3D Shape Recovery of Deformable Soft-tissue with Computed Tomography and Depth Scan

Song, JW; Wang, J; Zhao, L; Huang, S; Dissanayake, G

3D Shape Recovery of Deformable Soft-tissue with Computed Tomography and Depth Scan

Song, JW Wang, J Zhao, L

Huang, S

Dissanayake, G

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Publisher:: ARAA
Publication Type:: Conference Proceeding
Citation:: Website proceedings of the Australasian Conference on Robotics and Automation 2016, 2016, pp. 1 - 9
Issue Date:: 2016

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Field	Value	Language
dc.contributor.author	Song, JW	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date	2016-12-05	en_US
dc.date.issued	2016	en_US
dc.identifier.citation	Website proceedings of the Australasian Conference on Robotics and Automation 2016, 2016, pp. 1 - 9	en_US
dc.identifier.uri	http://hdl.handle.net/10453/87270
dc.description.abstract	Knowing the tissue environment accurately is very important in minimal invasive surgery (MIS). While, as the soft-tissues is deformable, reconstruction of the soft-tissues environment is challenging. This paper proposes a new framework for recovering the deformation of the soft-tissues by using a single depth sensor. This framework makes use of the morphology information of the soft-tissues from Xray computed tomography, and deforms it by the embedded deformation method. Here, the key is to build a distance field function of the scan from the depth sensor, which can be used to perform accurate model-to-scan deformation together with robust non-rigid shape registration in the same go. Simulations show that soft-tissue shape in the previous step can be ef- ficiently deformed to fit the partially observed scan in the current step by using the proposed method. And the results from the simulated sequential deformation of three different softtissues demonstrate the potential clinical value for MIS.	en_US
dc.publisher	ARAA	en_US
dc.relation.ispartof	Website proceedings of the Australasian Conference on Robotics and Automation 2016	en_US
dc.relation.ispartof	Australasian Conference on Robotics and Automation	en_US
dc.title	3D Shape Recovery of Deformable Soft-tissue with Computed Tomography and Depth Scan	en_US
dc.type	Conference Proceeding
utslib.location	Australia	en_US
utslib.location.activity	Queensland, Australia	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2016-12-07	en_US
pubs.place-of-publication	Australia	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2016-12-05	en_US

Abstract:

Knowing the tissue environment accurately is very important in minimal invasive surgery (MIS). While, as the soft-tissues is deformable, reconstruction of the soft-tissues environment is challenging. This paper proposes a new framework for recovering the deformation of the soft-tissues by using a single depth sensor. This framework makes use of the morphology information of the soft-tissues from Xray computed tomography, and deforms it by the embedded deformation method. Here, the key is to build a distance field function of the scan from the depth sensor, which can be used to perform accurate model-to-scan deformation together with robust non-rigid shape registration in the same go. Simulations show that soft-tissue shape in the previous step can be ef- ficiently deformed to fit the partially observed scan in the current step by using the proposed method. And the results from the simulated sequential deformation of three different softtissues demonstrate the potential clinical value for MIS.

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http://hdl.handle.net/10453/87270