FAST: A C++ and OpenCL Framework for Real-Time Medical Image Pipelines
A framework for high-performance medical image processing, neural network inference and visualization
At a glance
- What is it?
- FAST is a BSD-2-Clause framework from NTNU and SINTEF that targets high-performance medical image processing, neural network inference and visualization across CPUs and GPUs. It is a strong fit for streaming and visualization work in C++ or Python, but the backend and build matrix is the real cost.
- Who is it for?
- Adopt FAST if you are building a C++ or Python application that must stream medical images, run inference and render results in one process, and you can commit to the OpenCL and OpenGL dependency chain. Do not adopt it if you need a pure CPU, pure Python pipeline, or a framework you can install and upgrade without touching graphics drivers.
- Can I use it commercially?
- Yes. BSD-2-Clause is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository received new commits within the last day.
- What is it written in?
- Mainly C++, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
The Gap FAST Fills: Streaming, Inference and Rendering in One Process
A typical medical imaging prototype is assembled from three unrelated stacks. A file reader loads a volume, a Python inference script runs a network, and a viewer renders the result. Each hop costs a copy, a format conversion and a thread boundary. FAST is built to remove those hops. The README describes the goal as making it easier to do high-performance processing, neural network inference and visualization of medical images using multi-core CPUs and GPUs, with modern C++, OpenCL and OpenGL as the substrate. The intended user is not a data scientist working from a notebook. It is an engineer or research group building an application that has to keep up with a live source: an ultrasound scanner, a camera, a movie file or a sequence of images. The framework is developed by researchers at the Norwegian University of Science and Technology and SINTEF, and two papers are cited in the README for citation purposes, one from 2015 and one from 2019 in IEEE Access. The 2015 date matters when reading the API: this is a codebase that predates much of the current Python imaging ecosystem and was designed around explicit device memory and rendering, not around array libraries.
How Data Objects Stay Coherent Across CPU, GPU and Render Thread
The mechanism the README calls high-level data management is the part worth understanding before you commit. A FAST data object represents the same data, such as an image, on all processors. The framework keeps that data coherent across the different storage areas, so the developer does not write explicit host-to-device transfers. In practice this means you construct an image, run an OpenCL algorithm on it, and pass the same object to a renderer, and the framework decides what has to move. That is a real convenience and also a real constraint: you are trusting the framework's scheduling rather than controlling it. The second structural decision is thread separation. Rendering and computation run in separate threads so that visualization stays responsive while processing continues. Combined with the streaming layer, which the README says handles static and dynamic or temporal data without any change to the code, this produces a pipeline model where the same graph works for a still image and for a live feed. The claim that no code change is needed between the two modes is the strongest architectural promise in the README, and it is also the claim most worth testing against your own data source.
The Backend Matrix: Four Inference Runtimes and Five Model Formats
FAST does not ship its own neural network engine. It provides a common interface over existing ones. The README lists Google TensorFlow, NVIDIA TensorRT, Intel OpenVINO and Microsoft ONNX Runtime as supported backends, and ONNX, protobuf, SavedModel, OpenVINO IR and UFF as supported model formats. That breadth is unusual and it is the main reason to consider FAST over a narrower runtime wrapper. It also means the framework inherits every quirk of whichever backend you select. TensorRT and OpenVINO are hardware-specific and add their own installation and version-matching burden on top of FAST's. TensorFlow and ONNX Runtime are more portable but will not give you the same latency profile on NVIDIA hardware. The README does not state which backend is the default, nor does it give per-backend performance numbers, so the choice has to be made against your own deployment target rather than from the documentation. Treat the backend list as a menu, not as a guarantee that any combination works on any machine.
