Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Al-Mohannadi, Aisha; Al-Maadeed, Somaya; Elharrouss, Omar; Sadasivuni, Kishor Kumar

doi:10.3390/s21206839

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…Table 7 presents the benchmarking table comprising fourteen review studies ( 9 , 11 , 13 , 80 , 100 - 118 ) for CVD risk stratification using AI (ML or DL). The table has eleven attributes along column 2 to column 12 for every corresponding study from row 2 to row 15.…”

Section: Critical Discussionmentioning

confidence: 99%

Cardiovascular disease/stroke risk stratification in deep learning framework: a review

Bhagawati,

Paul,

Agarwal

et al. 2023

Cardiovasc Diagn Ther

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The global mortality rate is known to be the highest due to cardiovascular disease (CVD). Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as healthcare cost is increasing day by day. Conventional methods for risk prediction of CVD lack robustness due to the non-linear relationship between risk factors and cardiovascular events in multi-ethnic cohorts. Few recently proposed machine learning-based risk stratification reviews without deep learning (DL) integration. The proposed study focuses on CVD risk stratification by the use of techniques mainly solo deep learning (SDL) and hybrid deep learning (HDL). Using a PRISMA model, 286 DL-based CVD studies were selected and analyzed. The databases included were Science Direct, IEEE Xplore, PubMed, and Google Scholar. This review is focused on different SDL and HDL architectures, their characteristics, applications, scientific and clinical validation, along with plaque tissue characterization for CVD/stroke risk stratification. Since signal processing methods are also crucial, the study further briefly presented Electrocardiogram (ECG)-based solutions. Finally, the study presented the risk due to bias in AI systems. The risk of bias tools used were (I) ranking method (RBS), (II) region-based map (RBM), (III) radial bias area (RBA), (IV) prediction model risk of bias assessment tool (PROBAST), and (V) risk of bias in non-randomized studies-of interventions (ROBINS-I). The surrogate carotid ultrasound image was mostly used in the UNet-based DL framework for arterial wall segmentation. Ground truth (GT) selection is vital for reducing the risk of bias (RoB) for CVD risk stratification. It was observed that the convolutional neural network (CNN) algorithms were widely used since the feature extraction process was automated. The ensemble-based DL techniques for risk stratification in CVD are likely to supersede the SDL and HDL paradigms. Due to the reliability, high accuracy, and faster execution on dedicated hardware, these DL methods for CVD risk assessment are powerful and promising. The risk of bias in DL methods can be best reduced by considering multicentre data collection and clinical evaluation.

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Section: Critical Discussionmentioning

confidence: 99%

Cardiovascular disease/stroke risk stratification in deep learning framework: a review

Bhagawati,

Paul,

Agarwal

et al. 2023

Cardiovasc Diagn Ther

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“…Also, the method used should be able to perform the same on fundus images collected under different settings. Over the years, researchers have used several traditional and artificial intelligence-based techniques to achieve this [6][7][8]. Artificial intelligence practices like machine learning and deep learning are the most highly preferred techniques for this task.…”

Section: Related Workmentioning

confidence: 99%

“…Sensitivity: This metric measures the rate of actual pixels generated as retinal blood vessels among all generated pixels that are actually retinal blood vessel pixels. Sensitivity = TP/(TP + FN) (6) Dice Coefficient(Sorenson Index/FMeasure): An important metric used in image segmentation, representing a special overlap index. Dice = 2 * TP/(2 * TP + FP + FN) (7) Specificity: This metric measures the rate of actual pixels generated as background pixels among all generated pixels that are actually background pixels.…”

Section: Evaluation Metricsmentioning

confidence: 99%

GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation

Sebastian,

Elharrouss,

Al-Maadeed

et al. 2023

Bioengineering

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Most diabetes patients develop a condition known as diabetic retinopathy after having diabetes for a prolonged period. Due to this ailment, damaged blood vessels may occur behind the retina, which can even progress to a stage of losing vision. Hence, doctors advise diabetes patients to screen their retinas regularly. Examining the fundus for this requires a long time and there are few ophthalmologists available to check the ever-increasing number of diabetes patients. To address this issue, several computer-aided automated systems are being developed with the help of many techniques like deep learning. Extracting the retinal vasculature is a significant step that aids in developing such systems. This paper presents a GAN-based model to perform retinal vasculature segmentation. The model achieves good results on the ARIA, DRIVE, and HRF datasets.

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“…Al-Mohannadi et al [ 13 ] proposed a deep-learning-based approach to apply semantic segmentation for the intima-media complex (IMC) and to calculate the cIMT measurement. Alshboul and Fraiwan [ 14 ] developed an algorithm to count the number of chews in eating video recordings using discrete wavelet decomposition and low pass filtration.…”

Section: Overview Of Contributionmentioning

confidence: 99%