Publication Type:Journal Article
Source:IEEE J Biomed Health Inform (2016)
ISBN:2168-2208 (Electronic)<br/>2168-2194 (Linking)
Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estima-tion of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate be-tween the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique ca-pabilities of the emerging deep learning framework. More specif-ically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network that will automatically ex-tract from the images the information that is optimal for the identification of the different plaque constituents. We used ap-proximately 90,000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed convolutional neural network (CNN). The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the poten-tial of deep learning for the challenging task of automatic charac-terization of plaque composition in carotid ultrasound.