Last Advances on Automatic Carotid Artery Analysis in Ultrasound Images: Towards Deep Learning

Vila, Maria del Mar; Remeseiro, Beatriz; Grau, María; Elosúa, Roberto; Igual, Laura

doi:10.1007/978-3-030-79161-2_9

Cited by 4 publications

(6 citation statements)

References 62 publications

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“…A therosclerosis is the main pathogenic process leading to most cardiovascular diseases [1]. In the treatment of atherosclerosis, IMC is the main reference factor.…”

Section: -2508/ C Audt 2023•http://wwwaudtorg This Is An Open-access ...mentioning

confidence: 99%

“…In a healthy carotid artery, the IMT is approximately within 0.8 mm, and when this value is exceeded, there is a chance of plaque deposition [2]. Clinically, there are two criteria for the diagnosis of plaque [1]. One is that the inner diameter of any intravascular irregular plaque that is continuous with the vascular wall and extends into the lumen is greater than 1.5 mm; the other is that the IMT is greater than 50% of the adjacent IMT.…”

Section: -2508/ C Audt 2023•http://wwwaudtorg This Is An Open-access ...mentioning

confidence: 99%

“…(2) The boundary contrast of the IMC is low, and the structure is narrow, showing dark boundaries or discontinuity in the image; (3) With irregular shape and fuzzy boundary, the surface structure of plaques is complex, and different plaques may have great differences in ultrasonic images due to their different shapes and compositions; (4) Segmentation results of the IMC and plaques by different doctors may be different [1]. Considering the above problems, artificial plaque segmentation is complex and highly subjective, which depends on personal experience accumulation and has strict requirements for doctors.…”

Section: Segmentation Of the Imc In Longitudinal Ultrasoundmentioning

confidence: 99%

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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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“…A therosclerosis is the main pathogenic process leading to most cardiovascular diseases [1]. In the treatment of atherosclerosis, IMC is the main reference factor.…”

Section: -2508/ C Audt 2023•http://wwwaudtorg This Is An Open-access ...mentioning

confidence: 99%

Section: -2508/ C Audt 2023•http://wwwaudtorg This Is An Open-access ...mentioning

confidence: 99%

Section: Segmentation Of the Imc In Longitudinal Ultrasoundmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…The ability of ML to adjust the non-linearity between a set of risk factors (or covariates) and the gold standard is the second major benefit of ML. Such evidence in the context of CVD risk assessment has recently been introduced [28,[158][159][160][161]. These risk factors include (i) conventional office-based, (ii) laboratory-based covariates, and (iii) current drug consumption, while the gold standard criteria are heart failure or stroke.…”

Section: Artificial Intelligence-based System For Cvd/stroke Risk Ass...mentioning

confidence: 99%

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction—A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

Khanna¹,

Maindarkar

Saxena³

et al. 2022

Diagnostics

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Purpose: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. Methods: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. Summary: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

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“…Deep learning (DL) has swiftly emerged as an important subclass of machine learning (ML) offering break-neck speeds that are important for a variety of applications in diverse domains such as pattern recognition, image classification, computer vision, etc. [1]. In order to reap the benefits of the deep learning models, tremendous costs need to be incurred though, owing to the requirements of such models to perform several complex and memory-access intensive operations [2].…”

Section: Introductionmentioning

confidence: 99%

Power-Intent Systolic Array Using Modified Parallel Multiplier for Machine Learning Acceleration

Inayat

Muslim

Iqbal

et al. 2023

Sensors

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Systolic arrays are an integral part of many modern machine learning (ML) accelerators due to their efficiency in performing matrix multiplication that is a key primitive in modern ML models. Current state-of-the-art in systolic array-based accelerators mainly target area and delay optimizations with power optimization being considered as a secondary target. Very few accelerator designs directly target power optimizations and that too using very complex algorithmic modifications that in turn result in a compromise in the area or delay performance. We present a novel Power-Intent Systolic Array (PI-SA) that is based on the fine-grained power gating of the multiplication and accumulation (MAC) block multiplier inside the processing element of the systolic array, which reduces the design power consumption quite significantly, but with an additional delay cost. To offset the delay cost, we introduce a modified decomposition multiplier to obtain smaller reduction tree and to further improve area and delay, we also replace the carry propagation adder with a carry save adder inside each sub-multiplier. Comparison of the proposed design with the baseline Gemmini naive systolic array design and its variant, i.e., a conventional systolic array design, exhibits a delay reduction of up to 6%, an area improvement of up to 32% and a power reduction of up to 57% for varying accumulator bit-widths.

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Last Advances on Automatic Carotid Artery Analysis in Ultrasound Images: Towards Deep Learning

Cited by 4 publications

References 62 publications

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

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

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction—A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

Power-Intent Systolic Array Using Modified Parallel Multiplier for Machine Learning Acceleration

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