A Structure-Affinity Dual Attention-based Network to Segment Spine for Scoliosis Assessment

Xie, H; Huang, Z; Leung, FHF; Ju, Y; Zheng, Y-P; Ling, SH

A Structure-Affinity Dual Attention-based Network to Segment Spine for Scoliosis Assessment

Xie, H Huang, Z Leung, FHF Ju, Y Zheng, Y-P Ling, SH

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2024, 00, pp. 1567-1574
Issue Date:: 2024-01-18

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Field	Value	Language
dc.contributor.author	Xie, H
dc.contributor.author	Huang, Z
dc.contributor.author	Leung, FHF
dc.contributor.author	Ju, Y
dc.contributor.author	Zheng, Y-P
dc.contributor.author	Ling, SH
dc.contributor.editor	Jiang, X
dc.contributor.editor	Wang, H
dc.contributor.editor	Alhajj, R
dc.contributor.editor	Hu, X
dc.contributor.editor	Engel, F
dc.contributor.editor	Mahmud, M
dc.contributor.editor	Pisanti, N
dc.contributor.editor	Cui, X
dc.contributor.editor	Song, H
dc.date	2023-12-05
dc.date.accessioned	2024-05-13T04:23:40Z
dc.date.available	2024-05-13T04:23:40Z
dc.date.issued	2024-01-18
dc.identifier.citation	2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2024, 00, pp. 1567-1574
dc.identifier.isbn	979-8-3503-3749-5
dc.identifier.issn	2156-1125
dc.identifier.uri	http://hdl.handle.net/10453/178904
dc.description.abstract	Ultrasound volume projection imaging has shown great promise to visualize spine features and diagnose scoliosis thanks to its harmlessness cheapness and efficiency The key to measuring spine deformity and assessing scoliosis is to accurately segment the spine bone features In this paper we propose a novel structure affinity dual attention based network SADANet for effective spine segmentation Global channel attention module and spatial criss cross attention module are combined in a parallel manner to generate rich global context of spine images Meanwhile we present a structure affinity strategy to encode the structural knowledge of spine bones into the semantic representations By this means the network can capture both contextual and structural information Experiments show that our proposed algorithm achieves promising performance on spine segmentation as compared with other state of the art candidates which makes it an appealing approach for intelligent scoliosis assessment
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
dc.relation.ispartof	2023 IEEE International Conference on Bioinformatics and Biomedicine
dc.relation.isbasedon	10.1109/bibm58861.2023.10385419
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	A Structure-Affinity Dual Attention-based Network to Segment Spine for Scoliosis Assessment
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Istanbul, Turkiye
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
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 Electrical and Data Engineering
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2026-01-18T00:00:00+1000Z
dc.date.updated	2024-05-13T04:23:37Z
pubs.finish-date	2023-12-08
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2023-12-05
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Ultrasound volume projection imaging has shown great promise to visualize spine features and diagnose scoliosis thanks to its harmlessness cheapness and efficiency The key to measuring spine deformity and assessing scoliosis is to accurately segment the spine bone features In this paper we propose a novel structure affinity dual attention based network SADANet for effective spine segmentation Global channel attention module and spatial criss cross attention module are combined in a parallel manner to generate rich global context of spine images Meanwhile we present a structure affinity strategy to encode the structural knowledge of spine bones into the semantic representations By this means the network can capture both contextual and structural information Experiments show that our proposed algorithm achieves promising performance on spine segmentation as compared with other state of the art candidates which makes it an appealing approach for intelligent scoliosis assessment

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/178904