Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks

Abstract

Atherosclerosis is the leading underlying pathologic process that results in cardiovascular diseases, which represents the main cause of death and disability in the world. The atherosclerotic process is a complex degenerative condition mainly affecting the medium- and large-size arteries, which begins in childhood and may remain unnoticed during decades. The intima-media thickness (IMT) of the common carotid artery (CCA) has emerged as one of the most powerful tool for the evaluation of preclinical atherosclerosis. IMT is measured by means of B-mode ultrasound images, which is a non-invasive and relatively low-cost technique. This paper proposes an effective image segmentation method for the IMT measurement in an automatic way. With this purpose, segmentation is posed as a pattern recognition problem, and a combination of artificial neural networks has been trained to solve this task. In particular, multi-layer perceptrons trained under the scaled conjugate gradient algorithm have been used. The suggested approach is tested on a set of 60 longitudinal ultrasound images of the CCA by comparing the automatic segmentation with four manual tracings. Moreover, the intra- and inter-observer errors have also been assessed. Despite of the simplicity of our approach, several quantitative statistical evaluations have shown its accuracy and robustness.

Potential for Orogastric Tube Attached Transesophageal Echocardiography to Improve Intraoperative Image Quality

“…Modern echocardiographic machines have an automatic adjustment system to optimize spatial resolution and postprocessing by time gain compensation, receiver gain, and system compression map. [8][9][10][11]14,15 However, even with these technologies, intermediating gas between the ultrasound transducer and object will affect image quality significantly because of ultrasound velocity difference in different media. [11][12][13]16,17 In our study, we were not able to measure the amount of air suctioned because the upper gastrointestinal tract is an open system and thus the suctioned amount of air likely would not be related to post-suction stomach size reduction.…”

Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms

“…[16][17][18][19][20][21] In echocardiography, machine learning methods have been applied for 2-D cardiac view classification, 22 LV boundary segmentation, 23 the measurement of cardiac parameters in M-and B-mode echocardiography, 24 fully automated 3-D segmentation of the cardiac chambers, valves, and leaflets. 25,26 Machine learning methods have also been applied to ultrasound image data for detection of breast lesions, 27,28 detection and evaluation of atherosclerosis, 29 staging of chronic liver disease, 30 and so on. However, we are not aware of any methods or applications designed for the detection of congenital heart diseases in newborns from ultrasound or from other imaging modality data.…”

Ultrasound intima‐media thickness measurement of the carotid artery using ant colony optimization combined with a curvelet‐based orientation‐selective filter

“…10,11 Different techniques have been proposed to perform automatic IMT measurement, including edge detection, [12][13][14] dynamic programming, 10,15,16 active contour models, [17][18][19][20][21][22][23][24] Hough transform, 20,25 Nakagami modeling, 26 and neural networks. 27 However, previous studies mostly focused on the accuracy of IMT measurement, but gave less consideration to the robustness. Generally, ultrasound images suffer from serious noise and relatively poor anatomical details, which could produce images with low signal to noise ratio.…”