Path_planning_for_FEVAR

Code for ICRA'2019 paper Towards 3d path planning from a single 2d fluoroscopic image for robot assisted fenestrated endovascular aortic repair

0. Brief Introduction
1. Requirement
2. Usage
3. Citing this work

0. Brief Intro

The segmented shape of the abdominal aorta from a CT scan:

The segmentation process is implemented following this work: Abdominal Aortic Aneurysm Segmentation with a Small Number of Training Subjects

The 3D abdominal aorta shape and the center line recovered from one 2D X-ray image:

How it looks like when a catheter moves through the recovered center line of the aorta:

1. Requirement

Other Matlab version could be also applicable

2. Usage

$DOWNLOAD_DIR/
├── data/
|   ├── IMG26.JPG
|   ├── Label_save_P26.mat
|   ├── Skeleton3D_P26.mat
|   └── ...
├── external/
|   ├── TPS3D/
|   ├── distance2curve/
|   └── phi-max-skeleton3d-matlab-a98ad07/
├── function/
|   ├── array2str.m
|   ├── branch_classify.m
|   ├── branch_node_assign.m
|   ├── node_classification.m
|   ├── placement_match.m
|   ├── points_dist.m
|   ├── project3D22D.m
|   ├── regist2D3D.m
|   ├── regist_energy.m
|   └── trunk_node_assign.m
└── demo_2D3Dregist.m

2.1. Script 'demo_2D3Dregist.m':

This demonstrates how to recover a 3D skeleton for the robotic path from a 2D intra-operative segmented aneurysm shape and a 3D pre-operative skeleton. It will import a 2D jpg image of pre-operative fluoroscopy, a 2D segmentation label, and a 3D skeleton. It will display the time cost for registration of 2D/3D skeletons, the intra-operative (ground truth) skeleton, the pre-operative skeleton, and our prediction, as well as the evaluated distance errors in 2D and 3D.

2.2. Folder 'function':

It includes all the codes written for the deformable registration between 2D and 3D skeletons.

Please kindly read the license in each file.

2.3. Folder 'data':

It includes the imported data used in the demonstration.

2.4. Folder 'external':

It includes redistributed codes used in the demonstration.

Please kindly read the license in each file.

3. Citing this work

For any academic publication using the codes in this folder, please kindly cite:

J. Q. Zheng, X. Y. Zhou, C. Riga and G. Z. Yang, "Towards 3d path planning from a single 2d fluoroscopic image for robot assisted fenestrated endovascular aortic repair", IEEE International Conference on Robotics and Automation (ICRA), 2019.

@inproceedings{zheng2019towards,
  title={Towards 3d path planning from a single 2d fluoroscopic image for robot assisted fenestrated endovascular aortic repair},
  author={Zheng, Jian-Qing and Zhou, Xiao-Yun and Riga, Celia and Yang, Guang-Zhong},
  booktitle={2019 International Conference on Robotics and Automation (ICRA)},
  pages={8747--8753},
  year={2019},
  organization={IEEE},
  doi={10.1109/ICRA.2019.8793918},
}

and, if applicable, the aorta segmentation work:

J. Q. Zheng, X. Y. Zhou, Q. B. Li, C. Riga and G. Z. Yang, "Abdominal aortic aneurysm segmentation with a small number of training subjects." arXiv preprint arXiv:1804.02943.

@article{zheng2018abdominal,
  title={Abdominal aortic aneurysm segmentation with a small number of training subjects},
  author={Zheng, Jian-Qing and Zhou, Xiao-Yun and Li, Qing-Biao and Riga, Celia and Yang, Guang-Zhong},
  journal={arXiv preprint arXiv:1804.02943},
  year={2018}
}

jianqingzheng/path_planning_for_FEVAR