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Proceedings Paper

Application of convolutional artificial neural networks to echocardiograms for differentiating congenital heart diseases in a pediatric population
Author(s): Douglas P. Perrin; Alejandra Bueno; Andrea Rodriguez; Gerald R. Marx; Pedro J. del Nido
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Paper 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.

Paper Details

Date Published: 3 March 2017
PDF: 9 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013431 (3 March 2017); doi: 10.1117/12.2254083
Show Author Affiliations
Douglas P. Perrin, Boston Children's Hospital (United States)
Harvard Medical School (United States)
Alejandra Bueno, Boston Children's Hospital (United States)
Andrea Rodriguez, Boston Children's Hospital (United States)
Gerald R. Marx, Boston Children's Hospital (United States)
Harvard Medical School (United States)
Pedro J. del Nido, Boston Children's Hospital (United States)
Harvard Medical School (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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