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Proceedings Paper

Automatic segmentation of left ventricle in cardiac cine MRI images based on deep learning
Author(s): Tian Zhou; Ilknur Icke; Belma Dogdas; Sarayu Parimal; Smita Sampath; Joseph Forbes; Ansuman Bagchi; Chih-Liang Chin; Antong Chen
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Paper Abstract

In developing treatment of cardiovascular diseases, short axis cine MRI has been used as a standard technique for understanding the global structural and functional characteristics of the heart, e.g. ventricle dimensions, stroke volume and ejection fraction. To conduct an accurate assessment, heart structures need to be segmented from the cine MRI images with high precision, which could be a laborious task when performed manually. Herein a fully automatic framework is proposed for the segmentation of the left ventricle from the slices of short axis cine MRI scans of porcine subjects using a deep learning approach. For training the deep learning models, which generally requires a large set of data, a public database of human cine MRI scans is used. Experiments on the 3150 cine slices of 7 porcine subjects have shown that when comparing the automatic and manual segmentations the mean slice-wise Dice coefficient is about 0.930, the point-to-curve error is 1.07 mm, and the mean slice-wise Hausdorff distance is around 3.70 mm, which demonstrates the accuracy and robustness of the proposed inter-species translational approach.

Paper Details

Date Published: 24 February 2017
PDF: 8 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331W (24 February 2017); doi: 10.1117/12.2253963
Show Author Affiliations
Tian Zhou, Merck Research Labs. (United States)
Rutgers, The State Univ. of New Jersey (United States)
Ilknur Icke, Merck Research Labs. (United States)
Belma Dogdas, Merck Research Labs. (United States)
Sarayu Parimal, Merck Research Labs. (Singapore)
Smita Sampath, Merck Research Labs. (Singapore)
Joseph Forbes, Merck Research Labs. (United States)
Ansuman Bagchi, Merck Research Labs. (United States)
Chih-Liang Chin, Merck Research Labs. (Singapore)
Antong Chen, Merck Research Labs. (United States)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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