MRI-based brain tumor segmentation using FPGA-accelerated neural network

MRI-based brain tumor segmentation using FPGA-accelerated neural network

Background: Brain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed...

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Bibliographic Details
Main Authors: Cao, W. (Author), Ding, S. (Author), Fan, X. (Author), Feng, X. (Author), Huang, Z. (Author), Shi, Z. (Author), Wang, L. (Author), Wu, G. (Author), Xiong, S. (Author), Yu, J. (Author), Zhou, X. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
Automatic segmentations
Brain
Brain Neoplasms
brain tumor
Brain tumor segmatation
Brain tumor segmentation
Complex networks
Computational complexity
Computer aided detection systems
Computer aided logic design
Deep learning
Deep neural networks
Diagnosis
diagnostic imaging
Electric power utilization
Energy efficiency
Field programmable gate arrays (FPGA)
FPGA acceleration
Graphics processing unit
Green computing
human
Humans
image processing
Image processing and analysis
Image Processing, Computer-Assisted
Image segmentation
Learning neural networks
Learning-based methods
Magnetic Resonance Imaging
Medical imaging
Multilayer neural networks
Neural network
Neural Networks, Computer
nuclear magnetic resonance imaging
Segmentation accuracy
Traditional computing
Tumors
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