predict-idlab/landmarker

predict-idlab/landmarker has 49 GitHub stars and 11 forks. Star changes are not measured yet. Last checked 2026-11-14 12:34 UTC. Last push 2026-09-07.

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What you can do

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.

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À propos

PyTorch-based toolkit for landmark localization

Apprentissage automatique

README.md

Original

Landmarker is a PyTorch-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	`pip install landmarker`

🚀 Getting Started

Technical documentation is available at documentation.

Examples and tutorials are available at examples

✨ 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 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.

👤 Jef Jonkers