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Proceedings Paper

Ultrasound segmentation of rat hearts using convolution neural networks
Author(s): James D. Dormer; Rongrong Guo; Ming Shen; Rong Jiang; Mary B. Wagner; Baowei Fei
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Paper Abstract

Ultrasound is widely used for diagnosing cardiovascular diseases. However, estimates such as left ventricle volume currently require manual segmentation, which can be time consuming. In addition, cardiac ultrasound is often complicated by imaging artifacts such as shadowing and mirror images, making it difficult for simple intensity-based automated segmentation methods. In this work, we use convolutional neural networks (CNNs) to segment ultrasound images of rat hearts embedded in agar phantoms into four classes: background, myocardium, left ventricle cavity, and right ventricle cavity. We also explore how the inclusion of a single diseased heart changes the results in a small dataset. We found an average overall segmentation accuracy of 70.0% ± 7.3% when combining the healthy and diseased data, compared to 72.4% ± 6.6% for just the healthy hearts. This work suggests that including diseased hearts with healthy hearts in training data could improve segmentation results, while testing a diseased heart with a model trained on healthy hearts can produce accurate segmentation results for some classes but not others. More data are needed in order to improve the accuracy of the CNN based segmentation.

Paper Details

Date Published: 6 March 2018
PDF: 9 pages
Proc. SPIE 10580, Medical Imaging 2018: Ultrasonic Imaging and Tomography, 105801A (6 March 2018); doi: 10.1117/12.2293558
Show Author Affiliations
James D. Dormer, Emory Univ. (United States)
Rongrong Guo, Emory Univ. (United States)
Ming Shen, Emory Univ. (United States)
Rong Jiang, Emory Univ. (United States)
Mary B. Wagner, Emory Univ. (United States)
Baowei Fei, Emory Univ. (United States)
Georgia Institute of Technology (United States)
Winship Cancer Institute of Emory Univ. (United States)

Published in SPIE Proceedings Vol. 10580:
Medical Imaging 2018: Ultrasonic Imaging and Tomography
Neb Duric; Brett C. Byram, Editor(s)

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