Semantic segmentation with DenseNets for carotid artery ultrasound plaque segmentation and CIMT estimation

Vila, Maria del Mar; Remeseiro, Beatriz; Grau, María; Elosúa, Roberto; Betriu, Àngels; Fernández-Giráldez, Elvira; Igual, Laura

doi:10.1016/j.artmed.2019.101784

Cited by 32 publications

(19 citation statements)

References 19 publications

(36 reference statements)

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“…A fully automatic deep-learning method able to properly localize the intima media region and then estimate the IMT was used (Supplementary Table 2). This machinelearning procedure is based on convolutional neural networks and was validated using the IMT estimates performed in AVICA as the gold-standard [15]. Left and right common carotid IMT were obtained for each participant and the mean considered in the analysis.…”

Section: Common Carotid Intima Media Thicknessmentioning

confidence: 99%

“…First, the added value of common carotid IMT for cardiovascular risk prediction beyond classical risk factors remains controversial [36,37]. In addition, the reproducibility of IMT measures is a controversial issue [38] that we have minimized with a previously validated machine-learning procedure [39]. On the other hand, the use of carotid ultrasound revealed the arterial stiffness of the carotid wall but did not allow measurement of carotid-femoral pulse wave velocity, the gold standard for assessing this variable.…”

Section: Limitationsmentioning

confidence: 99%

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Do individuals with autoimmune disease have increased risk of subclinical carotid atherosclerosis and stiffness?

et al. 2021

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To explore the role of chronic inflammation inherent to autoimmune diseases in the development of subclinical atherosclerosis and arterial stiffness, this study recruited two population-based samples of individuals with and without autoimmune disease (ratio 1:5) matched by age, sex, and education level and with a longstanding (≥6 years) diagnosis of autoimmune disease. Common carotid intima media thickness (IMT) and arterial distensibility and compliance were assessed with carotid ultrasound.Multivariable linear and logistic regression models were adjusted for 10-year cardiovascular risk. In total, 546 individuals with and without autoimmune diseases (91 and 455, respectively) were included. Mean age was 66 years (standard deviation 12), and 240 (43.9%) were women. Arterial stiffness did not differ according to presence of autoimmune diseases. In men, the diagnosis of autoimmune diseases significantly increased common carotid IMT [beta-coefficient (95% confidence interval): 0.058 (0.009; 0.108); p-value=0.022] and the percentage having IMT ≥ percentile 75 [1.012 (0.145; 1.880); p-value=0.022]. Women without autoimmune disease were more likely to have IMT ≥ percentile 75 [-2.181 (-4.214; -0.149); p-value=0.035] but analysis of IMT as a continuous variable did not yield significant results. In conclusion, subclinical carotid atherosclerosis, but not arterial stiffness, was higher in men with autoimmune diseases. Women did not show significant differences in any of these carotid features.Sex was an effect modifier in the association between common carotid IMT values and the diagnosis of autoimmune diseases.

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Section: Common Carotid Intima Media Thicknessmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Do individuals with autoimmune disease have increased risk of subclinical carotid atherosclerosis and stiffness?

et al. 2021

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“…Second, codec introduces dense block. Vila et al [ 100 ] performed semantic segmentation of carotid ultrasound plaques based on DenseNet, dense connectivity captured multiscale contextual information, and correlation coefficient of carotid intima-media thickness reached 0.81 in the experiment; robustness and generalization ability were validated on two datasets. Thirdly, 3D dense block was applied to segmentation networks.…”

Section: Application Of Densenet In Medical Image Analysismentioning

confidence: 99%

Dense Convolutional Network and Its Application in Medical Image Analysis

Tao

XinYu

et al. 2022

BioMed Research International

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Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet.

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“…Nowadays, convolutional neural network (CNN) based deep learning methods [ 17 , 18 , 19 ] are becoming increasingly more advantageous for semantic segmentation tasks; however, these methods need numerous labeled images for training. Specially, CNN-based plaque or IMC segmentation methods [ 20 , 21 , 22 ] obtain satisfactory results, but these methods need manual ROI preprocessing, and they require more ultrasound images for training. Moreover, our plaque segmentation task has no anatomy prior compared to IMC segmentation [ 23 , 24 , 25 ], which increases the difficulty for plaque segmentation.…”

Section: Introductionmentioning

confidence: 99%

HRU-Net: A Transfer Learning Method for Carotid Artery Plaque Segmentation in Ultrasound Images

Yuan

Zhang

et al. 2022

Diagnostics

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Carotid artery stenotic plaque segmentation in ultrasound images is a crucial means for the analysis of plaque components and vulnerability. However, segmentation of severe stenotic plaques remains a challenging task because of the heterogeneities of inter-plaques and intra-plaques, and obscure boundaries of plaques. In this paper, we propose an automated HRU-Net transfer learning method for segmenting carotid plaques, using the limited images. The HRU-Net is based on the U-Net encoder–decoder paradigm, and cross-domain knowledge is transferred for plaque segmentation by fine-tuning the pretrained ResNet-50. Moreover, a cropped-blood-vessel image augmentation is customized for the plaque position constraint during training only. Moreover, hybrid atrous convolutions (HACs) are designed to derive diverse long-range dependences for refined plaque segmentation that are used on high-level semantic layers to exploit the implicit discrimination features. The experiments are performed on 115 images; Firstly, the 10-fold cross-validation, using 40 images with severe stenosis plaques, shows that the proposed method outperforms some of the state-of-the-art CNN-based methods on Dice, IoU, Acc, and modified Hausdorff distance (MHD) metrics; the improvements on metrics of Dice and MHD are statistically significant (p < 0.05). Furthermore, our HRU-Net transfer learning method shows fine generalization performance on 75 new images with varying degrees of plaque stenosis, and it may be used as an alternative for automatic noisy plaque segmentation in carotid ultrasound images clinically.

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Semantic segmentation with DenseNets for carotid artery ultrasound plaque segmentation and CIMT estimation

Cited by 32 publications

References 19 publications

Do individuals with autoimmune disease have increased risk of subclinical carotid atherosclerosis and stiffness?

Do individuals with autoimmune disease have increased risk of subclinical carotid atherosclerosis and stiffness?

Dense Convolutional Network and Its Application in Medical Image Analysis

HRU-Net: A Transfer Learning Method for Carotid Artery Plaque Segmentation in Ultrasound Images

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