MONAI-Medical Open Network for Artificial Intelligence

MONAI is an open-source framework designed for deep learning in medical imaging. Here’s how you can get started:

1. Installation: Install MONAI core using pip:
   pip install monai
2. Data Processing: MONAI provides specialized transforms for medical imaging, including preprocessing, augmentation, and caching.
3. Model Training: You can use MONAI’s built-in networks and training workflows to develop deep learning models for segmentation, classification, and more.
4. MONAI Label: A tool for interactive annotation and active learning, useful for medical image segmentation.
   pip install monailabel
5. Deployment: MONAI Deploy helps package and deploy AI models for clinical applications.
   pip install monai-deploy-app-sdk

To use a model from the MONAI Model Zoo, follow these steps:

1. Install MONAI (if you haven’t already): pip install "monai[fire]"
2. Download a model from the Model Zoo:
   python -m monai.bundle download "spleen_ct_segmentation" --bundle_dir "bundles/"
   This will download the Spleen CT Segmentation model into the bundles/ directory.

Other example:

python -m monai.bundle download "brats_mri_generative_diffusion" --bundle_dir "bundles/"

downloads the BraTS MRI image latent diffusion generation model, which is is trained on BraTS 2016 and 2017 data from Medical Decathlon, using the Latent diffusion model.

Meanwhile,

python -m monai.bundle download "brain_image_synthesis_latent_diffusion_model" --bundle_dir "bundles/"

downloads the Latent Diffusion Model for the synthesis of conditioned 3D brain MRI data. It is conditioned on age, sex, the volume of ventricular cerebrospinal fluid, and brain volume normalised for head size. It was trained on data from 31,740 participants from the UK Biobank. More detailed.

1. Load the model in your Python script: from monai.bundle import load model = load("bundles/spleen_ct_segmentation")
2. Use the model for inference: result = model(input_image)

You can explore more models and their documentation in the MONAI Model Zoo or check out the GitHub repository for additional details.