Getting It Running: Pip, Docker or a Source Build
The README points to installation instructions for Windows, Ubuntu Linux, macOS and Docker containers, hosted on the project site at fast-imaging.github.io. It does not inline the commands, so the exact package names and CMake flags have to be read from those pages rather than from the repository front page. What the README does confirm is that a Python path exists: the repository carries pip download statistics badges, and separate Python tutorials and Python examples are linked alongside the C++ ones. The tutorials are the intended entry point, with distinct tracks for C++ and Python and example galleries for each. Docker containers are offered as a supported installation route, which is the sensible way to sidestep the OpenCL and OpenGL setup on a workstation. If you are evaluating rather than deploying, start from a container and one tutorial, not from a source build. A source build pulls in OpenCL, OpenGL, Qt and at least one inference runtime, and the README states plainly that the binaries link against many third-party libraries under MIT, Apache 2.0 and LGPL among others.
Where FAST Is the Wrong Choice
The dependency chain is the limitation. OpenCL is required for the high-performance algorithm set, and OpenGL is required for visualization. On macOS, OpenCL is deprecated and the README's own macOS installation page exists precisely because that platform needs separate handling. On headless servers without a GPU, you lose the parallel algorithm implementations that the README lists as a main feature: marching cubes surface extraction, Gaussian smoothing, non-local means, block matching tracking and seeded region growing. What remains is a heavier C++ framework than a plain array library. The second limitation is portability of the streaming claim. The README names specific sources: movie files, webcamera, Intel RealSense, image sequences, and ultrasound scanners such as Clarius. A source outside that list is not covered by the no-code-change promise. Third, this is a C++ framework with Python bindings, not a Python framework. If your work is exploratory and lives in NumPy and PyTorch, the interoperability that matters is the Python path, and the README only says FAST can be used with Python and integrated into existing Qt applications. It does not describe how the bindings map FAST data objects onto Python objects, which is the detail that determines whether the integration is pleasant or painful.
Compared With ITK and VTK
The closest well-known comparison is the ITK and VTK pairing, which covers much of the same ground: ITK for registration, segmentation and filtering, VTK for rendering. The difference in approach is where the compute happens. ITK and VTK have long CPU-oriented histories with GPU acceleration added through specific backends, and their pipelines are pull-based, evaluated when you ask for an output. FAST inverts several of those assumptions. Its algorithms are written as OpenCL kernels from the outset, its data objects are coherent across processors by design, and its pipelines are built for streaming rather than for batch evaluation of a fixed input. That makes FAST a better match for live acquisition and for pipelines that end in a rendered frame. It makes FAST a worse match for a large offline batch job where you want the widest possible set of vetted algorithms, and for teams whose existing code is already ITK-based. The two are not mutually exclusive at the file level, since FAST reads DICOM, MHD and NIFTI, but mixing them means moving data between two pipeline models.
Release Cadence, Licence and What an Upgrade Costs You
The release history shows a steady patch rhythm: v4.17.1 in March 2026, v4.17.0 in February 2026, v4.16.1 earlier the same month. Frequent minor releases are a good sign for maintenance but a cost for anyone pinning versions, because an upgrade can move the underlying inference runtime or graphics dependency as well as the framework itself. The source is BSD-2-Clause, which is permissive and places few obligations on redistribution of the source. The README is explicit that this applies to the source code only, and that the prebuilt binaries link against third-party libraries under a range of licences including MIT, Apache 2.0 and LGPL. The README directs readers to the licences folder in the release for details. If you ship a product built on the binaries rather than on a source build, the LGPL components in that list are the ones to examine with your own legal counsel, because the licence of the FAST source does not determine the obligations attached to what it links against. The README also asks that the two cited papers be referenced in research use, which is a citation request rather than a licence term.
Editorial conclusion
Adopt FAST if you are building a C++ or Python application that must stream medical images, run inference and render results in one process, and you can commit to the OpenCL and OpenGL dependency chain. Do not adopt it if you need a pure CPU, pure Python pipeline, or a framework you can install and upgrade without touching graphics drivers. Verify first that your target hardware has a working OpenCL runtime and that the inference backend you intend to use (TensorRT, OpenVINO, TensorFlow or ONNX Runtime) is available for your platform, then run the C++ or Python tutorial that matches your data source before planning any integration.
Community notes