Automated segmentation of atherosclerotic histology based on pattern classification

• Main Authors: Arna van Engelen (Author), Wiro J Niessen (Author), Stefan Klein (Author), Harald C Groen (Author), Kim van Gaalen (Author), Hence J Verhagen (Author), Jolanda J Wentzel (Author), Aad van der Lugt (Author), Marleen de Bruijne (Author)
• Format: Book
• Published: Elsevier, 2013-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2153-3539; 2153-3539; 10.4103/2153-3539.109844
• Keywords: Histology; Segmentation; Classification; Atherosclerosis; Computer applications to medicine. Medical informatics; R858-859.7; Pathology; RB1-214
• Source: Journal of Pathology Informatics, Vol 4, Iss 2, Pp 3-3 (2013)

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.