Bone Feature Segmentation in Ultrasound Spine Image with Robustness to Speckle and Regular Occlusion Noise

Huang, Z; Wang, L-W; Leung, FHF; Banerjee, Sunetra; Yang, D; Lee, T; Lyu, J; Ling, SH; Zheng, Y-P

Bone Feature Segmentation in Ultrasound Spine Image with Robustness to Speckle and Regular Occlusion Noise

Huang, Z Wang, L-W Leung, FHF Banerjee, Sunetra Yang, D Lee, T Lyu, J Ling, SH Zheng, Y-P

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Publisher:: IEEE
Publication Type:: Journal Article
Citation:: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020, 00, pp. 1566-1571
Issue Date:: 2020-10-11

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Field	Value	Language
dc.contributor.author	Huang, Z
dc.contributor.author	Wang, L-W
dc.contributor.author	Leung, FHF
dc.contributor.author	Banerjee, Sunetra
dc.contributor.author	Yang, D
dc.contributor.author	Lee, T
dc.contributor.author	Lyu, J
dc.contributor.author	Ling, SH
dc.contributor.author	Zheng, Y-P
dc.date.accessioned	2021-01-18T02:48:13Z
dc.date.available	2021-01-18T02:48:13Z
dc.date.issued	2020-10-11
dc.identifier.citation	2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020, 00, pp. 1566-1571
dc.identifier.uri	http://hdl.handle.net/10453/145464
dc.description.abstract	3D ultrasound imaging shows great promise for scoliosis diagnosis thanks to its low-costing, radiation-free and real-time characteristics. The key to accessing scoliosis by ultrasound imaging is to accurately segment the bone area and measure the scoliosis degree based on the symmetry of the bone features. The ultrasound images tend to contain many speckles and regular occlusion noise which is difficult, tedious and time-consuming for experts to find out the bony feature. In this paper, we propose a robust bone feature segmentation method based on the U-net structure for ultrasound spine Volume Projection Imaging (VPI) images. The proposed segmentation method introduces a total variance loss to reduce the sensitivity of the model to small-scale and regular occlusion noise. The proposed approach improves 2.3% of Dice score and 1% of AUC score as compared with the u-net model and shows high robustness to speckle and regular occlusion noise.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
dc.relation.isbasedon	10.1109/smc42975.2020.9283335
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Bone Feature Segmentation in Ultrasound Spine Image with Robustness to Speckle and Regular Occlusion Noise
dc.type	Journal Article
utslib.citation.volume	00
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2021-01-18T02:47:42Z
pubs.publication-status	Published
pubs.volume	00

Abstract:

3D ultrasound imaging shows great promise for scoliosis diagnosis thanks to its low-costing, radiation-free and real-time characteristics. The key to accessing scoliosis by ultrasound imaging is to accurately segment the bone area and measure the scoliosis degree based on the symmetry of the bone features. The ultrasound images tend to contain many speckles and regular occlusion noise which is difficult, tedious and time-consuming for experts to find out the bony feature. In this paper, we propose a robust bone feature segmentation method based on the U-net structure for ultrasound spine Volume Projection Imaging (VPI) images. The proposed segmentation method introduces a total variance loss to reduce the sensitivity of the model to small-scale and regular occlusion noise. The proposed approach improves 2.3% of Dice score and 1% of AUC score as compared with the u-net model and shows high robustness to speckle and regular occlusion noise.

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http://hdl.handle.net/10453/145464