Fully automated segmentation of left ventricular myocardium from 3D late gadolinium enhancement magnetic resonance images using a U-net convolutional neural network-based model

Fatemeh Zabihollahy; James A. White; Eranga Ukwatta

doi:10.1117/12.2512146

13 March 2019 Fully automated segmentation of left ventricular myocardium from 3D late gadolinium enhancement magnetic resonance images using a U-net convolutional neural network-based model

Fatemeh Zabihollahy, James A. White, Eranga Ukwatta

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109503C (2019) https://doi.org/10.1117/12.2512146
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Myocardial tissue characterization on 3-dimensional late gadolinium enhancement magnetic resonance (3D LGE MR) is of increasing clinical importance for the quantification and spatial mapping of myocardial scar, a recognized substrate for malignant ventricular arrhythmias. Success of this task is dependent upon reproducible segmentation of myocardial architecture in 3D-space. In this paper, we describe a novel method to segment left ventricle (LV) myocardium from 3D LGE MR images using a U-Net convolutional neural network (CNN)-based model. Our proposed network consists of shrinking and expanding paths, where image features are captured and localized through several convolutional, pooling and up-sampling layers. We trained our model using 2090 slices extracted and artificially augmented from 14 3D LGE MR datasets, followed by validation of the trained model on ten 3D LGE MR unobserved test datasets inclusive of 926 slices. Averages of Dice index (DI) and absolute volume difference as a percentage versus manual defined myocardial volumes (NAVD) on the test dataset were obtained, providing values of 86.61 ± 3.80 % and 12.95 ± 9.56%, respectively. These algorithm-generated results demonstrate usefulness of our proposed fully automated method for segmentation of the LV myocardium from 3D LGE MR images.

Citation Download Citation

Fatemeh Zabihollahy, James A. White, and Eranga Ukwatta "Fully automated segmentation of left ventricular myocardium from 3D late gadolinium enhancement magnetic resonance images using a U-net convolutional neural network-based model", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109503C (13 March 2019); https://doi.org/10.1117/12.2512146

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available