TY - GEN
T1 - Label-Driven Brain Deformable Registration Using Structural Similarity and Nonoverlap Constraints
AU - Hu, Shunbo
AU - Zhang, Lintao
AU - Xu, Yan
AU - Shen, Dinggang
N1 - Funding Information:
Acknowledgment. This work was supported in part by NSFC 61771230, 61773244, Shandong Provincial Natural Science Foundation ZR2019PF005, and Shandong Key R&D Program Project 2019GGX101006, 2019GNC106027. And we also thank for the open source code of Label-reg published by Hu Y et al.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Accurate deformable image registration is important for brain analysis. However, there are two challenges in deformation registration of brain magnetic resonance (MR) images. First, the global cerebrospinal fluid (CSF) regions are rarely aligned since most of them are located in narrow regions outside of gray matter (GM) tissue. Second, the small complex morphological structures in tissues are rarely aligned since dense deformation fields are too blurred. In this work, we use a weakly supervised registration scheme, which is driven by global segmentation labels and local segmentation labels via two special loss functions. Specifically, multiscale double Dice similarity is used to maximize the overlap of the same labels and also minimize the overlap of regions with different labels. The structural similarity loss function is further used to enhance registration performance of small structures, thus enhancing the whole image registration accuracy. Experimental results on inter-subject registration of T1-weighted MR brain images from the OASIS-1 dataset show that the proposed scheme achieves higher accuracy on CSF, GM and white matter (WM) compared with the baseline learning model.
AB - Accurate deformable image registration is important for brain analysis. However, there are two challenges in deformation registration of brain magnetic resonance (MR) images. First, the global cerebrospinal fluid (CSF) regions are rarely aligned since most of them are located in narrow regions outside of gray matter (GM) tissue. Second, the small complex morphological structures in tissues are rarely aligned since dense deformation fields are too blurred. In this work, we use a weakly supervised registration scheme, which is driven by global segmentation labels and local segmentation labels via two special loss functions. Specifically, multiscale double Dice similarity is used to maximize the overlap of the same labels and also minimize the overlap of regions with different labels. The structural similarity loss function is further used to enhance registration performance of small structures, thus enhancing the whole image registration accuracy. Experimental results on inter-subject registration of T1-weighted MR brain images from the OASIS-1 dataset show that the proposed scheme achieves higher accuracy on CSF, GM and white matter (WM) compared with the baseline learning model.
KW - Deformable registration
KW - Label-driven learning
KW - MR brain images
KW - Structural similarity
UR - http://www.scopus.com/inward/record.url?scp=85092726205&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59861-7_22
DO - 10.1007/978-3-030-59861-7_22
M3 - Conference contribution
AN - SCOPUS:85092726205
SN - 9783030598600
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 210
EP - 219
BT - Machine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Liu, Mingxia
A2 - Lian, Chunfeng
A2 - Yan, Pingkun
A2 - Cao, Xiaohuan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
Y2 - 4 October 2020 through 4 October 2020
ER -