Disclaimer:

Not for commercial use.

This python package is currently a public beta release. You can use it free of charge for non-commercial applications until further notice. To use it, you still require a (free) license key, which you can get from the Python SDK product page. For commercial applications, please get in touch with us at info@imfusion.com.

Note also, the functionality offered here is only a subset of the Python bindings we have available. In particular, modality-specific plugins (e.g. for Ultrasound, CT, etc.) are not included. Please reach out to us if you are interested in such functionality or visit our webshop.

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Overview

Description

The imfusion package enables easy and fast loading, handling and processing of medical image data. It is a wrapper around the ImFusion SDK and exposes a subset of its functionality to Python. The major advantages of using imfusion are:

  • High Performance:

    Leveraging optimized C++ for fast execution and OpenGL for GPU acceleration, ensuring compatibility with various GPU vendors.

  • Versatile Data Structures:

    Handle a wide range of medical images and data types, including 2D/3D images, metadata, deformations, rotations, masks, and segmentations. It also supports keypoints, point clouds, and meshes.

  • Extensive Set of Algorithms:

    Access a vast array of image processing algorithms, from basic cropping to complex multi-modal image registration. Even algorithms that don’t have dedicated Python bindings can be executed through a functional interface.

  • File Format Support:

    Load and save numerous medical imaging formats, including Nifti, MHD, Dicom, HDF5, PNG, and JPG and featuring a reliable Dicom loader used in FDA-approved products.

  • Deployment-Ready Data Pipelines:

    Construct efficient data pipelines for ML model training and deployment, ensuring consistent pre-processing and post-processing.

  • numpy-like arithmetic but with images

    Perform arithmetic operations on images with a functional API or operators, supporting GPU or CPU execution and, optionally, delayed expression evaluation for enhanced performance.

Documentation

Please find the documentation for this Python package at docs.imfusion.com/python.

Support

If you experience issues with this package, please let us know in our forum.

For business inquiries please contact info@imfusion.com.