TY - GEN
T1 - Spherical u-net for infant cortical surface parcellation
AU - Zhao, Fenqiang
AU - Xia, Shunren
AU - Wu, Zhengwang
AU - Wang, Li
AU - Chen, Zengsi
AU - Lin, Weili
AU - Gilmore, John H.
AU - Shen, Dinggang
AU - Li, Gang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - In human brain MRI studies, it is of great importance to accurately parcellate cortical surfaces into anatomically and functionally meaningful regions. In this paper, we propose a novel end-to-end deep learning method by formulating surface parcellation as a semantic segmentation task on the sphere. To extend the convolutional neural networks (CNNs) to the spherical space, corresponding operations of surface convolution, pooling and upsampling are first developed to deal with data representation on spherical surface meshes, and then spherical CNNs are constructed accordingly. Specifically, the U-Net and SegNet architectures are transformed to the spherical representation for neonatal cortical surface parcellation. Experimental results on 90 neonates indicate the effectiveness and efficiency of our proposed spherical U-Net, in comparison with the spherical SegNet and the previous patch-wise classification method.
AB - In human brain MRI studies, it is of great importance to accurately parcellate cortical surfaces into anatomically and functionally meaningful regions. In this paper, we propose a novel end-to-end deep learning method by formulating surface parcellation as a semantic segmentation task on the sphere. To extend the convolutional neural networks (CNNs) to the spherical space, corresponding operations of surface convolution, pooling and upsampling are first developed to deal with data representation on spherical surface meshes, and then spherical CNNs are constructed accordingly. Specifically, the U-Net and SegNet architectures are transformed to the spherical representation for neonatal cortical surface parcellation. Experimental results on 90 neonates indicate the effectiveness and efficiency of our proposed spherical U-Net, in comparison with the spherical SegNet and the previous patch-wise classification method.
KW - Spherical u-net
KW - Surface parcellation
UR - http://www.scopus.com/inward/record.url?scp=85073909149&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2019.8759537
DO - 10.1109/ISBI.2019.8759537
M3 - Conference contribution
AN - SCOPUS:85073909149
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1882
EP - 1886
BT - ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Y2 - 8 April 2019 through 11 April 2019
ER -