Mr. Vu Q. Dinh Profile

Vu Q. Dinh

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 1 April 2024 Presentation + Paper

Echocardiogram image segmentation and cardiac adipose tissue estimation using spectral analysis and deep learning

Julian Cuellar, Lucas Gillette, Vu Dinh, Pamela Woodard, Manjula Burri, Jon Klingensmith

Proceedings Volume 12932, 129320C (2024) https://doi.org/10.1117/12.3004836

KEYWORDS: Computed tomography, Magnetic resonance imaging, Education and training, Ultrasonography, Image segmentation, Heart, Deep learning, Data modeling, Adipose tissue, Spectral data processing

Read Abstract +

Cardiovascular disease (CVD) is the main cause of death worldwide. Cardiac adipose tissue (CAT) around the heart correlates with CVD risk. MRI and CT scans are preferred for CAT quantification. Ultrasound (US) is limited to linear CAT thickness measurements on the right ventricle. Comprehensive CAT volume and distribution quantification with US could aid CVD assessment. This study uses deep learning to automatically segment the left myocardium (LM), left epicardium (LE), and right epicardium (RE) boundaries in echocardiograms. A U-Net ResNet34 model was trained using 506 expert-traced images. Data was split into 70/10/20 training/validation/test sets. The model achieved Dice scores of 0.94, 0.89, and 0.88 for the three boundaries respectively. Regions-of-interest (ROIs) around the combined left and right epicardium contours were classified as containing CAT or not using spectral analysis of raw RF data. MRI expert-traced images of the same patients provided ground truth labels for CAT. A total of 102 corresponding US-MRI image pairs were matched using visual assessment and anatomical landmarks. ROIs were defined around the predicted epicardium contours, and for each ROI, 9 spectral parameters were computed as a random forest classifier input. A randomized 75/25 training/test split was used. The classifier achieved an average accuracy of 76%, sensitivity of 85%, and specificity of 74%. Results show the feasibility of performing automatic CAT assessment involving echocardiogram segmentation, contour detection, and classification of ROIs around the perimeter of the left and right epicardium in short-axis echocardiograms.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years