MONAI

MONAI

MONAI

Introduction

MONAI (Medical Open Network for AI) is an open-source framework built on PyTorch, designed for developing and deploying AI models in medical imaging.

Created by NVIDIA and the Linux Foundation, it provides specialized tools for handling medical data formats like DICOM and NIfTI, along with advanced preprocessing, augmentation, and 3D image analysis capabilities.

MONAI includes ready-to-use deep learning models (such as UNet and SegResNet) and utilities for segmentation, classification, and image registration. It supports distributed GPU training and ensures reproducible research workflows.

Example 1: Installing MONAI using the PythonPip module

Example 1: Installing MONAI using the PythonPip module

Introduction

With the PythonPip module, you can import additional Python libraries into MeVisLab.

Steps to do

Install PyTorch

As MONAI requires PyTorch, install it by using the PythonPip module as described here.

Install MONAI

After installing torch and torchvision, we install MONAI.

For installing MONAI enter "monai" into the Command textbox and press Install.

Example 2: Applying a spleen segmentation model from MONAI in MeVisLab

Example 2: Applying a spleen segmentation model from MONAI in MeVisLab

Introduction

In the following, we will perform a spleen segmentation using a model from the MONAI Model Zoo. The MONAI Model Zoo is a collection of pre-trained models for medical imaging, offering standardized bundles for tasks like segmentation, classification, and detection across MRI, CT, and pathology data, all built for easy use and reproducibility within the MONAI framework. Further information and the required files can be found here.