Semantic segmentation with DenseNets for carotid artery ultrasound plaque segmentation and CIMT estimation.

Artificial Intelligence in Medicine
Maria Del Mar VilaLaura Igual

Abstract

The measurement of carotid intima media thickness (CIMT) in ultrasound images can be used to detect the presence of atherosclerotic plaques. Usually, the CIMT estimation strategy is semi-automatic, since it requires: (1) a manual examination of the ultrasound image for the localization of a region of interest (ROI), a fast and useful operation when only a small number of images need to be measured; and (2) an automatic delineation of the CIM region within the ROI. The existing efforts for automating the process have replicated the same two-step structure, resulting in two consecutive independent approaches. In this work, we propose a fully automatic single-step approach based on semantic segmentation that allows us to segment the plaque and to estimate the CIMT in a fast and useful manner for large data sets of images. Our single-step approach is based on densely connected convolutional neural networks (DenseNets) for semantic segmentation of the whole image. It has two remarkable characteristics: (1) it avoids ROI definition, and (2) it captures multi-scale contextual information in the complete image interpretation, due to the concatenation of feature maps carried out in DenseNets. Once the input image is segmented, a straigh...Continue Reading

Citations

Sep 5, 2020·Ultrasonic Imaging·Nirvedh H MeshramTomy Varghese
Apr 10, 2021·Hypertension Research : Official Journal of the Japanese Society of Hypertension·Maria Del Mar VilaMaria Grau
Apr 25, 2021·Biomedical Engineering Online·Anna TargoszGrzegorz Mrugacz

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