Frequency-domain active contours solution to evaluate intima–media thickness of the common carotid artery

Bastida-Jumilla, María-Consuelo; Menchón-Lara, Rosa-María; Morales‐Sánchez, Juan; Verdú‐Monedero, Rafael; Larrey-Ruiz, Jorge; Sancho‐Gómez, José‐Luis

doi:10.1016/j.bspc.2014.08.012

Cited by 26 publications

(19 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A combination of dynamic programming and properties of edge-based approach defines arterial layer followed by IMT and LD [31,32]. Active contours and its variation based methods have better performance [22,28]. There are techniques based on statistical modelling [11,14], Haugh transforms [15,22] and Nakagami distribution [33].…”

Section: Discussionmentioning

confidence: 99%

“…Active contours and its variation based methods have better performance [22,28]. There are techniques based on statistical modelling [11,14], Haugh transforms [15,22] and Nakagami distribution [33]. Recently, most of the techniques are based on deep learning methods [18,19,29,34].…”

Section: Discussionmentioning

confidence: 99%

“…Recently, most of the techniques are based on deep learning methods [18,19,29,34]. Menchen-Lara et al has used ELM-AE with SLFN and estimated IMT, but the performance of the system is still below the active contour-based method proposed by the same group [22]. A detailed analysis of the techniques is covered in the review article by Loizou [35] and Kumar et al [30].…”

Section: Discussionmentioning

confidence: 99%

“…The proposed, overlapping block-based system outperforms in ROI detection to [18,19]. However, IMT estimation underperforms with some existing method [18,21,22]. We have measured LD of the CCA with MAD metrics [20].…”

Section: Imt and Ld Measurementmentioning

confidence: 99%

See 3 more Smart Citations

Arterial Parameters and Elasticity Estimation in Common Carotid Artery Using Deep Learning Approach

Patel¹,

Jain²

2019

IJIGSP

View full text Add to dashboard Cite

The risk of cardiovascular diseases is growing worldwide, and its early detection is necessary to reduce the level of risk. Structural parameters of the carotid artery as intima-media thickness and functional parameters such as arterial elasticity are directly associated with cardiovascular diseases. Segmentation of the carotid artery is required to measure the structural parameters and its temporal value that is used to estimate the arterial elasticity. This paper has two primary objectives: (i) Segmentation of the sequence of carotid artery ultrasound to measure temporal value of intimamedia thickness and lumen-diameter, and (ii) Young's modulus of elasticity estimation. The proposed segmentation method uses the contextual feature of the image pattern and is based on multi-layer extreme learning machine auto-encoder network. This segmentation method has two parts: (a) region of interest localization and (b) lumen-intima interface and mediaadventitia interface detection at the far wall. ROI localization algorithm divides the ultrasound frame into columns and also divides each column into overlapping blocks, ensuring that every column has a region of interest block. A multi-layer extreme learning machine with auto-encoder is trained with labelled data and in testing; system classifies the blocks into 'region of interest' and 'non-region of interest'. Pixels belonging to the region of interest are classified in the first part and a similar network-based method is proposed for lumenintima and media-adventitia interface detection at the near wall of the carotid artery. Structural parameter of the artery, intima-media thickness and lumen diameter are measured in a sequence of images of the cardiac cycle. The temporal values of structural parameters are used to estimate the young's modulus of elasticity. .

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Imt and Ld Measurementmentioning

confidence: 99%

See 2 more Smart Citations

Arterial Parameters and Elasticity Estimation in Common Carotid Artery Using Deep Learning Approach

Patel¹,

Jain²

2019

IJIGSP

View full text Add to dashboard Cite

show abstract

“…Year Thickness error (µm) Current work -2.8 ± 2.1 Bastida-Jumillacite [1] 2015 13.8±31.9 Ilea [7] 2013 80 ± 40 Loizou [14] 2013 30 ± 30 Molinari [20] 2011 43 ± 93 tween efficiency and accuracy for CIMT video analysis. We should note that throughout this paper, all performance evaluations were performed without involving any user interactions.…”

Section: Authormentioning

confidence: 99%

Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks

Shin

Tajbakhsh

Hurst

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations.

show abstract

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

Vila

Remeseiro

Grau

et al. 2021

Handbook of Artificial Intelligence in Healthcare

View full text Add to dashboard Cite

Frequency-domain active contours solution to evaluate intima–media thickness of the common carotid artery

Cited by 26 publications

References 40 publications

Arterial Parameters and Elasticity Estimation in Common Carotid Artery Using Deep Learning Approach

Arterial Parameters and Elasticity Estimation in Common Carotid Artery Using Deep Learning Approach

Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks

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

Contact Info

Product

Resources

About