|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Fast||342||14 days ago||11||June 10, 2022||11||bsd-2-clause||C++|
|A framework for high-performance medical image processing, neural network inference and visualization|
|Alizams||169||19 hours ago||2||gpl-3.0||C++|
|Apertusvr||128||a year ago||6||mit||C++|
|Virtual Reality Software Library|
|Hic Data Analysis Bootcamp||78||5 years ago||1||mit||Jupyter Notebook|
|Workshop on measuring, analyzing, and visualizing the 3D genome with Hi-C data.|
|Patient Viz||57||4 years ago||17||mit||Python|
|Visualization of electronic medical records or other sequence event data.|
|Platipy||57||3 days ago||10||June 02, 2022||25||apache-2.0||Python|
|Processing Library and Analysis Toolkit for Medical Imaging in Python|
|Slicerdmri||55||3 months ago||47||other||C++|
|Diffusion MRI analysis and visualization in 3D Slicer open source medical imaging platform.|
|Agenoria||43||3 months ago||11||other||Python|
|Python utility for visualizing growth data from a newborn's first year, such as feeding, diapering, sleep, and growth, recorded in the Glow Baby app.|
|Mrivis||21||2 months ago||12||mit||Python|
|medical image visualization library and development toolkit|
|Scalismo Ui||20||a year ago||5||May 13, 2022||6||gpl-3.0||Scala|
|Visualization for Statistical Shape Models and Medical Images based on Scalismo.|
FAST is an open-source framework with the main goal of making it easier to do high-performance processing, neural network inference, and visualization of medical images utilizing multi-core CPUs and GPUs. To achieve this, FAST use modern C++, OpenCL and OpenGL, and neural network inference libraries such as TensorRT, OpenVINO, TensorFlow and ONNX Runtime.
See installation instructions for Windows, Ubuntu Linux and macOS.
To start using the framework, check out the C++ Intro Tutorial or the Python Intro Tutorial.
Learn best by example? Check out all the examples for C++ and Python.
For more examples and documentation, go to fast.eriksmistad.no.
Need help? Post your questions on the Discussions page or use the Gitter Chat.
FAST has been described in the following research articles. If you use this framework for research please cite them:
FAST: framework for heterogeneous medical image computing and visualization Erik Smistad, Mohammadmehdi Bozorgi, Frank Lindseth International Journal of Computer Assisted Radiology and Surgery 2015
High Performance Neural Network Inference, Streaming, and Visualization of Medical Images Using FAST Erik Smistad, Andreas Østvik, André Pedersen IEEE Access 2019
To setup and build the framework, see the instructions for your operating system:
FAST itself is licenced under the permissive BSD 2-clause license, however the binary releases of FAST include several third-party libraries which use a number of different open source licences (MIT, Apache 2.0, LGPL ++), see the licences folder in the release for more details.