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Boundary Constraint-free Biomechanical Model-Based Surface Matching for Intraoperative Liver Deformation Correction

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# Boundary Constraint-Free Biomechanical Model-Based Surface Matching for Intraoperative Liver Deformation Correction

https://github.com/user-attachments/assets/bfac26af-f29b-4098-8b31-646bdb1ff5de

## Datasets

Datasets include our constructed phantoms, PBSM dataset, and [Sparse data challenge dataset](https://osf.io/etpq9).

The preprocessed dataset and our raw results can be downloaded via:

- [Demo](https://drive.google.com/drive/folders/1eiGozqjyq-7D0obMpAE-W-mebk_UKNp8?usp=sharing) 
- [Our phatnoms](https://drive.google.com/drive/folders/1rrtw6r6oDdFevubUwgiszS884Aef9xrc?usp=sharing)
- [PBSM dataest](https://drive.google.com/drive/folders/1QAGupR4feKBT0FdsBJXGhF74WL53JCmk?usp=sharing) 
- [Sparse dataest](https://drive.google.com/drive/folders/1TzAVQzJkgcccABnnGOT5Wfy1Rp8xA0Y3?usp=sharing)

## Our Code (very easy to run!)

### Run Non-rigid Organ Registration
Our registration code is within the BCF-FEM folder.

The only dependence is the [eigen library](https://drive.google.com/drive/folders/1TnSgEn-Km1tiKHBdASub9rFEpTOj6R8J?usp=sharing). Download and put it inside the BCF-FEM folder, or change its path in the CMakeLists.txt.

The code is developed on Windows 10 using [Visual Studio 2019](https://learn.microsoft.com/en-us/cpp/build/cmake-projects-in-visual-studio?view=msvc-170). You can run the following files within the VS or use the built .exe files. Please set your EXECUTABLE_OUTPUT_PATH and LIBRARY_OUTPUT_PATH in the CMakeLists.txt.

```
# Please change the data paths to your paths 
app/Demo.cpp                       
app/run_our_phantom.cpp
app/run_pbsm_dataset.cpp
app/run_sparse_dataset.cpp
```

### Evaluation

```
# change the data paths to yours
Evaluation/eval_our_dataset.py
Evaluation/eval_pbsm_dataset.py
Evaluation/eval_sparse_dataset.py
```

### Test on Other Datasets

We use the [.off](https://en.wikipedia.org/wiki/OFF_(file_format)) format to store data. If you want to transfer other formats such as .vtk and .ply to .off. Please see the functions within the
```
Data_Format_Prepare/format_transfer.py
```

Please check:
```
Data_Format_Prepare/prepare_dataset.py
```
, which includes an example of preparing a preoperative model .stl and an intraoperative point cloud .ply to the format shown in the Demo. It contains functions to generate the tetrahedron from the surface mesh, downsample, and filter the intraoperative point cloud:

![plot](https://raw.githubusercontent.com/zixinyang9109/BCF-FEM/main/Data_Format_Prepare/Prepare_data.jpg)

Then, you can run our the BCF-FEM to get registration results:

![plot](https://raw.githubusercontent.com/zixinyang9109/BCF-FEM/main/Data_Format_Prepare/Registration_comparison.jpg)

## Reference
The reference for our phantoms:
```bibtex
@article{yang2024boundary,
  title={Boundary Constraint-free Biomechanical Model-Based Surface Matching for Intraoperative Liver Deformation Correction},
  author={Yang, Zixin and Simon, Richard and Merrell, Kelly and Linte, Cristian A},
  journal={IEEE Transactions on Medical Imaging},
  year={2024},
  publisher={IEEE}
}
```

Three references for the Sparse Data Challenge dataset:
```bibtex
@inproceedings{brewer2019image,
  title={The image-to-physical liver registration sparse data challenge},
  author={Brewer, E Lee and Clements, Logan W and Collins, Jarrod A and Doss, Derek J and Heiselman, Jon S and Miga, Michael I and Pavas, Chris D and Wisdom III, Edward H},
  booktitle={Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling},
  volume={10951},
  pages={364--370},
  year={2019},
  organization={SPIE}
}

@article{collins2017improving,
  title={Improving registration robustness for image-guided liver surgery in a novel human-to-phantom data framework},
  author={Collins, Jarrod A and Weis, Jared A and Heiselman, Jon S and Clements, Logan W and Simpson, Amber L and Jarnagin, William R and Miga, Michael I},
  journal={IEEE transactions on medical imaging},
  volume={36},
  number={7},
  pages={1502--1510},
  year={2017},
  publisher={IEEE}
}

@article{heiselman2024image,
  title={The image-to-physical liver registration sparse data challenge: comparison of state-of-the-art using a common dataset},
  author={Heiselman, Jon S and Collins, Jarrod A and Ringel, Morgan J and Peter Kingham, T and Jarnagin, William R and Miga, Michael I},
  journal={Journal of Medical Imaging},
  volume={11},
  number={1},
  pages={015001--015001},
  year={2024},
  publisher={Society of Photo-Optical Instrumentation Engineers}
}
```

The reference for PBSM dataset:
```bibtex
@article{suwelack2014physics,
  title={Physics-based shape matching for intraoperative image guidance},
  author={Suwelack, Stefan and R{\"o}hl, Sebastian and Bodenstedt, Sebastian and Reichard, Daniel and Dillmann, R{\"u}diger and dos Santos, Thiago and Maier-Hein, Lena and Wagner, Martin and W{\"u}nscher, Josephine and Kenngott, Hannes and others},
  journal={Medical physics},
  volume={41},
  number={11},
  pages={111901},
  year={2014},
  publisher={Wiley Online Library}
}
```

## Related Software/ Projects

- [MeshLab](https://www.meshlab.net/) (Visualization) 
- [PyVista](https://pyvista.org/) (Data processing, evaluation, and visualization)
- [Gmsh](https://gmsh.info/) (Volumetric mesh generation)
- [GMM-FEM](https://github.com/siavashk/GMM-FEM) (Baseline comparison)
- [V2S-Net](https://gitlab.com/nct_tso_public/Volume2SurfaceCNN) (Baseline comparison)
- [Adjoint-elastic-registration](https://github.com/gmestdagh/adjoint-elastic-registration) (Data term formulation)

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