Share Email Print

Proceedings Paper

Segmentation of left ventricle myocardium in porcine cardiac cine MR images using a hybrid of fully convolutional neural networks and convolutional LSTM
Author(s): Dongqing Zhang; Ilknur Icke; Belma Dogdas; Sarayu Parimal; Smita Sampath; Joseph Forbes; Ansuman Bagchi; Chih-Liang Chin; Antong Chen
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In the development of treatments for cardiovascular diseases, short axis cardiac cine MRI is important for the assessment of various structural and functional properties of the heart. In short axis cardiac cine MRI, Cardiac properties including the ventricle dimensions, stroke volume, and ejection fraction can be extracted based on accurate segmentation of the left ventricle (LV) myocardium. One of the most advanced segmentation methods is based on fully convolutional neural networks (FCN) and can be successfully used to do segmentation in cardiac cine MRI slices. However, the temporal dependency between slices acquired at neighboring time points is not used. Here, based on our previously proposed FCN structure, we proposed a new algorithm to segment LV myocardium in porcine short axis cardiac cine MRI by incorporating convolutional long short-term memory (Conv-LSTM) to leverage the temporal dependency. In this approach, instead of processing each slice independently in a conventional CNN-based approach, the Conv-LSTM architecture captures the dynamics of cardiac motion over time. In a leave-one-out experiment on 8 porcine specimens (3,600 slices), the proposed approach was shown to be promising by achieving average mean Dice similarity coefficient (DSC) of 0.84, Hausdorff distance (HD) of 6.35 mm, and average perpendicular distance (APD) of 1.09 mm when compared with manual segmentations, which improved the performance of our previous FCN-based approach (average mean DSC=0.84, HD=6.78 mm, and APD=1.11 mm). Qualitatively, our model showed robustness against low image quality and complications in the surrounding anatomy due to its ability to capture the dynamics of cardiac motion.

Paper Details

Date Published: 2 March 2018
PDF: 7 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740A (2 March 2018); doi: 10.1117/12.2293984
Show Author Affiliations
Dongqing Zhang, Merck & Co., Inc. (United States)
Vanderbilt Univ. (United States)
Ilknur Icke, Merck & Co., Inc. (United States)
Belma Dogdas, Merck & Co., Inc. (United States)
Sarayu Parimal, Merck Sharp & Dohme Corp. (Singapore)
Smita Sampath, Merck Sharp & Dohme Corp. (Singapore)
Joseph Forbes, Merck & Co., Inc. (United States)
Ansuman Bagchi, Merck & Co., Inc. (United States)
Chih-Liang Chin, Merck Sharp & Dohme Corp. (Singapore)
Antong Chen, Merck & Co., Inc. (United States)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

© SPIE. Terms of Use
Back to Top