Automatic segmentation of the carotid artery and internal jugular vein from 2D ultrasound images for 3D vascular reconstruction

Groves, Leah; VanBerlo, Blake; Veinberg, Natan; Alboog, Abdulrahman; Peters, Terry M.; Chen, Elvis C. S.

doi:10.1007/s11548-020-02248-2

Cited by 28 publications

(15 citation statements)

References 26 publications

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“…The contributions of ML in the context of carotid artery disease can be assigned to four broader categories. First, carotid artery segmentation [144][145][146][147], which is the basis for many secondary analyses, provides potential for more comprehensive analyses of vessel anatomy and pathology. For example, Tsakanikas et al proposed a U-net model to produce a 3D meshed model of the carotid bifurcation and branches using multispectral MR image series and reported an accuracy of 99.1% for lumen area [144].…”

Section: Advances In Artificial Intelligencementioning

confidence: 99%

State-of-the-art CT and MR imaging and assessment of atherosclerotic carotid artery disease: standardization of scanning protocols and measurements—a consensus document by the European Society of Cardiovascular Radiology (ESCR)

et al. 2022

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The European Society of Cardiovascular Radiology (ESCR) is the European specialist society of cardiac and vascular imaging. This society’s highest priority is the continuous improvement, development, and standardization of education, training, and best medical practice, based on experience and evidence. The present intra-society consensus is based on the existing scientific evidence and on the individual experience of the members of the ESCR writing group on carotid diseases, the members of the ESCR guidelines committee, and the members of the executive committee of the ESCR. The recommendations published herein reflect the evidence-based society opinion of ESCR. We have produced a twin-papers consensus, indicated through the documents as respectively “Part I” and “Part II.” The first document (Part I) begins with a discussion of features, role, indications, and evidence for CT and MR imaging-based diagnosis of carotid artery disease for risk stratification and prediction of stroke (Section I). It then provides an extensive overview and insight into imaging-derived biomarkers and their potential use in risk stratification (Section II). Finally, detailed recommendations about optimized imaging technique and imaging strategies are summarized (Section III). The second part of this consensus paper (Part II) is focused on structured reporting of carotid imaging studies with CT/MR. Key Points • CT and MR imaging-based evaluation of carotid artery disease provides essential information for risk stratification and prediction of stroke. • Imaging-derived biomarkers and their potential use in risk stratification are evolving; their correct interpretation and use in clinical practice must be well-understood. • A correct imaging strategy and scan protocol will produce the best possible results for disease evaluation.

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Section: Advances In Artificial Intelligencementioning

confidence: 99%

State-of-the-art CT and MR imaging and assessment of atherosclerotic carotid artery disease: standardization of scanning protocols and measurements—a consensus document by the European Society of Cardiovascular Radiology (ESCR)

et al. 2022

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“…In future work we aim to improve this vascular reconstruction pipeline by replacing the image proces-singÀbased vessel segmentation algorithm with a deep learningÀbased segmentation technique trained on animal images acquired using the forward-looking Foresight ICE probe. The use of machine learning for vascular segmentation and reconstruction has been previously performed using both surface US scans (Yang et al 2013;Groves et al 2020) and intravascular US (Yang et al 2018). The integration of a machine lear-ningÀbased segmentation will allow for accurate patient-specific reconstructions to be obtained that account for differences in patient pathology.…”

Section: Discussionmentioning

confidence: 99%

Toward Fluoro-Free Interventions: Using Radial Intracardiac Ultrasound for Vascular Navigation

Nisar

Groves

Cardarelli-Leite

et al. 2022

Ultrasound in Medicine & Biology

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“…Meanwhile, LSTM is useful when the input data is present within the time sequence (165). Finally, RCNN and Mask R-CNN are used for segmentation (166).…”

Section: Deep Learning Strategies Using Mri Ct and The Usmentioning

confidence: 99%

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Saba¹,

Skandha²,

Gupta³

et al. 2021

Ann Transl Med

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Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type

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Automatic segmentation of the carotid artery and internal jugular vein from 2D ultrasound images for 3D vascular reconstruction

Cited by 28 publications

References 26 publications

State-of-the-art CT and MR imaging and assessment of atherosclerotic carotid artery disease: standardization of scanning protocols and measurements—a consensus document by the European Society of Cardiovascular Radiology (ESCR)

State-of-the-art CT and MR imaging and assessment of atherosclerotic carotid artery disease: standardization of scanning protocols and measurements—a consensus document by the European Society of Cardiovascular Radiology (ESCR)

Toward Fluoro-Free Interventions: Using Radial Intracardiac Ultrasound for Vascular Navigation

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

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