MONAI

What is MONAI?

MONAI is an open-source framework for deep learning in medical imaging, co-founded by NVIDIA and leading academic medical centers. It aims to accelerate AI advancements in healthcare imaging by developing and sharing best practices across academia and industry. MONAI provides a comprehensive toolkit for creating, training, and deploying AI models in medical imaging, offering features like flexible preprocessing, compositional APIs, and domain-specific implementations for deep learning tasks. It is built on PyTorch and supports various medical imaging modalities such as DICOM and NIFTI. MONAI facilitates research breakthroughs and the transition of AI into clinical practice by harnessing medical data for deep learning model development.

How to use MONAI?

To use MONAI effectively, start by exploring the MONAI Core framework, which offers tools for preprocessing, training, and deploying AI models in medical imaging. Users can leverage the Model Zoo for pre-trained models and utilize the community resources for customization and integration into existing workflows.

MONAI's Core Features

• Flexible preprocessing for multi-dimensional medical imaging data.

• Compositional and portable APIs for easy integration into existing workflows.

• Domain-specific implementations for networks, losses, and evaluation metrics.

• Customizable design catering to varying user expertise.

• Support for multi-GPU and multi-node data parallelism.

MONAI's Use Cases

• #1

  Image Segmentation: MONAI can be used to segment medical images, identifying specific structures like tumors or organs.

• #2

  Image Classification: It aids in classifying medical images into different categories, such as distinguishing between healthy and diseased tissues.

• #3

  Image Registration: MONAI helps align multiple images taken at different times or from different viewpoints, crucial for tracking changes in medical conditions.