TY - JOUR
T1 - Cascaded Conditional Generative Adversarial Networks with Multi-Scale Attention Fusion for Automated Bi-Ventricle Segmentation in Cardiac MRI
AU - Qi, Lin
AU - Zhang, Haoran
AU - Tan, Wenjun
AU - Qi, Shouliang
AU - Xu, Lisheng
AU - Yao, Yudong
AU - Qian, Wei
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Bi-ventricle segmentation
KW - MSAF module
KW - ROI extraction
KW - cascaded conditional generative adversarial networks (C-cGANs)
UR - http://www.scopus.com/inward/record.url?scp=85077997081&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077997081&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2956210
DO - 10.1109/ACCESS.2019.2956210
M3 - Article
AN - SCOPUS:85077997081
VL - 7
SP - 172305
EP - 172320
JO - IEEE Access
JF - IEEE Access
M1 - 8915846
ER -