18th AIAI 2022, 17 - 20 June 2022, Greece

Deep learning-based segmentation of the atherosclerotic carotid plaque in ultrasonic images

Georgia Liapi, Efthyvoulos Kyriacou, Christos Loizou, Andreas Panayides, Constantinos Pattichis, Andrew Nicolaides


  Early stroke risk stratification in individuals with carotid atherosclerosis is of great importance, especially in high-risk asymptomatic (AS) cases. In this study, we present a new computer-aided diagnostic (CAD) system for the automated segmentation of the atherosclerotic plaque in carotid ultrasound (US) images and the extraction of a refined set of ultrasonic features to robustly characterize plaques in carotid US images and videos (AS vs symptomatic (SY)). So far, we trained a UNet model (16 to 256 neurons in the contracting path; the reverse, for the expanding path), starting from a dataset of 201 (AS=109 and SY=92) carotid US videos of atherosclerotic plaques, from which their first frames were extracted to prepare three subsets, a training, an internal validation, and final evaluation set, with 150, 30 and 15 images, respectively. The automated segmentations were evaluated based on manual segmentations, performed by a vascular surgeon. To assess our model’s capacity to segment plaques in previously unseen images, we calculated 4 evaluation metrics (mean±std). The evaluation of the proposed model yielded a 0.736±0.10 Dice similarity score (DSC), a 0.583±0.12 intersection of union (IoU), a 0.728±0.10 Cohen’s Kappa coefficient (KI) and a 0.65±0.19 Hausdorff distance. The proposed segmentation workflow will be further optimized and evaluated, using a larger dataset and more neurons in each UNet layer, as in the original model architecture. Our results are close to others published in relevant studies.  

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