Artificial intelligence in symptomatic carotid plaque detection: a narrative review
G Miceli, G Rizzo, MG Basso, E Cocciola… - Applied Sciences, 2023 - mdpi.com
Identifying atherosclerotic disease is the mainstay for the correct diagnosis of the large artery
atherosclerosis ischemic stroke subtype and for choosing the right therapeutic strategy in …
atherosclerosis ischemic stroke subtype and for choosing the right therapeutic strategy in …
Carotid vessel wall segmentation through domain aligner, topological learning, and segment anything model for sparse annotation in mr images
Medical image analysis poses significant challenges due to limited availability of clinical
data, which is crucial for training accurate models. This limitation is further compounded by …
data, which is crucial for training accurate models. This limitation is further compounded by …
[HTML][HTML] Nested star-shaped objects segmentation using diameter annotations
Most current deep learning based approaches for image segmentation require annotations
of large datasets, which limits their application in clinical practice. We observe a mismatch …
of large datasets, which limits their application in clinical practice. We observe a mismatch …
Going off-grid: continuous implicit neural representations for 3D vascular modeling
Personalised 3D vascular models are valuable for diagnosis, prognosis and treatment
planning in patients with cardiovascular disease. Traditionally, such models have been …
planning in patients with cardiovascular disease. Traditionally, such models have been …
SIRE: scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks
Blood vessel orientation as visualized in 3D medical images is an important descriptor of its
geometry that can be used for centerline extraction and subsequent segmentation and …
geometry that can be used for centerline extraction and subsequent segmentation and …
Automatic Coronary Artery Plaque Quantification and CAD-RADS Prediction using Mesh Priors
Coronary artery disease (CAD) remains the leading cause of death worldwide. Patients with
suspected CAD undergo coronary CT angiography (CCTA) to evaluate the risk of …
suspected CAD undergo coronary CT angiography (CCTA) to evaluate the risk of …
VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis
We present a data-driven generative framework for synthesizing blood vessel 3D geometry.
This is a challenging task due to the complexity of vascular systems, which are highly …
This is a challenging task due to the complexity of vascular systems, which are highly …
UR-CarA-Net: a cascaded framework with uncertainty regularization for automated segmentation of carotid arteries on black blood MR images
We present a fully automated method for carotid artery (CA) outer wall segmentation in black
blood MRI using partially annotated data and compare it to the state-of-the-art reference …
blood MRI using partially annotated data and compare it to the state-of-the-art reference …
Physics-informed graph neural networks for flow field estimation in carotid arteries
Hemodynamic quantities are valuable biomedical risk factors for cardiovascular pathology
such as atherosclerosis. Non-invasive, in-vivo measurement of these quantities can only be …
such as atherosclerosis. Non-invasive, in-vivo measurement of these quantities can only be …
Uncertainty-based quality assurance of carotid artery wall segmentation in black-blood MRI
The application of deep learning models to large-scale data sets requires means for
automatic quality assurance. We have previously developed a fully automatic algorithm for …
automatic quality assurance. We have previously developed a fully automatic algorithm for …