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Automatic lumbar spinal MRI image segmentation with a multi-scale attention network
Li HX(李海昕)1,2,3,4,5; Luo HB(罗海波)1,2,4,5; Huan, Wang6; Shi ZL(史泽林)1,2,4,5; Yan, Chongnan6; Wang, Lanbo6; Mu, Yueming6; Liu YP(刘云鹏)1,2,4,5
Department光电信息技术研究室
Source PublicationNeural Computing and Applications
ISSN0941-0643
2021
Pages1-14
Indexed BySCI ; EI
EI Accession number20211110087485
WOS IDWOS:000627241100006
Contribution Rank1
KeywordLumbar spinal stenosis Magnetic resonance imaging image Deep learning Dual-branch multi-scale attention module Feature extraction
Abstract

Lumbar spinal stenosis (LSS) is a lumbar disease with a high incidence in recent years. Accurate segmentation of the vertebral body, lamina and dural sac is a key step in the diagnosis of LSS. This study presents an lumbar spine magnetic resonance imaging image segmentation method based on deep learning. In addition, we define the quantitative evaluation methods of two clinical indicators (that is the anteroposterior diameter of the spinal canal and the cross-sectional area of the dural sac) to assist LSS diagnosis. To improve the segmentation performance, a dual-branch multi-scale attention module is embedded into the network. It contains multi-scale feature extraction based on three 3 × 3 convolution operators and vital information selection based on attention mechanism. In the experiment, we used lumbar datasets from the spine surgery department of Shengjing Hospital of China Medical University to evaluate the effect of the method embedded the dual-branch multi-scale attention module. Compared with other state-of-the-art methods, the average dice similarity coefficient was improved from 0.9008 to 0.9252 and the average surface distance was decreased from 6.40 to 2.71 mm.

Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28631
Collection光电信息技术研究室
Corresponding AuthorLuo HB(罗海波)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, No. 114 Nanta Street, Shenhe District, Shenyang City, Liaoning Province, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang Institute of Automation, No. 114 Nanta Street, Shenhe District, Shenyang City, Liaoning Province, China
3.University of Chinese Academy of Sciences, No. 52 Sanlihe Road, Xicheng District, Beijing, China
4.Key Laboratory of Opto-Electronic Information Processing, No. 114 Nanta Street, Shenhe District, Shenyang City, Liaoning Province, China
5.The Key Lab of Image Understanding and Computer Vision, No. 114 Nanta Street, Shenhe District, Shenyang City, Liaoning Province, China
6.Department of Spine Surgery, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang City, Liaoning Province, China
Recommended Citation
GB/T 7714
Li HX,Luo HB,Huan, Wang,et al. Automatic lumbar spinal MRI image segmentation with a multi-scale attention network[J]. Neural Computing and Applications,2021:1-14.
APA Li HX.,Luo HB.,Huan, Wang.,Shi ZL.,Yan, Chongnan.,...&Liu YP.(2021).Automatic lumbar spinal MRI image segmentation with a multi-scale attention network.Neural Computing and Applications,1-14.
MLA Li HX,et al."Automatic lumbar spinal MRI image segmentation with a multi-scale attention network".Neural Computing and Applications (2021):1-14.
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