Intelligent Segmentation of Intima-Media and Plaque Recognition in Carotid Artery Ultrasound Images.

Ultrasound imaging has been established as an effective method for measuring the thickness of the intima-media, the thickening of which, along with carotid plaque, is an indicator of cerebrovascular diseases. Here, a 2-D V-Net model that can automatically segment the intima-media in carotid artery ultrasound images is proposed. Moreover, a plaque recognition algorithm that automatically identifies plaque-affected areas is described. Performance tests to determine the average accuracy of the intima-media segmentation yielded the following results (expressed as lumen-intima boundary/media-adventitia boundary): intersection over union (IOU) of 0.752/0.813, pixel accuracy of 0.813/0.885 and Dice loss of 0.858/0.897. Finally, average IOU of 0.785, pixel accuracy of 0.825 and Dice loss of 0.866 were obtained for plaque recognition. These results satisfy the threshold for clinical application and indicate that the proposed model can assist doctors in making more efficient and accurate diagnoses.