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+## 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