TY - GEN
T1 - Automatic 3D Atrial Segmentation from GE-MRIs Using Volumetric Fully Convolutional Networks
AU - Xia, Qing
AU - Yao, Yuxin
AU - Hu, Zhiqiang
AU - Hao, Aimin
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - In this paper, we propose an approach for automatic 3D atrial segmentation from Gadolinium-enhanced MRIs based on volumetric fully convolutional networks. The entire framework consists of two networks, the first network is to roughly locate the atrial center based on a low-resolution down-sampled version of the input and cut out a fixed size area that covers the atrial cavity, leaving out other pixels irrelevant to reduce memory consumption, and the second network is to precisely segment atrial cavity from the cropped sub-regions obtained from last step. Both two networks are trained end-to-end from scratch using 2018 Atrial Segmentation Challenge (http://atriaseg2018.cardiacatlas.org/ ) dataset which contains 100 GE-MRIs for training, and our method achieves satisfactory segmentation accuracy, up to 0.932 in Dice Similarity Coefficient score evaluated on the 54 testing samples, which ranks 1st among all participants.
AB - In this paper, we propose an approach for automatic 3D atrial segmentation from Gadolinium-enhanced MRIs based on volumetric fully convolutional networks. The entire framework consists of two networks, the first network is to roughly locate the atrial center based on a low-resolution down-sampled version of the input and cut out a fixed size area that covers the atrial cavity, leaving out other pixels irrelevant to reduce memory consumption, and the second network is to precisely segment atrial cavity from the cropped sub-regions obtained from last step. Both two networks are trained end-to-end from scratch using 2018 Atrial Segmentation Challenge (http://atriaseg2018.cardiacatlas.org/ ) dataset which contains 100 GE-MRIs for training, and our method achieves satisfactory segmentation accuracy, up to 0.932 in Dice Similarity Coefficient score evaluated on the 54 testing samples, which ranks 1st among all participants.
KW - Automatic atrial segmentation
KW - Fully convolutional networks
KW - Gadolinium-enhanced-MRI
UR - https://www.scopus.com/pages/publications/85064055073
U2 - 10.1007/978-3-030-12029-0_23
DO - 10.1007/978-3-030-12029-0_23
M3 - 会议稿件
AN - SCOPUS:85064055073
SN - 9783030120283
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 211
EP - 220
BT - Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers
A2 - Young, Alistair
A2 - Rhode, Kawal
A2 - Pop, Mihaela
A2 - Mansi, Tommaso
A2 - Zhao, Jichao
A2 - McLeod, Kristin
A2 - Li, Shuo
A2 - Sermesant, Maxime
PB - Springer Verlag
T2 - 9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 16 September 2018
ER -