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* Components
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* Components
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- [Orthanc](https://www.orthanc-server.com/static.php?page=documentation). Orthanc is an open source, light-weight DICOMm server.
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- [Orthanc](https://www.orthanc-server.com/static.php?page=documentation). Orthanc is an open source, light-weight DICOMm server.
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- [OHIF](https://ohif.org/). OHIF is an extensible web imaging platform to create custom DICOM viewers quickly and efficiently.
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- [OHIF](https://ohif.org/). OHIF is an extensible web imaging platform to create custom DICOM viewers quickly and efficiently.
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- [Slicer](https://www.slicer.org/). Sonador IO integrates with 3D Slicer to perform advanced biomedical image analysis. Using Slicer, it is possible to create segmentations for surgical planning, generate training sets for AI, or perform advanced simulation.
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- [Airflow](https://airflow.apache.org/). Airflow is a platform to create, schedule, and monitor workflows. Airflow is used within the Sonador project for scheduling data pipelines, training machine learning models, and performing batch tasks on medical imaging data.
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- [Airflow](https://airflow.apache.org/). Airflow is a platform to create, schedule, and monitor workflows. Airflow is used within the Sonador project for scheduling data pipelines, training machine learning models, and performing batch tasks on medical imaging data.
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* Integrations
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- Python
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+ [Sonador IO](https://code.oak-tree.tech/oak-tree/medical-imaging/sonador-client). Sonador IO includes a Python software library for the Orthanc REST API which allows developers to work with imaging resoruces using an object-relational mapping (ORM) interface similar to those provided by the poular web development framework [Django](https://docs.djangoproject.com/en/4.0/topics/db/models/) or [SQLAlchemy](https://www.sqlalchemy.org/). This greatly simplifies the implementation of complex systems and allows for data stored in Orthanc to be integrated with popular Python libraries for machine learning, [Artificial Intelligence](https://pytorch.org/), and [3D processing](https://docs.pyvista.org/).
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+ [Medical Open Network for Artificial Intelligence](https://monai.io/index.html). MONAI is a collection of components and programs designed to build end-to-end AI systems based on medical imaging standards and best practices. Built on top of [PyTorch](https://pytorch.org/), MONAI provides a framework for multi-dimensional medical imaging pipelines; implementations of networks, losses, and evaluation metrics; and support for multi-GPU training.
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## Development
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## Development
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