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A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work...

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Autores principales: Feng, Yuan, Kawrakow, Iwan, Olsen, Jeff, Parikh, Parag J., Noel, Camille, Wooten, Omar, Du, Dongsu, Mutic, Sasa, Hu, Yanle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5875567/
https://www.ncbi.nlm.nih.gov/pubmed/27074465
http://dx.doi.org/10.1120/jacmp.v17i2.5820
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author Feng, Yuan
Kawrakow, Iwan
Olsen, Jeff
Parikh, Parag J.
Noel, Camille
Wooten, Omar
Du, Dongsu
Mutic, Sasa
Hu, Yanle
author_facet Feng, Yuan
Kawrakow, Iwan
Olsen, Jeff
Parikh, Parag J.
Noel, Camille
Wooten, Omar
Du, Dongsu
Mutic, Sasa
Hu, Yanle
author_sort Feng, Yuan
collection PubMed
description On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image‐guided radiotherapy (MR‐IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k‐means (FKM), k‐harmonic means (KHM), and reaction‐diffusion level set evolution (RD‐LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR‐TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR‐TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD‐LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP‐TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high‐contrast images (i.e., kidney), the thresholding method provided the best speed ([Formula: see text]) with a satisfying accuracy ([Formula: see text]). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on‐board MR‐IGRT system. PACS number(s): 87.57.nm, 87.57.N‐, 87.61.Tg
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spelling pubmed-58755672018-04-02 A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT Feng, Yuan Kawrakow, Iwan Olsen, Jeff Parikh, Parag J. Noel, Camille Wooten, Omar Du, Dongsu Mutic, Sasa Hu, Yanle J Appl Clin Med Phys Radiation Oncology Physics On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image‐guided radiotherapy (MR‐IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k‐means (FKM), k‐harmonic means (KHM), and reaction‐diffusion level set evolution (RD‐LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR‐TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR‐TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD‐LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP‐TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high‐contrast images (i.e., kidney), the thresholding method provided the best speed ([Formula: see text]) with a satisfying accuracy ([Formula: see text]). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on‐board MR‐IGRT system. PACS number(s): 87.57.nm, 87.57.N‐, 87.61.Tg John Wiley and Sons Inc. 2016-03-08 /pmc/articles/PMC5875567/ /pubmed/27074465 http://dx.doi.org/10.1120/jacmp.v17i2.5820 Text en © 2016 The Authors. This is an open access article under the terms of the http://creativecommons.org/licenses/by/3.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Radiation Oncology Physics
Feng, Yuan
Kawrakow, Iwan
Olsen, Jeff
Parikh, Parag J.
Noel, Camille
Wooten, Omar
Du, Dongsu
Mutic, Sasa
Hu, Yanle
A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT
title A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT
title_full A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT
title_fullStr A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT
title_full_unstemmed A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT
title_short A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT
title_sort comparative study of automatic image segmentation algorithms for target tracking in mr‐igrt
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5875567/
https://www.ncbi.nlm.nih.gov/pubmed/27074465
http://dx.doi.org/10.1120/jacmp.v17i2.5820
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