This repository contains several implementations of the 2D/3D registration strategies described in the IPCAI/IJCARS 2020 paper "Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration." The paper may be found here or here. This repository is a companion to DeepFluoroLabeling-IPCAI2020, which provides implementations of the PyTorch models and references to the entire dataset used in the IPCAI/IJCARS paper.
The global, semi-automatic, approaches are inappropriate for intraoperative purposes as they often require several minutes to complete processing. However, these offline techniques are useful for creating a dataset that may be used for training a convolutional neural network (CNN), capable of performing these annotations very quickly. An online registration strategy may then use the CNN-derived annotations to calculate automatic and robust registrations with intraoperatively compatible runtimes.
The following tools are provided by this repository:
- Data extraction from the HDF5 dataset:
- Creating annotations for CNN-training:
- Intraoperative Registration
Python scripts are provided (extract_fcsv_from_nn_csv.py and extract_seg_from_nn_h5.py) in order to extract segmentations and landmarks from CNN inferences.
Instructions for performing CNN training and testing may be found here.
The tools provided here rely on the xReg library and users should see the xReg wiki for details on building the software.
Demonstrations detailing the usage of the tools listed above are provided on the wiki of this repository.
The tools provided here are intended to demonstrate simplified use-cases of these registration strategies. As such, users are encouraged to extend, modify and adapt these programs in order to conduct large scale studies efficiently.
Please submit an issue for any problems, feature requests, or suggestions.
The software is available for use under the MIT License.
If you have found this software useful in your work, we kindly ask that you cite the IPCAI/IJCARS paper:
Grupp, Robert B., et al. "Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration." International Journal of Computer Assisted Radiology and Surgery (2020): 1-11.
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@article{grupp2020automatic,
title={Automatic annotation of hip anatomy in fluoroscopy for robust and efficient {2D}/{3D} registration},
author={Grupp, Robert B and Unberath, Mathias and Gao, Cong and Hegeman, Rachel A and Murphy, Ryan J and Alexander, Clayton P and Otake, Yoshito and McArthur, Benjamin A and Armand, Mehran and Taylor, Russell H},
journal={International Journal of Computer Assisted Radiology and Surgery},
pages={1--11},
publisher={Springer}
}