Journal of Pathology Informatics

SYMPOSIUM - ORIGINAL RESEARCH
Year
: 2013  |  Volume : 4  |  Issue : 2  |  Page : 3-

Automated segmentation of atherosclerotic histology based on pattern classification


Arna van Engelen1, Wiro J Niessen2, Stefan Klein1, Harald C Groen3, Kim van Gaalen4, Hence J Verhagen5, Jolanda J Wentzel4, Aad van der Lugt6, Marleen de Bruijne7 
1 Biomedical Imaging Group Rotterdam, Department of Medical Informatics and Radiology, Erasmus Medical Centre, Rotterdam, Netherlands
2 Biomedical Imaging Group Rotterdam, Department of Medical Informatics and Radiology, Erasmus Medical Centre, Rotterdam; Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, Netherlands
3 Department of Biomedical Engineering, Radiology and Nuclear Medicine, Erasmus Medical Centre, Rotterdam, Netherlands
4 Department of Biomedical Engineering, Erasmus Medical Centre, Rotterdam, Netherlands
5 Department of Vascular Surgery, Erasmus Medical Centre, Rotterdam, Netherlands
6 Department of Radiology, Erasmus Medical Centre, Rotterdam, Netherlands
7 Biomedical Imaging Group Rotterdam, Department of Medical Informatics and Radiology, Erasmus Medical Centre, Rotterdam, Netherlands; Department of Computer Science, University of Copenhagen, Denmark

Correspondence Address:
Arna van Engelen
Biomedical Imaging Group Rotterdam, Department of Medical Informatics and Radiology, Erasmus Medical Centre, Rotterdam, Netherlands

Background: Histology sections provide accurate information on atherosclerotic plaque composition, and are used in various applications. To our knowledge, no automated systems for plaque component segmentation in histology sections currently exist. Materials and Methods: We perform pixel-wise classification of fibrous, lipid, and necrotic tissue in Elastica Von Gieson-stained histology sections, using features based on color channel intensity and local image texture and structure. We compare an approach where we train on independent data to an approach where we train on one or two sections per specimen in order to segment the remaining sections. We evaluate the results on segmentation accuracy in histology, and we use the obtained histology segmentations to train plaque component classification methods in ex vivo Magnetic resonance imaging (MRI) and in vivo MRI and computed tomography (CT). Results: In leave-one-specimen-out experiments on 176 histology slices of 13 plaques, a pixel-wise accuracy of 75.7 ± 6.8% was obtained. This increased to 77.6 ± 6.5% when two manually annotated slices of the specimen to be segmented were used for training. Rank correlations of relative component volumes with manually annotated volumes were high in this situation (P = 0.82-0.98). Using the obtained histology segmentations to train plaque component classification methods in ex vivo MRI and in vivo MRI and CT resulted in similar image segmentations for training on the automated histology segmentations as for training on a fully manual ground truth. The size of the lipid-rich necrotic core was significantly smaller when training on fully automated histology segmentations than when manually annotated histology sections were used. This difference was reduced and not statistically significant when one or two slices per section were manually annotated for histology segmentation. Conclusions: Good histology segmentations can be obtained by automated segmentation, which show good correlations with ground truth volumes. In addition, these can be used to develop segmentation methods in other imaging modalities. Accuracy increases when one or two sections of the same specimen are used for training, which requires a limited amount of user interaction in practice.


How to cite this article:
van Engelen A, Niessen WJ, Klein S, Groen HC, van Gaalen K, Verhagen HJ, Wentzel JJ, van der Lugt A, de Bruijne M. Automated segmentation of atherosclerotic histology based on pattern classification.J Pathol Inform 2013;4:3-3


How to cite this URL:
van Engelen A, Niessen WJ, Klein S, Groen HC, van Gaalen K, Verhagen HJ, Wentzel JJ, van der Lugt A, de Bruijne M. Automated segmentation of atherosclerotic histology based on pattern classification. J Pathol Inform [serial online] 2013 [cited 2020 Jan 27 ];4:3-3
Available from: http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=2;spage=3;epage=3;aulast=van;type=0