ArticleInternational journal of computer assisted radiology and surgery2015
FAST: framework for heterogeneous medical image computing and visualization.
Article in International journal of computer assisted radiology and surgery, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
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- Generative augmentations for improved cardiac ultrasound segmentation using diffusion models.Scientific reports · 2025Article
- Segmentation of Non-Small Cell Lung Carcinomas: Introducing DRU-Net and Multi-Lens Distortion.Journal of imaging · 2025Article
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- AeroPath: An airway segmentation benchmark dataset with challenging pathology and baseline method.PloS one · 2024Article
- Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions.European heart journal. Imaging methods and practice · 2023Article
- Geometrically accurate real-time volumetric visualization of the middle ear using optical coherence tomography.Biomedical optics express · 2023Article
- Integrating computational fluid dynamics data into medical image visualization workflows via DICOM.International journal of computer assisted radiology and surgery · 2022Article
- Using Image Recognition to Process Unbalanced Data in Genetic Diseases From Biobanks.Frontiers in genetics · 2022Article
- Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology.Frontiers in medicine · 2021Article
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- AVATREE: An open-source computational modelling framework modelling Anatomically Valid Airway TREE conformations.PloS one · 2020Article
- Instant Feedback Rapid Prototyping for GPU-Accelerated Computation, Manipulation, and Visualization of Multidimensional Data.International journal of biomedical imaging · 2018Article
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Authors and funding
3 authors.
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Abstract
purposeComputer systems are becoming increasingly heterogeneous in the sense that they consist of different processors, such as multi-core CPUs and graphic processing units. As the amount of medical image data increases, it is crucial to exploit the computational power of these processors. However, this is currently difficult due to several factors, such as driver errors, processor differences, and the need for low-level memory handling. This paper presents a novel FrAmework for heterogeneouS medical image compuTing and visualization (FAST). The framework aims to make it easier to simultaneously process and visualize medical images efficiently on heterogeneous systems.
methodsFAST uses common image processing programming paradigms and hides the details of memory handling from the user, while enabling the use of all processors and cores on a system. The framework is open-source, cross-platform and available online.
resultsCode examples and performance measurements are presented to show the simplicity and efficiency of FAST. The results are compared to the insight toolkit (ITK) and the visualization toolkit (VTK) and show that the presented framework is faster with up to 20 times speedup on several common medical imaging algorithms.
conclusionsFAST enables efficient medical image computing and visualization on heterogeneous systems. Code examples and performance evaluations have demonstrated that the toolkit is both easy to use and performs better than existing frameworks, such as ITK and VTK.
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