Learning-based initialization for correntropy-based level sets to segment atherosclerotic plaque in ultrasound images

Qian, Chunjun; Su, Enjie; Ni, Xinye

doi:10.1016/j.ultras.2022.106826

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…Qian et al [27] compared the effect of combining different classic classification models and automatic context models and found that random forest combined with automatic context can achieve the best results. Qian et al [28] also proposed a plaque segmentation method based on machine learning initialization combined with correntropy level set. The introduction of the correntropy method instead of the traditional mean square error can adaptively reduce the mutation rate where the noise occurs.…”

Section: Segmentation Of the Plaque In Longitudinal Ultrasoundmentioning

confidence: 99%

Advances in Intelligent Segmentation and 3D/4D Reconstruction of Carotid Ultrasound Imaging

Cui¹,

Li²,

Lin³

2023

Advanced Ultrasound in Diagnosis and Therapy

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Cardiovascular disease (CVD) is one of the ten leading causes of death worldwide. Atherosclerotic disease, which can lead to myocardial infarction and stroke, is the main cause of CVD. The two main ultrasound image phenotypes used to monitor atherosclerotic load are carotid intima-media thickness (IMT) and plaque area (PA). Early segmentation and measurement methods were based on manual or threshold segmentation, snake models, etc. Usually, these methods are semi-automatic and have poor repeatability and accuracy. Segmentation of the carotid intima-media complex (IMC) and plaque in ultrasound based on artificial intelligence can achieve good accuracy. Compared with two-dimensional ultrasound, three-dimensional/fourdimensional ultrasound can provide spatial dynamic vascular information, which is helpful for doctors to evaluate. This study reviews the progress of artificial intelligence (AI) segmentation methods based on machine learning (ML) and deep learning (DL) used in the segmentation of the IMC and plaque as well as the 3D / 4D reconstruction of carotid ultrasound.

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Section: Segmentation Of the Plaque In Longitudinal Ultrasoundmentioning

confidence: 99%