Ms. Jamie Alexis Goco Profile

Jamie Alexis Goco

at University of British Columbia

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 | 4 April 2022 Presentation + Paper

An efficient deep landmark detection network for PLAX EF estimation using sparse annotations

Jamie Alexis D. Goco, Mohammad Jafari, Christina Luong, Teresa Tsang, Purang Abolmaesumi

Proceedings Volume 12034, 120340N (2022) https://doi.org/10.1117/12.2611239

KEYWORDS: Video, Systems modeling, Data modeling, Heart, Ultrasonography, Error analysis

Read Abstract +

The parasternal long axis (PLAX) is a routine imaging plane used by clinicians to assess the overall function of the left ventricle during an echocardiogram. Measurements from the PLAX view, in particular left ventricle internal dimension at both end-diastole (LVIDd) and end-systole (LVIDs), are significant markers used to identify cardiovascular disease and can provide an estimation of ejection fraction (EF). However, due to the user-dependent nature of echocardiograms, these measurements suffer from a large amount of inter-observer variability, which greatly affect the sensitive formula used to calculate PLAX EF. While few previous works have attempted to reduce this variability by automating LVID measurements, their models not only lack reliable accuracy and precision, but also generally are not suited to be adapted in point-of-care ultrasound (POCUS) which has limited computing resources. In this paper, we propose a fully automatic, light-weight landmark detection network for detecting LVID and rapidly estimating PLAX EF. Our model is built upon recent advances in deep video landmark tracking with extremely sparse annotations.¹ The model is trained on only two frames in the cardiac cine that contain either the LVIDd or LVIDs measurements labeled by clinicians. Using data from 34,305 patients for our experiments, the proposed model accurately tracks the contraction of left ventricular walls. Our model achieves a mean absolute error and standard deviation of 2:65 ± 2:36 mm, 2:77 ± 2:58 mm, and 8:45 ± 7:43% for predicting LVIDd length, LVIDs length, and PLAX EF, respectively. As a light-weight network with less than 125,000 parameters, our model is extremely accessible for POCUS applications.

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