VolumeNet: A Lightweight Parallel Network for Super-Resolution of MR and CT Volumetric Data

Deep learning-based super-resolution (SR) techniques have generally achieved excellent performance in the computer vision field. Recently, it has been proven that three-dimensional (3D) SR for medical volumetric data delivers better visual results than conventional two-dimensional (2D) processing. H...

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Veröffentlicht in:IEEE transactions on image processing Jg. 30; S. 1
Hauptverfasser: Li, Yinhao, Iwamoto, Yutaro, Lin, Lanfen, Xu, Rui, Chen, Yen-Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
3D Image Super-Resolution
Accuracy
Artificial neural networks
Computational modeling
Computed tomography
Computer vision
Convolution
Feature extraction
Feature maps
Image reconstruction
Image resolution
Lightweight
Lightweight Convolutional Neural Network
Magnetic resonance imaging
Mathematical models
Medical diagnostic imaging
Medical imaging
Medical Volumetric Image Processing
Modules
Parameters
Queues
Solid modeling
Three-dimensional displays
Training
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung:Deep learning-based super-resolution (SR) techniques have generally achieved excellent performance in the computer vision field. Recently, it has been proven that three-dimensional (3D) SR for medical volumetric data delivers better visual results than conventional two-dimensional (2D) processing. However, deepening and widening 3D networks increases training difficulty significantly due to the large number of parameters and small number of training samples. Thus, we propose a 3D convolutional neural network (CNN) for SR of medical volumetric data called ParallelNet using parallel connections. We construct a parallel connection structure based on the group convolution and feature aggregation to build a 3D CNN that is as wide as possible with few parameters. As a result, the model thoroughly learns more feature maps with larger receptive fields. In addition, to further improve accuracy, we present an efficient version of ParallelNet (called VolumeNet), which reduces the number of parameters and deepens ParallelNet using a proposed lightweight building block module called the Queue module. Unlike most lightweight CNNs based on depthwise convolutions, the Queue module is primarily constructed using separable 2D cross-channel convolutions. As a result, the number of network parameters and computational complexity can be reduced significantly while maintaining accuracy due to full channel fusion. Experimental results demonstrate that the proposed VolumeNet significantly reduces the number of model parameters and achieves high precision results compared to state-of-the-art methods.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2021.3076285