Path_planning_for_FEVAR

[](https://ieeexplore.ieee.org/abstract/document/8793918/) [](https://arxiv.org/abs/1809.05955)

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

Contents

• 0. Brief Introduction
• 1. Requirement
• 2. Usage
  • 2.1. Script 'demo_2D3Dregist.m'
  • 2.2. Folder 'function'
  • 2.3. Folder 'data'
  • 2.4. Folder 'external'
• 3. Tutorial
• 4. 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. Tutorial

A detailed tutorial can be found in the provided (powered by Zread.ai).

4. 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. bibtex @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. bibtex @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} }