This tool allows on-the-fly augmentation and training for networks in the medical imaging domain. It uses SimpleITK to load and augment input data, and Tensorflow to define and train networks. As this framework is mainly used for research, some files are not well documented. However, I'm working on improving this. If you have problems or find any bugs, don't hesitate to send me a message.
Update: The bin folder with example experiments got removed.
The individual experiments of our papers are now in dedicated repositories due to increased memory requirements.
List of experiment repositories:
If you use this code for your research, please cite any of our papers.
Coarse to Fine Vertebrae Localization and Segmentation with SpatialConfiguration-Net and U-Net
@inproceedings{Payer2020,
title = {Coarse to Fine Vertebrae Localization and Segmentation with SpatialConfiguration-Net and U-Net},
author = {Payer, Christian and {\v{S}}tern, Darko and Bischof, Horst and Urschler, Martin},
booktitle = {Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP},
doi = {10.5220/0008975201240133},
pages = {124--133},
volume = {5},
year = {2020}
}
Segmenting and Tracking Cell Instances with Cosine Embeddings and Recurrent Hourglass Networks:
@article{Payer2019b,
title = {Segmenting and Tracking Cell Instances with Cosine Embeddings and Recurrent Hourglass Networks},
author = {Payer, Christian and {\v{S}}tern, Darko and Feiner, Marlies and Bischof, Horst and Urschler, Martin},
journal = {Medical Image Analysis},
volume = {57},
year = {2019},
month = {oct},
pages = {106--119},
doi = {10.1016/j.media.2019.06.015},
}
Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization:
@article{Payer2019a,
title = {Integrating Spatial Configuration into Heatmap Regression Based {CNNs} for Landmark Localization},
author = {Payer, Christian and {\v{S}}tern, Darko and Bischof, Horst and Urschler, Martin},
journal = {Medical Image Analysis},
volume = {54},
year = {2019},
month = {may},
pages = {207--219},
doi = {10.1016/j.media.2019.03.007},
}
Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks:
@inproceedings{Payer2018b,
title = {Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks},
author = {Payer, Christian and {\v{S}}tern, Darko and Neff, Thomas and Bischof, Horst and Urschler, Martin},
booktitle = {Medical Image Computing and Computer-Assisted Intervention - {MICCAI} 2018},
doi = {10.1007/978-3-030-00934-2_1},
pages = {3--11},
year = {2018},
}
Multi-label Whole Heart Segmentation Using CNNs and Anatomical Label Configurations:
@inproceedings{Payer2018a,
title = {Multi-label Whole Heart Segmentation Using {CNNs} and Anatomical Label Configurations},
author = {Payer, Christian and {\v{S}}tern, Darko and Bischof, Horst and Urschler, Martin},
booktitle = {Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017},
doi = {10.1007/978-3-319-75541-0_20},
pages = {190--198},
year = {2018},
}
Regressing Heatmaps for Multiple Landmark Localization Using CNNs:
@inproceedings{Payer2016,
title = {Regressing Heatmaps for Multiple Landmark Localization Using {CNNs}},
author = {Payer, Christian and {\v{S}}tern, Darko and Bischof, Horst and Urschler, Martin},
booktitle = {Medical Image Computing and Computer-Assisted Intervention - {MICCAI} 2016},
doi = {10.1007/978-3-319-46723-8_27},
pages = {230--238},
year = {2016},
}