Deep neural network-based semantic segmentation of microvascular decompression images

doi:10.3390/s21041167

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	Deep neural network-based semantic segmentation of microvascular decompression images
	R. Bai; S. Jiang; H. Sun; Y. Yang and G. Li
	2021
发表期刊	Sensors (Switzerland)
ISSN	14248220
卷号	21 期号:4 页码:1-16
摘要	Image semantic segmentation has been applied more and more widely in the fields of satellite remote sensing, medical treatment, intelligent transportation, and virtual reality. However, in the medical field, the study of cerebral vessel and cranial nerve segmentation based on true-color medical images is in urgent need and has good research and development prospects. We have extended the current state-of-the-art semantic-segmentation network DeepLabv3+ and used it as the basic framework. First, the feature distillation block (FDB) was introduced into the encoder structure to refine the extracted features. In addition, the atrous spatial pyramid pooling (ASPP) module was added to the decoder structure to enhance the retention of feature and boundary information. The proposed model was trained by fine tuning and optimizing the relevant parameters. Experimental results show that the encoder structure has better performance in feature refinement processing, improving target boundary segmentation precision, and retaining more feature information. Our method has a segmentation accuracy of 75.73%, which is 3% better than DeepLabv3+. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
DOI	10.3390/s21041167
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收录类别	SCI ; EI
引用统计	正在获取...
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65130
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	R. Bai,S. Jiang,H. Sun,et al. Deep neural network-based semantic segmentation of microvascular decompression images[J]. Sensors (Switzerland),2021,21(4):1-16.
APA	R. Bai,S. Jiang,H. Sun,&Y. Yang and G. Li.(2021).Deep neural network-based semantic segmentation of microvascular decompression images.Sensors (Switzerland),21(4),1-16.
MLA	R. Bai,et al."Deep neural network-based semantic segmentation of microvascular decompression images".Sensors (Switzerland) 21.4(2021):1-16.

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