Atherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty.

Atherosclerotic plaque composition can indicate plaque vulnerability. We segment atherosclerotic plaque components from the carotid artery on a combination of in vivo MRI and CT-angiography (CTA) data using supervised voxelwise classification. In contrast to previous studies the ground truth for tra...

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Autores principales:	Arna van Engelen, Wiro J Niessen, Stefan Klein, Harald C Groen, Hence J M Verhagen, Jolanda J Wentzel, Aad van der Lugt, Marleen de Bruijne
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/dc917c5c69b0465ca098f504182113a7
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spelling	oai:doaj.org-article:dc917c5c69b0465ca098f504182113a72021-11-18T08:21:37ZAtherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty.1932-620310.1371/journal.pone.0094840https://doaj.org/article/dc917c5c69b0465ca098f504182113a72014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24762678/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Atherosclerotic plaque composition can indicate plaque vulnerability. We segment atherosclerotic plaque components from the carotid artery on a combination of in vivo MRI and CT-angiography (CTA) data using supervised voxelwise classification. In contrast to previous studies the ground truth for training is directly obtained from 3D registration with histology for fibrous and lipid-rich necrotic tissue, and with μCT for calcification. This registration does, however, not provide accurate voxelwise correspondence. We therefore evaluate three approaches that incorporate uncertainty in the ground truth used for training: I) soft labels are created by Gaussian blurring of the original binary histology segmentations to reduce weights at the boundaries between components, and are weighted by the estimated registration accuracy of the histology and in vivo imaging data (measured by overlap), II) samples are weighted by the local contour distance of the lumen and outer wall between histology and in vivo data, and III) 10% of each class is rejected by Gaussian outlier rejection. Classification was evaluated on the relative volumes (% of tissue type in the vessel wall) for calcified, fibrous and lipid-rich necrotic tissue, using linear discriminant (LDC) and support vector machine (SVM) classification. In addition, the combination of MRI and CTA data was compared to using only one imaging modality. Best results were obtained by LDC and outlier rejection: the volume error per vessel was 0.9±1.0% for calcification, 12.7±7.6% for fibrous and 12.1±8.1% for necrotic tissue, with Spearman rank correlation coefficients of 0.91 (calcification), 0.80 (fibrous) and 0.81 (necrotic). While segmentation using only MRI features yielded low accuracy for calcification, and segmentation using only CTA features yielded low accuracy for necrotic tissue, the combination of features from MRI and CTA gave good results for all studied components.Arna van EngelenWiro J NiessenStefan KleinHarald C GroenHence J M VerhagenJolanda J WentzelAad van der LugtMarleen de BruijnePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 4, p e94840 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Arna van Engelen Wiro J Niessen Stefan Klein Harald C Groen Hence J M Verhagen Jolanda J Wentzel Aad van der Lugt Marleen de Bruijne Atherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty.
description	Atherosclerotic plaque composition can indicate plaque vulnerability. We segment atherosclerotic plaque components from the carotid artery on a combination of in vivo MRI and CT-angiography (CTA) data using supervised voxelwise classification. In contrast to previous studies the ground truth for training is directly obtained from 3D registration with histology for fibrous and lipid-rich necrotic tissue, and with μCT for calcification. This registration does, however, not provide accurate voxelwise correspondence. We therefore evaluate three approaches that incorporate uncertainty in the ground truth used for training: I) soft labels are created by Gaussian blurring of the original binary histology segmentations to reduce weights at the boundaries between components, and are weighted by the estimated registration accuracy of the histology and in vivo imaging data (measured by overlap), II) samples are weighted by the local contour distance of the lumen and outer wall between histology and in vivo data, and III) 10% of each class is rejected by Gaussian outlier rejection. Classification was evaluated on the relative volumes (% of tissue type in the vessel wall) for calcified, fibrous and lipid-rich necrotic tissue, using linear discriminant (LDC) and support vector machine (SVM) classification. In addition, the combination of MRI and CTA data was compared to using only one imaging modality. Best results were obtained by LDC and outlier rejection: the volume error per vessel was 0.9±1.0% for calcification, 12.7±7.6% for fibrous and 12.1±8.1% for necrotic tissue, with Spearman rank correlation coefficients of 0.91 (calcification), 0.80 (fibrous) and 0.81 (necrotic). While segmentation using only MRI features yielded low accuracy for calcification, and segmentation using only CTA features yielded low accuracy for necrotic tissue, the combination of features from MRI and CTA gave good results for all studied components.
format	article
author	Arna van Engelen Wiro J Niessen Stefan Klein Harald C Groen Hence J M Verhagen Jolanda J Wentzel Aad van der Lugt Marleen de Bruijne
author_facet	Arna van Engelen Wiro J Niessen Stefan Klein Harald C Groen Hence J M Verhagen Jolanda J Wentzel Aad van der Lugt Marleen de Bruijne
author_sort	Arna van Engelen
title	Atherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty.
title_short	Atherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty.
title_full	Atherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty.
title_fullStr	Atherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty.
title_full_unstemmed	Atherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty.
title_sort	atherosclerotic plaque component segmentation in combined carotid mri and cta data incorporating class label uncertainty.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/dc917c5c69b0465ca098f504182113a7
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Atherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty.

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