Deep Learning-Based Carotid Plaque Segmentation from B-Mode Ultrasound Images.

Carotid ultrasound measurement of total plaque area (TPA) provides a method for quantifying carotid plaque burden and monitoring changes in carotid atherosclerosis in response to medical treatment. Plaque boundary segmentation is required to generate the TPA measurement; however, training of observers and manual delineation are time consuming. Thus, our objective was to develop an automated plaque segmentation method to generate TPA from longitudinal carotid ultrasound images. In this study, a deep learning-based method, modified U-Net, was used to train the segmentation model and generate TPA measurement. A total of 510 plaques from 144 patients were used in our study, where the Monte Carlo cross-validation was used by randomly splitting the data set into 2/3 and 1/3 for training and testing. Two observers were trained to manually delineate the 510 plaques separately, which were used as the ground-truth references. Two U-Net models (M1 and M2) were trained using the two different ground-truth data sets from the two observers to evaluate the accuracy, variability and sensitivity on the ground-truth data sets used for training our method. The results of the algorithm segmentations of the two models yielded strong agreement with the two manual segmentations with the Pearson correlation coefficient r = 0.989 (p < 0.0001) and r = 0.987 (p < 0.0001). Comparison of the U-Net and manual segmentations resulted in mean TPA differences of 0.05 ± 7.13 mm2 (95% confidence interval: 14.02-13.02 mm2) and 0.8 ± 8.7 mm2 (17.85-16.25 mm2) for the two models, which are small compared with the TPA range in our data set from 4.7 to 312.8 mm2. Furthermore, the mean time to segment a plaque was only 8.3 ± 3.1 ms. The presented deep learning-based method described has sufficient accuracy with a short computation time and exhibits high agreement between the algorithm and manual TPA measurements, suggesting that the method could be used to measure TPA and to monitor the progression and regression of carotid atherosclerosis.

Deep Learning-Based Measurement of Total Plaque Area in B-Mode Ultrasound Images.

Measurement of total-plaque-area (TPA) is important for determining long term risk for stroke and monitoring carotid plaque progression. Since delineation of carotid plaques is required, a deep learning method can provide automatic plaque segmentations and TPA measurements; however, it requires large datasets and manual annotations for training with unknown performance on new datasets. A UNet++ ensemble algorithm was proposed to segment plaques from 2D carotid ultrasound images, trained on three small datasets (n = 33, 33, 34 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The ensemble was also trained on the entire SPARC dataset and tested with a different dataset (n = 497, Zhongnan Hospital, China). Algorithm and manual segmentations were compared using Dice-similarity-coefficient (DSC), and TPAs were compared using the difference ( ∆TPA), Pearson correlation coefficient (r) and Bland-Altman analyses. Segmentation variability was determined using the intra-class correlation coefficient (ICC) and coefficient-of-variation (CoV). For 44 SPARC subjects, algorithm DSC was 83.3-85.7%, and algorithm TPAs were strongly correlated (r = 0.985-0.988; p < 0.001) with manual results with marginal biases (0.73-6.75) mm 2 using the three training datasets. Algorithm ICC for TPAs (ICC = 0.996) was similar to intra- and inter-observer manual results (ICC = 0.977, 0.995). Algorithm CoV = 6.98% for plaque areas was smaller than the inter-observer manual CoV (7.54%). For the Zhongnan dataset, DSC was 88.6% algorithm and manual TPAs were strongly correlated (r = 0.972, p < 0.001) with ∆TPA = -0.44 ±4.05 mm 2 and ICC = 0.985. The proposed algorithm trained on small datasets and segmented a different dataset without retraining with accuracy and precision that may be useful clinically and for research.

The association between daily naps and metabolic syndrome: Evidence from a population-based study in the Middle-East.

BACKGROUND: Daily naps are a common habit in many Middle Eastern and Asian countries; however, little is known about the association between daily naps and other health consequences, including the presence of metabolic syndrome (MetS). METHODS: Participants were recruited from the Mashhad stroke and heart atherosclerotic disorders study. We defined MetS according to International Diabetes Federation criteria. Nighttime sleeping hours were categorized into three categories: <6, 6-8, and >8 hours. Using logistic regression models, we analyzed the association between the duration of night-time sleep and daily naps with MetS and its different components. RESULTS: A total of 9652 individuals were included in the study: 3859 with MetS (40%) and 5793 without MetS (60%), as the control group. Of all, 72% participants had a regular daily nap. Those with daily naps had a higher odd of MetS [Odds ratio:1.19, confidence interval: (1.08-1.33); P < .001]. We also observed significantly higher odds of obesity, central obesity, hypertriglyceridemia, and diabetes or impaired fasting glucose in these subjects. Men sleeping <6 hours per night had a lower odd of MetS. However, we observed higher odds of cardiovascular risk factors in participants sleeping <6 hours, including obesity and diabetes or IFG. CONCLUSION: Napping is a common habit in middle Eastern countries. Although the cross-sectional design of our study cannot prove causality, we observed a significant association between the presence of MetS and daily naps. The public should be aware of this possibility and be educated about the importance of sleeping patterns.