Paper Info
Title
Multi-Scale Feature Fusion Convolutional Neural Network for Concurrent Segmentation of Left Ventricle and Myocardium in Cardiac MR Images
Abstract
Accurate segmentation of the blood pool of left ventricle (LV) and myocardium (or left ventricular epicardium, MYO) from cardiac magnetic resonance (MR) can help doctors to quantify LV ejection fraction and myocardial deformation. To reduce doctor's burden of manual segmentation, in this study, we propose an automated and concurrent segmentation method of the LV and MYO. First, we employ a convolutional neural network (CNN) architecture to extract the region of interest (ROI) from short-axis cardiac cine MR images as a preprocessing step. Next, we present a multi-scale feature fusion (MSFF) CNN with a new weighted Dice index (WDI) loss function to get the concurrent segmentation of`the LV and MYO. We use MSFF modules with three scales to extract different features, and then concatenate feature maps by the short and long skip connections in the encoder and decoder path to capture more complete context information and geometry structure for better segmentation. Finally, we compare the proposed method with Fully Convolutional Networks (FCN) and U-Net on the combined cardiac datasets from MICCAI 2009 and ACDC 2017. Experimental results demonstrate that the proposed method could perform effectively on LV and MYOs segmentation in the combined datasets, indicating its potential for clinical application.
Year
DOI
Venue
2020
10.1166/jmihi.2020.3005
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
Keywords
DocType
Volume
MRI Segmentation,Left Ventricle and Myocardium,Multi-Scale Feature Fusion,Deep Learning,ROI Extraction
Journal
10
Issue
ISSN
Citations 
5
2156-7018
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Lin Qi131.45
Haoran Zhang211.37
Xuehao Cao300.34
Xuyang Lyu400.34
Lisheng Xu517839.09
Benqiang Yang600.34
Yangming Ou729117.18