Carotid artery wall segmentation in ultrasound image sequences using a deep convolutional neural network

2022/01/28 by Nolann Lainé, Lainé, Nolann, Guillaume Zahnd +4
Medicine · #Cardiovascular Health and Disease Prevention #Cerebrovascular and Carotid Artery Diseases #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #I.4.6 #Image and Video Processing (eess.IV) #Retinal Imaging and Analysis #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2201.12152

openalex publication_date 2022/01/28 · openalex created_date 2022/12/26 · openalex updated_date 2026/07/28

Abstract

The objective of this study is the segmentation of the intima-media complex of the common carotid artery, on longitudinal ultrasound images, to measure its thickness. We propose a fully automatic region-based segmentation method, involving a supervised region-based deep-learning approach based on a dilated U-net network. It was trained and evaluated using a 5-fold cross-validation on a multicenter database composed of 2176 images annotated by two experts. The resulting mean absolute difference (<120 um) compared to reference annotations was less than the inter-observer variability (180 um). With a 98.7% success rate, i.e., only 1.3% cases requiring manual correction, the proposed method has been shown to be robust and thus may be recommended for use in clinical practice.

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