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Abstract: Cardiovascular disease (CVD)
is one of the leading causes of death in urban areas.
Carotid artery segmentation is the initial step
in the automated diagnosis of carotid artery disease.
The segmentation of carotid wall and lumen region
boundaries are used as an essential part
in assessing plaque morphology. In this paper,
two types of Convolutional Neural Network (CNN)
architectures are used for segmentation: U-Net
and SegNet. The models used in this paper are applied
on 257 ultrasound images containing a transverse
section of the vessel acquired by ultrasound.
Ultrasound imaging is noninvasive, completely
unharming for the patient and a low-cost imaging
method, but the main challenge when working
with this kind of images is a very low signal
to noise ratio and the process of imaging is highly
dependent on the device operator. Different
models are tested for various ranges of hyperparameter
values and compared using different metrics.
The model presented in this paper achieved over 94%
Dice Coefficient for wall and lumen segmentation
when trained during 100 epochs.
Keywords: carotid artery, convolutional neural
network, SegNet, segmentation, U-Net
Published in: IPSI Bgd TIR (Volume: 18)
Number: 1
ISSN: 1820 - 4503
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