Carotid Vessel Wall Segmentation Through Domain Aligner, Topological Learning, and Segment Anything Model for Sparse Annotation in MR Images

Li, Xibao; Ouyang, Xi; Zhang, Jiadong; Ding, Zhongxiang; Zhang, Yuyao; Xue, Zhong; Shi, Feng; Shen, Dinggang

doi:10.1109/tmi.2024.3424884

IEEE Trans. Med. Imaging

2024

DOI: 10.1109/tmi.2024.3424884

|View full text |Cite

Carotid Vessel Wall Segmentation Through Domain Aligner, Topological Learning, and Segment Anything Model for Sparse Annotation in MR Images

Xibao Li,

Xi Ouyang,

Jiadong Zhang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Preprint1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Long-Term Carotid Plaque Progression and the Role of Intraplaque Hemorrhage: Analysis from Deep Learning-based Longitudinal Vessel Wall Imaging

Guo,

Akcicek,

Hippe

et al. 2024

Preprint

View full text Add to dashboard Cite

Background: Carotid atherosclerosis is a major etiology of stroke. Although intraplaque hemorrhage (IPH) is known to increase stroke risk and plaque burden, its long-term effects on plaque dynamics remain unclear. Objectives: This study aimed to evaluate the long-term impact of IPH on carotid plaque burden progression using deep learning-based segmentation on multi-contrast vessel wall imaging (VWI). Methods: Twenty-eight asymptomatic subjects with carotid atherosclerosis underwent an average of 4.7 ± 0.6 VWI scans over 5.8 ± 1.1 years. Deep learning pipelines segmented the carotid vessel walls and IPH. Bilateral plaque progression was analyzed using generalized estimating equations, and linear mixed-effects models evaluated long-term associations between IPH occurrence, IPH volume (%HV), and plaque burden (%WV) progression. Results: Two subjects with ipsilateral IPH developed new ischemic infarcts during follow-up. IPH was detected in 23/50 of arteries. Of arteries without IPH at baseline, 11/39 developed new IPH that persisted, while 5/11 arteries with baseline IPH exhibited it throughout the study. Bilateral plaque growth was significantly correlated (r = 0.54, p < 0.001), but this symmetry was weakened with IPH presence. The progression rate for arteries without IPH was -0.001 %/year (p = 0.895). However, IPH presence or development at any point was associated with a 2.34% absolute increase in %WV (p < 0.001), and %HV was associated with 0.73% per 2-fold increase over the mean of %HV (p = 0.005). Conclusions: IPH may persist asymptomatically for extended periods. While arteries without IPH demonstrated minimal progression under contemporary treatment, IPH significantly accelerated long-term plaque growth.

show abstract

Long-Term Carotid Plaque Progression and the Role of Intraplaque Hemorrhage: Analysis from Deep Learning-based Longitudinal Vessel Wall Imaging

Guo,

Akcicek,

Hippe

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Carotid Vessel Wall Segmentation Through Domain Aligner, Topological Learning, and Segment Anything Model for Sparse Annotation in MR Images

Cited by 1 publication

References 46 publications

Long-Term Carotid Plaque Progression and the Role of Intraplaque Hemorrhage: Analysis from Deep Learning-based Longitudinal Vessel Wall Imaging

Long-Term Carotid Plaque Progression and the Role of Intraplaque Hemorrhage: Analysis from Deep Learning-based Longitudinal Vessel Wall Imaging

Contact Info

Product

Resources

About