Paper Info
Title
Cascaded Conditional Generative Adversarial Networks With Multi-Scale Attention Fusion For Automated Bi-Ventricle Segmentation In Cardiac Mri
Abstract
Accurate segmentation of bi-ventricle from cardiac magnetic resonance images (MRI) is a critical step in cardiac function analysis and disease diagnosis. Due to the morphological diversification of the heart and the factors of MRI itself, fully automated and concurrent bi-ventricle segmentation is a well-known challenge. In this paper, we propose cascaded conditional generative adversarial networks (C-cGANs) to divide the problem into two segmentation subtasks: binary segmentation for region of interest (ROI) extraction and bi-ventricle segmentation. In both subtasks, we adopt adversarial training that makes discriminator network to discriminate segmentation maps either from generator network or ground-truth which aims to detect and correct pixel-wise inconsistency between the sources of segmentation maps. For capturing more spatial information with multi-scale semantic features, in the generator network, we insert a multi-scale attention fusion (MSAF) module between the encoder and decoder paths. The experiment on ACDC 2017 dataset shows that the proposed model outperforms other state-of-the-art methods in most metrics. Moreover, we validate the generalization capability of this model on MS-CMRSeg 2019 and RVSC 2012 datasets without fine-tuning, and the results demonstrate the effectiveness and robustness of the proposed method for bi-ventricle segmentation.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2956210
IEEE ACCESS
Keywords
DocType
Volume
Bi-ventricle segmentation, ROI extraction, cascaded conditional generative adversarial networks (C-cGANs), MSAF module
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Lin Qi131.45
Haoran Zhang200.34
Wenjun Tan300.34
Shouliang Qi4238.19
Lisheng Xu517839.09
Yu-Dong Yao61781119.83
W. Qian715522.21