Landmarks To Shape

Generate Anatomy From Landmarks

This code accompanies the submission "AnatomyGen: Deep Anatomy Generation From Dense Representation With Applications in Mandible Synthesis", in the Medical Imaging with Deep Learning Conference, London, 2019. The manuscript is accessible on openreview.

Citation

If you used the code or the voxelized version of the dataset in your research, please include the following citation:

@inproceedings{LandmarksToShape2019,
  title={AnatomyGen: Deep Anatomy Generation From Dense Representation With Applications in Mandible Synthesis},
  author={Abdi, Amir H. and Borgard, Heather and Abolmaesumi, Purang and Fels, Sidney},
  booktitle={Medical Imaging with Deep Learning},
  series={Proceedings of Machine Learning Research (PMLR)},
  year={2019}
}

Data

The data that accompanies the code includes voxelized version of mandible (jaw) bones collected from the MICCAI 2015 segmentation challenge [1], Cancer Imaging Archive (TCIA) [2], and samples released by Wallner et al [3]. The submisison also made use of 48 more mandible samples, which are not included here, but can be accessed through data sharing agreements (please contact authors for further instructions).

To download the data and set environment variables, please run

 source download-data.sh

You can also download the data separately here. Please set the environment variable $MANDIBLE_DATA_PATH to the data directory.

Training and Testing

This is a python3 implementation. To install the requirements, depending on your preferred package manager, please run:

pip3 install -r requirements.txt    
or
conda install --file requirements.txt

To train the model on the training subset, run the script

bash train-test.sh

To test your model on the test subset, set the --test flag in the train-test.sh script to true.

Licensing

This code is released under GNU GENERAL PUBLIC LICENSE V3.

References

[1] Patrik F. Raudaschl and Paolo Zaffino et al. Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015.Medical Physics, 44(5):2020–2036, 2017a.
[2] Margarita L. Zuley and Rose Jarosz et al. Radiology data fromthe cancer genome atlas head-neck squamous cell carcinoma collection, 2016.
[3] Jrgen Wallner and Jan Egger. Mandibular ct dataset collection, 2018.

amir-abdi / LandmarksToShape