landmarker 0.4.0
A PyTorch-based toolkit for (anatomical) landmark detection in images.
License: MIT
Requires Python: <3.14,>=3.10

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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`                  |
<!-- | [**conda**](https://anaconda.org/conda-forge/landmarker) | `conda install -c conda-forge 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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<p align="center">
👤 <i>Jef Jonkers</i>
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