https://github.com/predict-idlab/landmarker
PyTorch-based toolkit for landmark localization
https://github.com/predict-idlab/landmarker
computer-vision keypoint-detection keypoints-detector landmark-detection landmark-localization medical-image-analysis medical-image-processing python-package pytorch
Last synced: 19 days ago
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PyTorch-based toolkit for landmark localization
- Host: GitHub
- URL: https://github.com/predict-idlab/landmarker
- Owner: predict-idlab
- License: mit
- Created: 2023-11-30T16:31:45.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-06-26T08:53:44.000Z (9 months ago)
- Last Synced: 2025-06-26T09:41:01.523Z (9 months ago)
- Topics: computer-vision, keypoint-detection, keypoints-detector, landmark-detection, landmark-localization, medical-image-analysis, medical-image-processing, python-package, pytorch
- Language: Python
- Homepage: https://predict-idlab.github.io/landmarker/
- Size: 39.5 MB
- Stars: 29
- Watchers: 3
- Forks: 8
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
[](https://pypi.org/project/landmarker/)
[](https://pypi.org/project/landmarker/)
[](https://codecov.io/gh/predict-idlab/landmarker)
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Landmarker is a [PyTorch](https://pytorch.org/)-based toolkit for (anatomical) landmark localization in 2D/3D images. It is designed to be easy to use and to provide a flexible framework for state-of-the-art landmark localization algorithms for small and large datasets. Landmarker was developed for landmark detection in medical images. However, it can be used for any type of landmark localization problem.
## 🛠️ Installation
| | command |
| :--------------------------------------------------- | :------------------------------------ |
| [**pip**](https://pypi.org/project/landmarker) | `pip install landmarker` |
## 🚀 Getting Started
Technical documentation is available at [documentation](https://predict-idlab.github.io/landmarker/).
Examples and tutorials are available at [examples](https://predict-idlab.github.io/landmarker/examples/index.html)
## ✨ Features
- **Modular**: Landmarker is designed to be modular. Almost all components can be used independently.
- **Flexible**: Landmarker provides a flexible framework for landmark detection, allowing you to easily customize your model, loss function, and data loaders.
- **State-of-the-art**: Landmarker provides state-of-the-art landmark detection models and loss functions.
## 📈 Future Work
- Extension to landmark detection in videos.
- ...
## 👪 Contributing
We welcome contributions to Landmarker. Please read the [contributing guidelines](CONTRIBUTING.md) for more information.
## 📖 Citation
If you use landmarker in your research, please cite the following paper:
J. Jonkers, L. Duchateau, G. Van Wallendael, and S. Van Hoecke, “landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D Images,” SoftwareX, vol. 30, p. 102165, May 2025, doi: 10.1016/j.softx.2025.102165.
J. Jonkers, F. Coopman, L. Duchateau, G. V. Wallendael, and S. V. Hoecke, “Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal prediction,” Mar. 18, 2025, arXiv: arXiv:2503.14106. doi: 10.48550/arXiv.2503.14106.
## 📝 License
Landmark is licensed under the MIT [license](LICENSE).
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👤 Jef Jonkers