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 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.
Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, Rotterdam, the Netherlands.
Arna
A
van Engelen
Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, Rotterdam, the Netherlands; Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands.
Wiro J
WJ
Niessen
Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, Rotterdam, the Netherlands.
Stefan
S
Klein
Department of Biomedical Engineering, Erasmus MC, Rotterdam, the Netherlands; Department of Radiology, Erasmus MC, Rotterdam, the Netherlands; Department of Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.
Harald C
HC
Groen
Department of Vascular Surgery, Erasmus MC, Rotterdam, the Netherlands.
Hence J M
HJ
Verhagen
Department of Biomedical Engineering, Erasmus MC, Rotterdam, the Netherlands.
Jolanda J
JJ
Wentzel
Department of Radiology, Erasmus MC, Rotterdam, the Netherlands.
Aad
A
van der Lugt
Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, Rotterdam, the Netherlands; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
Marleen
M
de Bruijne
D000368:Aged; D050197:Atherosclerosis / Q000000981:diagnostic imaging* / Q000473:pathology; D002339:Carotid Arteries / Q000000981:diagnostic imaging / Q000473:pathology; D002340:Carotid Artery Diseases / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D018810:Magnetic Resonance Angiography; D008297:Male; D008875:Middle Aged; D058226:Plaque, Atherosclerotic / Q000000981:diagnostic imaging* / Q000473:pathology; D055114:X-Ray Microtomography