High performance neural network inference, streaming, and visualization of medical images using FAST
Open accessAbstract
Deep convolutional neural networks have quickly become the standard for medical image analysis. Although there are many frameworks focusing on training neural networks, there are few that focus on high performance inference and visualization of medical images. Neural network inference requires an inference engine (IE), and there are currently several IEs available including Intel's OpenVINO, NVIDIA's TensorRT, and Google's TensorFlow which supports multiple backends, including NVIDIA's cuDNN, AMD's ROCm and Intel's MKL-DNN. These IEs only work on specific processors and have completely different application programming interfaces (APIs). In this paper, we presents methods for extending FAST, an open-source high performance framework for medical imaging, to use any IE with a common programming interface. Thereby making it easier for users to deploy and test their neural networks on different processors. This article provides an overview of current IEs and how they can be combined with existing software such as FAST. The methods are demonstrated and evaluated on three performance demanding medical use cases: real-time ultrasound image segmentation, computed tomography (CT) volume segmentation, and patch-wise classification of whole slide microscopy images. Runtime performance was measured on the three use cases with several different IEs and processors. This revealed that the choice of IE and processor can affect performance of medical neural network image analysis considerably. In the most extreme case of processing 171 ultrasound frames, the difference between the fastest and slowest configuration were half a second vs. 24 seconds. For volume processing, using the CPU or the GPU, showed a difference of 2 vs. 53 seconds, and for processing an whole slide microscopy image, the difference was 81 seconds vs. almost 16 minutes. Source code, binary releases and test data can be found online on GitHub at https://github.com/smistad/FAST/.
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TensorRT can provide low-latency, high-throughput deployment reasoning for embedded platforms such as autonomous driving and security monitoring.
“TensorRT [38] to accelerate model inference. The basic principle is to optimize the GPU characteristics, network layer fusion computing, and accuracy during inference. It can provide low-latency, high-throughput deployment reasoning for embedded platforms such as autonomous driving and security monitoring.”
From Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles (2022) · cites this paper as [38]
The “3) TENSORRT” section says TensorRT enables high-performance inference on NVIDIA GPUs through precision calibration, layer fusing, and kernel auto-tuning. The Discussion reports 2-millisecond ultrasound-image segmentation inference, supporting low latency in that medical-imaging test. The paper does not establish throughput or deployment on embedded platforms for autonomous driving or security monitoring. (LLM, conf=0.88)
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- Authors
- Smistad, E., Ostvik, A., & Pedersen, A.
- Affiliations
- Norwegian University of Science and Technology (NO)
- SINTEF (NO)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 136310-136321
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2973444803
- Cited by
- 32
- References
- 15
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 7 · 2023: 2 · 2022: 7 · 2021: 5 · 2020: 5
- FWCI
- 1.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Funders
- Norges Forskningsråd (270941, 237887)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- neural network inference, inference engine, FAST framework, medical image analysis, ultrasound image segmentation, GPU acceleration, real-time processing, streaming, visualization, open source software
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