File for example: https://www.mobydicom.com/FLARE22.nii.gz
This application is a intuitive tool designed for medical imaging students and researchers. It enables users to load and visualize MRI (Magnetic Resonance Imaging) and CT (Computed Tomography) files seamlessly, offering a user-friendly interface for detailed analysis. The visualization is presented in three orthogonal projections—axial, coronal, and sagittal—providing an exhaustive view of medical data to support research, and further examination.

Key Features

1. File Loading and Visualization:
- Supports loading medical imaging files in the NIFTI format (*.nii, *.nii.gz), which is widely used in medical research and imaging technologies.
- Provides interactive visualization in three projections to analyze structures from different angles.
2. Organ Segmentation Using AI:
- Integrates advanced artificial intelligence models to perform accurate segmentation of organs within the abdominal region and the whole body.
- Segmentation is powered by a pre-trained model from the MONAI (Medical Open Network for AI) Model Zoo, specifically the "Whole Body CT Segmentation" model.
- Enables faster and more accurate delineation of anatomical structures, ideal for research and educational use cases.
3. Technology and Libraries Used:
- Utilizes the RNIfTI library (licensed under GPL-2) to handle NIFTI medical imaging files efficiently.
- Leverages AI segmentation models from MONAI (licensed under Apache License 2.0), ensuring high-quality and reliable segmentation results.

- Efficiency:
The application streamlines the visualization and segmentation of medical images, saving time and reducing manual effort in diagnosing and analyzing anatomical structures.

- Advanced AI Integration:
Harnesses cutting-edge machine learning technology to deliver precise and reliable abdominal organ segmentation, supporting research workflows.

- Flexibility:
With support for widely-used medical imaging formats like NIFTI, the application adapts seamlessly to the needs of professionals working with complex datasets.