Carotid wall volume quantification from magnetic resonance images using deformable model fitting and learning-based correction of systematic errors.

We present a method for carotid vessel wall volume quantification from magnetic resonance imaging (MRI). The method combines lumen and outer wall segmentation based on deformable model fitting with a learning-based segmentation correction step. After selecting two initialization points, the vessel wall volume in a region around the bifurcation is automatically determined. The method was trained on eight datasets (16 carotids) from a population-based study in the elderly for which one observer manually annotated both the lumen and outer wall. An evaluation was carried out on a separate set of 19 datasets (38 carotids) from the same study for which two observers made annotations. Wall volume and normalized wall index measurements resulting from the manual annotations were compared to the automatic measurements. Our experiments show that the automatic method performs comparably to the manual measurements. All image data and annotations used in this study together with the measurements are made available through the website http://ergocar.bigr.nl.