TY - GEN
T1 - Morphological Simplification of Brain MR Images by Deep Learning for Facilitating Deformable Registration
AU - Wei, Dongming
AU - Ahmad, Sahar
AU - Wu, Zhengwang
AU - Cao, Xiaohuan
AU - Ren, Xuhua
AU - Li, Gang
AU - Shen, Dinggang
AU - Wang, Qian
N1 - Funding Information:
Acknowledgement. This work was partially supported by the National Key Research and Development Program of China (2018YFC0116400) and STCSM (19QC1400600).
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Brain MR image registration is challenging due to the large inter-subject anatomical variation. Especially, the highly convoluted brain cortex makes it difficult to accurately align the corresponding structures of the underlying images. In this paper, we propose a novel deep learning strategy to simplify the image registration task. Specifically, we train a morphological simplification network (MS-Net), which can generate a simplified image with fewer anatomical details given a complex input image. With this trained MS-Net, we can reduce the complexity of both the fixed and the moving images and iteratively derive their respective trajectories of gradually simplified images. The generated images at the ends of the two trajectories are so simple that they are very similar in appearance and morphology and thus easy to register. In this way, these two trajectories can act as a bridge to link the fixed and the moving images and guide their registration. Our experiments show that the proposed method can achieve more accurate registration results than state-of-the-art methods. Moreover, the proposed method can be generalized to the unseen dataset without the need for re-training or domain adaptation.
AB - Brain MR image registration is challenging due to the large inter-subject anatomical variation. Especially, the highly convoluted brain cortex makes it difficult to accurately align the corresponding structures of the underlying images. In this paper, we propose a novel deep learning strategy to simplify the image registration task. Specifically, we train a morphological simplification network (MS-Net), which can generate a simplified image with fewer anatomical details given a complex input image. With this trained MS-Net, we can reduce the complexity of both the fixed and the moving images and iteratively derive their respective trajectories of gradually simplified images. The generated images at the ends of the two trajectories are so simple that they are very similar in appearance and morphology and thus easy to register. In this way, these two trajectories can act as a bridge to link the fixed and the moving images and guide their registration. Our experiments show that the proposed method can achieve more accurate registration results than state-of-the-art methods. Moreover, the proposed method can be generalized to the unseen dataset without the need for re-training or domain adaptation.
KW - Brain MRI
KW - Deep learning
KW - Deformable registration
KW - Morphology
UR - http://www.scopus.com/inward/record.url?scp=85075673307&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32692-0_24
DO - 10.1007/978-3-030-32692-0_24
M3 - Conference contribution
AN - SCOPUS:85075673307
SN - 9783030326913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 203
EP - 211
BT - Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Suk, Heung-Il
A2 - Liu, Mingxia
A2 - Lian, Chunfeng
A2 - Yan, Pingkun
PB - Springer
T2 - 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 13 October 2019
ER -