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Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours

Purpose. To achieve rapid automated delineation of gross target volume (GTV) and to quantify changes in volume/position of the target for radiotherapy planning using four-dimensional (4D) CT. Methods and Materials. Novel morphological processing and successive localization (MPSL) algorithms were des...

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Autores principales: Chebrolu, Venkata V., Saenz, Daniel, Tewatia, Dinesh, Sethares, William A., Cannon, George, Paliwal, Bhudatt R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4137600/
https://www.ncbi.nlm.nih.gov/pubmed/25165581
http://dx.doi.org/10.1155/2014/547075
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author Chebrolu, Venkata V.
Saenz, Daniel
Tewatia, Dinesh
Sethares, William A.
Cannon, George
Paliwal, Bhudatt R.
author_facet Chebrolu, Venkata V.
Saenz, Daniel
Tewatia, Dinesh
Sethares, William A.
Cannon, George
Paliwal, Bhudatt R.
author_sort Chebrolu, Venkata V.
collection PubMed
description Purpose. To achieve rapid automated delineation of gross target volume (GTV) and to quantify changes in volume/position of the target for radiotherapy planning using four-dimensional (4D) CT. Methods and Materials. Novel morphological processing and successive localization (MPSL) algorithms were designed and implemented for achieving autosegmentation. Contours automatically generated using MPSL method were compared with contours generated using state-of-the-art deformable registration methods (using Elastix© and MIMVista software). Metrics such as the Dice similarity coefficient, sensitivity, and positive predictive value (PPV) were analyzed. The target motion tracked using the centroid of the GTV estimated using MPSL method was compared with motion tracked using deformable registration methods. Results. MPSL algorithm segmented the GTV in 4DCT images in 27.0 ± 11.1 seconds per phase (512 × 512 resolution) as compared to 142.3 ± 11.3 seconds per phase for deformable registration based methods in 9 cases. Dice coefficients between MPSL generated GTV contours and manual contours (considered as ground-truth) were 0.865 ± 0.037. In comparison, the Dice coefficients between ground-truth and contours generated using deformable registration based methods were 0.909 ± 0.051. Conclusions. The MPSL method achieved similar segmentation accuracy as compared to state-of-the-art deformable registration based segmentation methods, but with significant reduction in time required for GTV segmentation.
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spelling pubmed-41376002014-08-27 Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours Chebrolu, Venkata V. Saenz, Daniel Tewatia, Dinesh Sethares, William A. Cannon, George Paliwal, Bhudatt R. Radiol Res Pract Research Article Purpose. To achieve rapid automated delineation of gross target volume (GTV) and to quantify changes in volume/position of the target for radiotherapy planning using four-dimensional (4D) CT. Methods and Materials. Novel morphological processing and successive localization (MPSL) algorithms were designed and implemented for achieving autosegmentation. Contours automatically generated using MPSL method were compared with contours generated using state-of-the-art deformable registration methods (using Elastix© and MIMVista software). Metrics such as the Dice similarity coefficient, sensitivity, and positive predictive value (PPV) were analyzed. The target motion tracked using the centroid of the GTV estimated using MPSL method was compared with motion tracked using deformable registration methods. Results. MPSL algorithm segmented the GTV in 4DCT images in 27.0 ± 11.1 seconds per phase (512 × 512 resolution) as compared to 142.3 ± 11.3 seconds per phase for deformable registration based methods in 9 cases. Dice coefficients between MPSL generated GTV contours and manual contours (considered as ground-truth) were 0.865 ± 0.037. In comparison, the Dice coefficients between ground-truth and contours generated using deformable registration based methods were 0.909 ± 0.051. Conclusions. The MPSL method achieved similar segmentation accuracy as compared to state-of-the-art deformable registration based segmentation methods, but with significant reduction in time required for GTV segmentation. Hindawi Publishing Corporation 2014 2014-08-03 /pmc/articles/PMC4137600/ /pubmed/25165581 http://dx.doi.org/10.1155/2014/547075 Text en Copyright © 2014 Venkata V. Chebrolu et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Chebrolu, Venkata V.
Saenz, Daniel
Tewatia, Dinesh
Sethares, William A.
Cannon, George
Paliwal, Bhudatt R.
Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours
title Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours
title_full Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours
title_fullStr Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours
title_full_unstemmed Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours
title_short Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours
title_sort rapid automated target segmentation and tracking on 4d data without initial contours
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4137600/
https://www.ncbi.nlm.nih.gov/pubmed/25165581
http://dx.doi.org/10.1155/2014/547075
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