ArticleUltrasonic imaging
Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture.
Article in Ultrasonic imaging. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Self-supervised ultrasound B-mode strain elastography using SMURF.Ultrasonics · 2026Article
- Mask R-CNN-based carotid plaque localization in ultrasound images.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- An open B-mode ultrasound database for deep learning-based atherosclerotic plaque segmentation.Scientific data · 2026Article
- Review
- Microstructure-Informed Hyper-Viscoelastic Model Capturing Soft Tissue Tensile Behavior Across Large Deformations.Journal of the mechanics and physics of solids · 2026Article
- Deep learning-based automatic segmentation of arterial vessel walls and plaques in MR vessel wall images for quantitative assessment.European radiology · 2025Article
- Automated scheme of plaque classification based on segmentation in carotid ultrasound images using transformer approach.Journal of medical ultrasonics (2001) · 2025Article
- Carotid artery segmentation in computed tomography angiography (CTA) using multi-scale deep supervision with Swin-UNet and advanced data augmentation.Quantitative imaging in medicine and surgery · 2025Article
- Combining artificial intelligence assisted image segmentation and ultrasound based radiomics for the prediction of carotid plaque stability.BMC medical imaging · 2025Article
- DualPlaqueNet with dual-branch structure and attention mechanism for carotid plaque semantic segmentation and size prediction.Frontiers in physiology · 2025Article
- CACSNet for automatic robust classification and segmentation of carotid artery calcification on panoramic radiographs using a cascaded deep learning network.Scientific reports · 2024Article
- Lumen segmentation using a Mask R-CNN in carotid arteries with stenotic atherosclerotic plaque.Ultrasonics · 2024Article
- A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study.Frontiers in neurology · 2024Article
- Application and prospects of AI-based radiomics in ultrasound diagnosis.Visual computing for industry, biomedicine, and art · 2023Review
- Cardiovascular disease/stroke risk stratification in deep learning framework: a review.Cardiovascular diagnosis and therapy · 2023Review
- 3D Ultrasound Reconstructions of the Carotid Artery and Thyroid Gland Using Artificial-Intelligence-Based Automatic Segmentation-Qualitative and Quantitative Evaluation of the Segmentation Results via Comparison with CT Angiography.Sensors (Basel, Switzerland) · 2023Article
- Imaging and Hemodynamic Characteristics of Vulnerable Carotid Plaques and Artificial Intelligence Applications in Plaque Classification and Segmentation.Brain sciences · 2023Review
- Stroke risk prediction by color Doppler ultrasound of carotid artery-based deep learning using Inception V3 and VGG-16.Frontiers in neurology · 2023Article
- Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm.Journal of cardiovascular development and disease · 2022Article
- The application of the nnU-Net-based automatic segmentation model in assisting carotid artery stenosis and carotid atherosclerotic plaque evaluation.Frontiers in physiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Carotid plaque segmentation in ultrasound longitudinal B-mode images using deep learning is presented in this work. We report on 101 severely stenotic carotid plaque patients. A standard U-Net is compared with a dilated U-Net architecture in which the dilated convolution layers were used in the bottleneck. Both a fully automatic and a semi-automatic approach with a bounding box was implemented. The performance degradation in plaque segmentation due to errors in the bounding box is quantified. We found that the bounding box significantly improved the performance of the networks with U-Net Dice coefficients of 0.48 for automatic and 0.83 for semi-automatic segmentation of plaque. Similar results were also obtained for the dilated U-Net with Dice coefficients of 0.55 for automatic and 0.84 for semi-automatic when compared to manual segmentations of the same plaque by an experienced sonographer. A 5% error in the bounding box in both dimensions reduced the Dice coefficient to 0.79 and 0.80 for U-Net and dilated U-Net respectively.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.