Landmarker is a PyTorch-based toolkit for (anatomical) landmark detection in images. It is designed to be easy to use and to provide a flexible framework for state-of-the-art landmark detection 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 detection problem.
| command | |
|---|---|
| pip | pip install landmarker |
Technical documentation is available at documentation.
Examples and tutorials are available at examples
- Modular: Landmarker is designed to be modular. It is easy to add new models, datasets, and loss functions.
- Flexible: Landmarker provides a flexible framework for landmark detection. It is easy to customize the training and evaluation process.
- Easy to use: Landmarker is easy to use. It provides a simple API for training and evaluation.
- State-of-the-art: Landmarker provides state-of-the-art landmark detection models and loss functions.
- Extension to 3D landmark detection.
- Extension to landmark detection in videos.
- Add uncertainty estimation.
- ...
We welcome contributions to Landmarker. Please read the contributing guidelines for more information.
If you use Landmarker in your research, please cite the following paper:
SCIENTIFIC PAPER UNDER REVIEW
Landmark is licensed under the MIT license.
👤 Jef Jonkers