Extraction of vascular wall in carotid ultrasound via a novel boundary-delineation network

Huang, Qinghua; Jia, Lizhi; Ren, Guanqing; Wang, Xiaoyi; Liu, Chunying

doi:10.1016/j.engappai.2023.106069

Cited by 25 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve problems such as category imbalance and boundary ambiguity, some researchers have developed corresponding modules. Considering the inaccurate location of vascular wall boundaries and segmentation errors occurring in discontinuous vascular walls and dark boundaries, Huang et al [17] proposed a new boundary delineation network (BDNet). First, the rough prediction is made based on the feature downsampling process and multi-scale feature fusion module, and then the PointHead boundary refinement module is used to realize the accurate location of the boundary points.…”

Section: Segmentation Of the Imc In Longitudinal Ultrasoundmentioning

confidence: 99%

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

Cui¹,

Li²,

Lin³

2023

Advanced Ultrasound in Diagnosis and Therapy

View full text Add to dashboard Cite

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.

show abstract

Section: Segmentation Of the Imc In Longitudinal Ultrasoundmentioning

confidence: 99%

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

Cui¹,

Li²,

Lin³

2023

Advanced Ultrasound in Diagnosis and Therapy

View full text Add to dashboard Cite

show abstract

“…A multilevel attention module is added to the U‐Net for retinal vessel segmentation; this module utilizes attention mechanisms at multiple levels to improve segmentation performance [10], and the experimental results on a public dataset demonstrate the effectiveness of the proposed method compared with existing methods. Huang et al [11] identified the issue of imprecise localization of blood vessel wall boundaries and introduced a novel boundary depiction network to represent the connection between pixels based on global and neighborhood characteristics using the structural features of blood vessel walls to assist the model in extracting crucial structured data.…”

Section: Introductionmentioning

confidence: 99%

Retinal Blood Vessels Segmentation With Improved SE‐UNet Model

Wan,

Wei,

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Accurate segmentation of retinal vessels is crucial for the early diagnosis and treatment of eye diseases, for example, diabetic retinopathy, glaucoma, and macular degeneration. Due to the intricate structure of retinal vessels, it is essential to extract their features with precision for the semantic segmentation of medical images. In this study, an improved deep learning neural network was developed with a focus on feature extraction based on the U‐Net structure. The enhanced U‐Net combines the architecture of convolutional neural networks (CNNs) with SE blocks (squeeze‐and‐excitation blocks) to adaptively extract image features after each U‐Net encoder's convolution. This approach aids in suppressing nonvascular regions and highlighting features for specific segmentation tasks. The proposed method was trained and tested on the DRIVECHASE_DB1 and STARE datasets. As a result, the proposed model had an algorithmic accuracy, sensitivity, specificity, Dice coefficient (Dc), and Matthews correlation coefficient (MCC) of 95.62/0.9853/0.9652, 0.7751/0.7976/0.7773, 0.9832/0.8567/0.9865, 82.53/87.23/83.42, and 0.7823/0.7987/0.8345, respectively, outperforming previous methods, including UNet++, attention U‐Net, and ResUNet. The experimental results demonstrated that the proposed method improved the retinal vessel segmentation performance.

show abstract

An effective fuzzy based segmentation and twin attention based convolutional gated recurrent network for skin cancer detection

Rai,

Agarwal,

Gupta

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

Extraction of vascular wall in carotid ultrasound via a novel boundary-delineation network

Cited by 25 publications

References 31 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

Retinal Blood Vessels Segmentation With Improved SE‐UNet Model

An effective fuzzy based segmentation and twin attention based convolutional gated recurrent network for skin cancer detection

Contact Info

Product

Resources

About