuhang Zhang, Jiangjie Wu, [Yuexin Ma](https://yuexinma.me/) and [Rui Zheng](https://sist.shanghaitech.edu.cn/zhengrui_en/main.htm). - - -RoCoSDF is a framework built on neural implicit signed distance functions for shape reconstruction of multi-view freehand 3D ultrasound imaging. - -<div align="center"> -<img src="img/Fig_RoCoScan.png" style="zoom:15%" alt="Data Aquisition Protocol"/> -</div> - - - - -## Demo -* Thoracic Vertebra T4 from ultrasound transducer 1 (UT1) - -<div align="center"> -<img src="img/Fig_Result_T4.png" style="zoom:14.7%" alt="Framework"/> -</div> - - -<br /> - - -* The example mesh results of RoCoSDF are in `outs/T4_RoCo/outputs/*.ply`. - - --------------------------------------- - -## 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 -``` --------------------------------------- - -### 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 -| -``` - --------------------------------------- - -### 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](mailto:chenhb@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" - } -``` --------------------------------------- - -### 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 +## RoCoSDF: Row-Column Scanned Neural Signed Distance Fields for Freehand 3D ultrasound Imaging Shape Reconstruction +-------------------------------------- + +The official implementation code for MICCAI 2024 paper: +[RoCoSDF: Row-Column Scanned Neural Signed Distance Fields for Freehand 3D Ultrasound Imaging Shape Reconstruction](https://chenhbo.github.io/RoCoSDF/) +by [Hongbo Chen](https://chenhbo.github.io/), Yuchong Gao, Shuhang Zhang, Jiangjie Wu, [Yuexin Ma](https://yuexinma.me/) and [Rui Zheng](https://sist.shanghaitech.edu.cn/zhengrui_en/main.htm). + + +RoCoSDF is a framework built on neural implicit signed distance functions for shape reconstruction of multi-view freehand 3D ultrasound imaging. + +<div align="center"> +<img src="https://github.com/chenhbo/RoCoSDF/blob/main/img/Fig_RoCoScan.png?raw=true" style="zoom:15%" alt="Data Aquisition Protocol"/> +</div> + + + + +## Demo +* Thoracic Vertebra T4 from ultrasound transducer 1 (UT1) + +<div align="center"> +<img src="https://github.com/chenhbo/RoCoSDF/blob/main/img/Fig_Result_T4.png?raw=true" style="zoom:14.7%" alt="Framework"/> +</div> + + +<br /> + + +* The example mesh results of RoCoSDF are in `outs/T4_RoCo/outputs/*.ply`. + + +-------------------------------------- + +## 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 +``` +-------------------------------------- + +### 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 +| +``` + +-------------------------------------- + +### 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](mailto:chenhb@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" + } +``` +-------------------------------------- + +### 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