Primary lung tumor segmentation from PET-CT volumes with spatial-topological constraint.

Cui, H; Wang, X; Lin, W; Zhou, J; Eberl, S; Feng, D; Fulham, M

Primary lung tumor segmentation from PET-CT volumes with spatial-topological constraint.

Cui, H Wang, X Lin, W Zhou, J

Eberl, S Feng, D Fulham, M

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Publisher:: SPRINGER HEIDELBERG
Publication Type:: Journal Article
Citation:: Int J Comput Assist Radiol Surg, 2016, 11, (1), pp. 19-29
Issue Date:: 2016-01

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Field	Value	Language
dc.contributor.author	Cui, H
dc.contributor.author	Wang, X
dc.contributor.author	Lin, W
dc.contributor.author	Zhou, J https://orcid.org/0000-0001-6034-644X
dc.contributor.author	Eberl, S
dc.contributor.author	Feng, D
dc.contributor.author	Fulham, M
dc.date.accessioned	2022-08-07T20:42:13Z
dc.date.available	2015-05-28
dc.date.available	2022-08-07T20:42:13Z
dc.date.issued	2016-01
dc.identifier.citation	Int J Comput Assist Radiol Surg, 2016, 11, (1), pp. 19-29
dc.identifier.issn	1861-6410
dc.identifier.issn	1861-6429
dc.identifier.uri	http://hdl.handle.net/10453/159748
dc.description.abstract	PURPOSE: Accurate lung tumor segmentation is a prerequisite for effective radiation therapy and surgical planning. However, tumor delineation is challenging when the tumor boundaries are indistinct on PET or CT. To address this problem, we developed a segmentation method to improve the delineation of primary lung tumors from PET-CT images. METHODS: We formulated the segmentation problem as a label information propagation process in an iterative manner. Our model incorporates spatial-topological information from PET and local intensity changes from CT. The topological information of the regions was extracted based on the metabolic activity of different tissues. The spatial-topological information moderates the amount of label information that a pixel receives: The label information attenuates as the spatial distance increases and when crossing different topological regions. Thus, the spatial-topological constraint assists accurate tumor delineation and separation. The label information propagation and transition model are solved under a random walk framework. RESULTS: Our method achieved an average DSC of 0.848 ± 0.036 and HD (mm) of 8.652 ± 4.532 on 40 patients with lung cancer. The t test showed a significant improvement (p value < 0.05) in segmentation accuracy when compared to eight other methods. Our method was better able to delineate tumors that had heterogeneous FDG uptake and which abutted adjacent structures that had similar densities. CONCLUSIONS: Our method, using a spatial-topological constraint, provided better lung tumor delineation, in particular, when the tumor involved or abutted the chest wall and the mediastinum.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	SPRINGER HEIDELBERG
dc.relation.ispartof	Int J Comput Assist Radiol Surg
dc.relation.isbasedon	10.1007/s11548-015-1231-0
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1103 Clinical Sciences
dc.subject.classification	Nuclear Medicine & Medical Imaging
dc.subject.mesh	Algorithms
dc.subject.mesh	Cone-Beam Computed Tomography
dc.subject.mesh	Humans
dc.subject.mesh	Lung
dc.subject.mesh	Lung Neoplasms
dc.subject.mesh	Positron-Emission Tomography
dc.subject.mesh	Lung
dc.subject.mesh	Humans
dc.subject.mesh	Lung Neoplasms
dc.subject.mesh	Positron-Emission Tomography
dc.subject.mesh	Algorithms
dc.subject.mesh	Cone-Beam Computed Tomography
dc.title	Primary lung tumor segmentation from PET-CT volumes with spatial-topological constraint.
dc.type	Journal Article
utslib.citation.volume	11
utslib.location.activity	Germany
utslib.for	1103 Clinical Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-07T20:42:09Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	1

Abstract:

PURPOSE: Accurate lung tumor segmentation is a prerequisite for effective radiation therapy and surgical planning. However, tumor delineation is challenging when the tumor boundaries are indistinct on PET or CT. To address this problem, we developed a segmentation method to improve the delineation of primary lung tumors from PET-CT images. METHODS: We formulated the segmentation problem as a label information propagation process in an iterative manner. Our model incorporates spatial-topological information from PET and local intensity changes from CT. The topological information of the regions was extracted based on the metabolic activity of different tissues. The spatial-topological information moderates the amount of label information that a pixel receives: The label information attenuates as the spatial distance increases and when crossing different topological regions. Thus, the spatial-topological constraint assists accurate tumor delineation and separation. The label information propagation and transition model are solved under a random walk framework. RESULTS: Our method achieved an average DSC of 0.848 ± 0.036 and HD (mm) of 8.652 ± 4.532 on 40 patients with lung cancer. The t test showed a significant improvement (p value < 0.05) in segmentation accuracy when compared to eight other methods. Our method was better able to delineate tumors that had heterogeneous FDG uptake and which abutted adjacent structures that had similar densities. CONCLUSIONS: Our method, using a spatial-topological constraint, provided better lung tumor delineation, in particular, when the tumor involved or abutted the chest wall and the mediastinum.

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http://hdl.handle.net/10453/159748