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Bioengineering
Special Issues
Development of Ultrasound Techniques for Cardiovascular Disease Assessment and Monitoring
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Development of Ultrasound Techniques for Cardiovascular Disease Assessment and Monitoring

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 1543

Special Issue Editors

Dr. Bernard Chiu

E-Mail Website
Guest Editor
Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
Interests: ultrasound Imaging; image processing; 3D imaging; segmentation; biomarkers
Dr. Ran Zhou

E-Mail Website
Guest Editor
School of Computer Science, Hubei University of Technology, Wuhan, China
Interests: ultrasound image segmentation; deep learning

Special Issue Information

Dear Colleagues,

Atherosclerosis is the underlying mechanism contributing to vascular diseases. The recent developments of ultrasound imaging techniques has allowed for a direct visualization and quantification of the vessel wall and plaque. Vascular measurements include the intima-media thickness, vessel wall and plaque thickness, and volume and textural features. As atherosclerosis is a focal disease which predominantly occurs at bends and bifurcations, biomarkers based on the distribution of the vessel wall and plaque thickness and volume measured from 3D ultrasound images have been developed for a sensitive detection of the effects of new dietary and medical therapies.

Machine learning plays an important role in making measurements more efficient and reproducible. Techniques have been developed for segmentation of the carotid vessel wall and plaques, automated classification for identifying vulnerable plaques, and the generation of biomarkers that are sensitive to the effects of medical/dietary treatments and accurate in stroke risk stratification. These techniques have great potential in making the assessment of arterial wall changes more efficient and accurate. 

We are seeking contributions presenting:

(1) The instrumentation of novel non-invasive ultrasound imaging techniques;

(2) Machine learning methods that can accelerate measurements of atherosclerotic burden and allow for an efficient plaque characterization from ultrasound images;

(3) The development of novel biomarkers that allow for accurate stroke risk stratification, the sensitive monitoring of patients treated for atherosclerosis, and the sensitive detection of treatment effects of new anti-atherosclerotic treatments;

(4) Techniques for carotid plaque tissue characterization and classification, and the prediction of cardiovascular and stroke from carotid ultrasound images.

Dr. Bernard Chiu
Dr. Ran Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ultrasound imaging
  • atherosclerosis
  • biomarkers
  • risk stratification
  • treatment effect evaluation
  • vessel wall and plaque segmentation

Published Papers (1 paper)

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Research

21 pages, 3497 KiB  
Article
Development of a Three-Dimensional Carotid Ultrasound Image Segmentation Workflow for Improved Efficiency, Reproducibility and Accuracy in Measuring Vessel Wall and Plaque Volume and Thickness
by Yuan Zhao, Mingjie Jiang, Wai Sum Chan and Bernard Chiu
Bioengineering 2023, 10(10), 1217; https://doi.org/10.3390/bioengineering10101217 - 18 Oct 2023
Viewed by 1012
Abstract
Automated segmentation of carotid lumen-intima boundary (LIB) and media-adventitia boundary (MAB) by deep convolutional neural networks (CNN) from three-dimensional ultrasound (3DUS) images has made assessment and monitoring of carotid atherosclerosis more efficient than manual segmentation. However, training of CNN still requires manual segmentation [...] Read more.
Automated segmentation of carotid lumen-intima boundary (LIB) and media-adventitia boundary (MAB) by deep convolutional neural networks (CNN) from three-dimensional ultrasound (3DUS) images has made assessment and monitoring of carotid atherosclerosis more efficient than manual segmentation. However, training of CNN still requires manual segmentation of LIB and MAB. Therefore, there is a need to improve the efficiency of manual segmentation and develop strategies to improve segmentation accuracy by the CNN for serial monitoring of carotid atherosclerosis. One strategy to reduce segmentation time is to increase the interslice distance (ISD) between segmented axial slices of a 3DUS image while maintaining the segmentation reliability. We, for the first time, investigated the effect of ISD on the reproducibility of MAB and LIB segmentations. The intra-observer reproducibility of LIB and MAB segmentations at ISDs of 1 mm and 2 mm was not statistically significantly different, whereas the reproducibility at ISD = 3 mm was statistically lower. Therefore, we conclude that segmentation with an ISD of 2 mm provides sufficient reliability for CNN training. We further proposed training the CNN by the baseline images of the entire cohort of patients for automatic segmentation of the follow-up images acquired for the same cohort. We validated that segmentation with this time-based partitioning approach is more accurate than that produced by patient-based partitioning, especially at the carotid bifurcation. This study forms the basis for an efficient, reproducible, and accurate 3DUS workflow for serial monitoring of carotid atherosclerosis useful in risk stratification of cardiovascular events and in evaluating the efficacy of new treatments. Full article
(This article belongs to the Special Issue Development of Ultrasound Techniques for Cardiovascular Disease Assessment and Monitoring)
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