ArticleUltrasonics2024
Lumen segmentation using a Mask R-CNN in carotid arteries with stenotic atherosclerotic plaque.
Article in Ultrasonics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- 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
- A hybrid pipeline for carotid artery segmentation using YOLOv11n and contour models.Scientific reports · 2026Article
- MHAHF-UNet: a multi-scale hybrid attention hierarchy fusion network for carotid artery segmentation.International journal of computer assisted radiology and surgery · 2025Article
- Enhanced stratification of male pattern hair loss using AI through novel loss region ratio analysis.Scientific reports · 2025Article
- Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
In patients at high risk for ischemic stroke, clinical carotid ultrasound is often used to grade stenosis, determine plaque burden and assess stroke risk. Analysis currently requires a trained sonographer to manually identify vessel and plaque regions, which is time and labor intensive. We present a method for automatically determining bounding boxes and lumen segmentation using a Mask R-CNN network trained on sonographer assisted ground-truth carotid lumen segmentations. Automatic lumen segmentation also lays the groundwork for developing methods for accurate plaque segmentation, and wall thickness measurements in cases with no plaque. Different training schemes are used to identify the Mask R-CNN model with the highest accuracy. Utilizing a single-channel B-mode training input, our model produces a mean bounding box intersection over union (IoU) of 0.81 and a mean lumen segmentation IoU of 0.75. However, we encountered errors in prediction when the jugular vein is the most prominently visualized vessel in the B-mode image. This was due to the fact that our dataset has limited instances of B-mode images with both the jugular vein and carotid artery where the vein is dominantly visualized. Additional training datasets are anticipated to mitigate this issue.
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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.