Cargando…

Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method

To assist radiation oncologists in the delineation of tumor regions during treatment planning for lung cancer, we have proposed an automated contouring algorithm based on an optimum contour selection (OCS) method for treatment planning computed tomography (CT) images with positron emission tomograph...

Descripción completa

Detalles Bibliográficos
Autores principales: Jin, Ze, Arimura, Hidetaka, Shioyama, Yoshiyuki, Nakamura, Katsumasa, Kuwazuru, Jumpei, Magome, Taiki, Yabu-Uchi, Hidetake, Honda, Hiroshi, Hirata, Hideki, Sasaki, Masayuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4229921/
https://www.ncbi.nlm.nih.gov/pubmed/24980022
http://dx.doi.org/10.1093/jrr/rru056
_version_ 1782344191807848448
author Jin, Ze
Arimura, Hidetaka
Shioyama, Yoshiyuki
Nakamura, Katsumasa
Kuwazuru, Jumpei
Magome, Taiki
Yabu-Uchi, Hidetake
Honda, Hiroshi
Hirata, Hideki
Sasaki, Masayuki
author_facet Jin, Ze
Arimura, Hidetaka
Shioyama, Yoshiyuki
Nakamura, Katsumasa
Kuwazuru, Jumpei
Magome, Taiki
Yabu-Uchi, Hidetake
Honda, Hiroshi
Hirata, Hideki
Sasaki, Masayuki
author_sort Jin, Ze
collection PubMed
description To assist radiation oncologists in the delineation of tumor regions during treatment planning for lung cancer, we have proposed an automated contouring algorithm based on an optimum contour selection (OCS) method for treatment planning computed tomography (CT) images with positron emission tomography (PET)/CT images. The basic concept of the OCS is to select a global optimum object contour based on multiple active delineations with a level set method around tumors. First, the PET images were registered to the planning CT images by using affine transformation matrices. The initial gross tumor volume (GTV) of each lung tumor was identified by thresholding the PET image at a certain standardized uptake value, and then each initial GTV location was corrected in the region of interest of the planning CT image. Finally, the contours of final GTV regions were determined in the planning CT images by using the OCS. The proposed method was evaluated by testing six cases with a Dice similarity coefficient (DSC), which denoted the degree of region similarity between the GTVs contoured by radiation oncologists and the proposed method. The average three-dimensional DSC for the six cases was 0.78 by the proposed method, but only 0.34 by a conventional method based on a simple level set method. The proposed method may be helpful for treatment planners in contouring the GTV regions.
format Online
Article
Text
id pubmed-4229921
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-42299212014-11-21 Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method Jin, Ze Arimura, Hidetaka Shioyama, Yoshiyuki Nakamura, Katsumasa Kuwazuru, Jumpei Magome, Taiki Yabu-Uchi, Hidetake Honda, Hiroshi Hirata, Hideki Sasaki, Masayuki J Radiat Res Oncology To assist radiation oncologists in the delineation of tumor regions during treatment planning for lung cancer, we have proposed an automated contouring algorithm based on an optimum contour selection (OCS) method for treatment planning computed tomography (CT) images with positron emission tomography (PET)/CT images. The basic concept of the OCS is to select a global optimum object contour based on multiple active delineations with a level set method around tumors. First, the PET images were registered to the planning CT images by using affine transformation matrices. The initial gross tumor volume (GTV) of each lung tumor was identified by thresholding the PET image at a certain standardized uptake value, and then each initial GTV location was corrected in the region of interest of the planning CT image. Finally, the contours of final GTV regions were determined in the planning CT images by using the OCS. The proposed method was evaluated by testing six cases with a Dice similarity coefficient (DSC), which denoted the degree of region similarity between the GTVs contoured by radiation oncologists and the proposed method. The average three-dimensional DSC for the six cases was 0.78 by the proposed method, but only 0.34 by a conventional method based on a simple level set method. The proposed method may be helpful for treatment planners in contouring the GTV regions. Oxford University Press 2014-11 2014-06-30 /pmc/articles/PMC4229921/ /pubmed/24980022 http://dx.doi.org/10.1093/jrr/rru056 Text en © The Author 2014. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Oncology
Jin, Ze
Arimura, Hidetaka
Shioyama, Yoshiyuki
Nakamura, Katsumasa
Kuwazuru, Jumpei
Magome, Taiki
Yabu-Uchi, Hidetake
Honda, Hiroshi
Hirata, Hideki
Sasaki, Masayuki
Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method
title Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method
title_full Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method
title_fullStr Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method
title_full_unstemmed Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method
title_short Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method
title_sort computer-assisted delineation of lung tumor regions in treatment planning ct images with pet/ct image sets based on an optimum contour selection method
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4229921/
https://www.ncbi.nlm.nih.gov/pubmed/24980022
http://dx.doi.org/10.1093/jrr/rru056
work_keys_str_mv AT jinze computerassisteddelineationoflungtumorregionsintreatmentplanningctimageswithpetctimagesetsbasedonanoptimumcontourselectionmethod
AT arimurahidetaka computerassisteddelineationoflungtumorregionsintreatmentplanningctimageswithpetctimagesetsbasedonanoptimumcontourselectionmethod
AT shioyamayoshiyuki computerassisteddelineationoflungtumorregionsintreatmentplanningctimageswithpetctimagesetsbasedonanoptimumcontourselectionmethod
AT nakamurakatsumasa computerassisteddelineationoflungtumorregionsintreatmentplanningctimageswithpetctimagesetsbasedonanoptimumcontourselectionmethod
AT kuwazurujumpei computerassisteddelineationoflungtumorregionsintreatmentplanningctimageswithpetctimagesetsbasedonanoptimumcontourselectionmethod
AT magometaiki computerassisteddelineationoflungtumorregionsintreatmentplanningctimageswithpetctimagesetsbasedonanoptimumcontourselectionmethod
AT yabuuchihidetake computerassisteddelineationoflungtumorregionsintreatmentplanningctimageswithpetctimagesetsbasedonanoptimumcontourselectionmethod
AT hondahiroshi computerassisteddelineationoflungtumorregionsintreatmentplanningctimageswithpetctimagesetsbasedonanoptimumcontourselectionmethod
AT hiratahideki computerassisteddelineationoflungtumorregionsintreatmentplanningctimageswithpetctimagesetsbasedonanoptimumcontourselectionmethod
AT sasakimasayuki computerassisteddelineationoflungtumorregionsintreatmentplanningctimageswithpetctimagesetsbasedonanoptimumcontourselectionmethod