Application of convolutional artificial neural networks to echocardiograms for differentiating congenital heart diseases in a pediatric population

Douglas P. Perrin; Alejandra Bueno; Andrea Rodriguez; Gerald R. Marx; Pedro J. del Nido

doi:10.1117/12.2254083

3 March 2017 Application of convolutional artificial neural networks to echocardiograms for differentiating congenital heart diseases in a pediatric population

Douglas P. Perrin, Alejandra Bueno, Andrea Rodriguez, Gerald R. Marx, Pedro J. del Nido

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 1013431 (2017) https://doi.org/10.1117/12.2254083
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

In this paper we describe a pilot study, where machine learning methods are used to differentiate between congenital heart diseases. Our approach was to apply convolutional neural networks (CNNs) to echocardiographic images from five different pediatric populations: normal, coarctation of the aorta (CoA), hypoplastic left heart syndrome (HLHS), transposition of the great arteries (TGA), and single ventricle (SV). We used a single network topology that was trained in a pairwise fashion in order to evaluate the potential to differentiate between patient populations. In total we used 59,151 echo frames drawn from 1,666 clinical sequences. Approximately 80% of the data was used for training, and the remainder for validation. Data was split at sequence boundaries to avoid having related images in the training and validation sets. While training was done with echo images/frames, evaluation was performed for both single frame discrimination as well as sequence discrimination (by majority voting). In total 10 networks were generated and evaluated. Unlike other domains where this network topology has been used, in ultrasound there is low visual variation between classes. This work shows the potential for CNNs to be applied to this low-variation domain of medical imaging for disease discrimination.

Citation Download Citation

Douglas P. Perrin, Alejandra Bueno, Andrea Rodriguez, Gerald R. Marx, and Pedro J. del Nido "Application of convolutional artificial neural networks to echocardiograms for differentiating congenital heart diseases in a pediatric population", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013431 (3 March 2017); https://doi.org/10.1117/12.2254083

ACCESS THE FULL ARTICLE

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