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Proceedings Paper

Left ventricular and atrial segmentation of 2D echocardiography with convolutional neural networks
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Paper Abstract

Segmentation of heart substructures in 2D echocardiography images is an important step in diagnosis and management of cardiovascular disease. Given the ubiquity of echocardiography in routine cardiology practice, the time-consuming nature of manual segmentation, and the high degree of inter-observer variability, fully automatic segmentation is a goal common to both clinicians and researchers. The recent publication of the annotated CA- MUS dataset will help catalyze these efforts. In this work we develop and validate against this dataset a deep fully convolutional neural network architecture for the multi-structure segmentation of echocardiography, in- cluding the left ventricular endocardium and epicardium, and the left atrium. In ten-fold cross validation with data augmentation, we obtain mean Dice overlaps of 0.93, 0.95, and 0.89 on the three structures respectively, representing state of the art on this dataset. We further report small biases and narrow limits of agreement between the automatic and manual segmentations in derived clinical indices, including median absolute errors for left ventricular diastolic (7.4mL) and systolic (4.8mL) volumes, and ejection fraction (4.1%), within previously reported inter-observer variability. These encouraging results must still be validated against large-scale independent clinical data.

Paper Details

Date Published: 10 March 2020
PDF: 7 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130A (10 March 2020); doi: 10.1117/12.2547375
Show Author Affiliations
Joshua V. Stough, Bucknell Univ. (United States)
Imaging Science and Innovation (United States)
Sushravya Raghunath, Imaging Science and Innovation (United States)
Xiaoyan Zhang, Imaging Science and Innovation (United States)
John M. Pfeifer, Imaging Science and Innovation (United States)
Brandon K. Fornwalt, Imaging Science and Innovation (United States)
Christopher M. Haggerty, Imaging Science and Innovation (United States)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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