TY - GEN
T1 - Semantic Hierarchy Guided Registration Networks for Intra-subject Pulmonary CT Image Alignment
AU - Chen, Liyun
AU - Cao, Xiaohuan
AU - Chen, Lei
AU - Gao, Yaozong
AU - Shen, Dinggang
AU - Wang, Qian
AU - Xue, Zhong
N1 - Funding Information:
Acknowledgement. This work was partially supported by the National Key Research and Development Program of China (2018YFC0116400).
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - CT scanning has been widely used for diagnosis, staging and follow-up studies of pulmonary nodules, where image registration plays an essential role in follow-up assessment of CT images. However, it is challenging to align subtle structures in the lung CTs often with large deformation. Unsupervised learning-based registration methods, optimized according to the image similarity metrics, become popular in recent years due to their efficiency and robustness. In this work, we consider segmented tissues, i.e., airways, lobules, and pulmonary vessel structures, in a hierarchical way and propose a multi-stage registration workflow to predict deformation fields. The proposed workflow consists of two registration networks. The first network is the label alignment network, used to align the given segmentations. The second network is the vessel alignment network, used to further predict deformation fields to register vessels in lungs. By combining these two networks, we can register lung CT images not only in the semantic level but also in the texture level. In experiments, we evaluated the proposed algorithm on lung CT images for clinical follow-ups. The results indicate that our method has better performance especially in aligning critical structures such as airways and vessel branches in the lung, compared to the existing methods.
AB - CT scanning has been widely used for diagnosis, staging and follow-up studies of pulmonary nodules, where image registration plays an essential role in follow-up assessment of CT images. However, it is challenging to align subtle structures in the lung CTs often with large deformation. Unsupervised learning-based registration methods, optimized according to the image similarity metrics, become popular in recent years due to their efficiency and robustness. In this work, we consider segmented tissues, i.e., airways, lobules, and pulmonary vessel structures, in a hierarchical way and propose a multi-stage registration workflow to predict deformation fields. The proposed workflow consists of two registration networks. The first network is the label alignment network, used to align the given segmentations. The second network is the vessel alignment network, used to further predict deformation fields to register vessels in lungs. By combining these two networks, we can register lung CT images not only in the semantic level but also in the texture level. In experiments, we evaluated the proposed algorithm on lung CT images for clinical follow-ups. The results indicate that our method has better performance especially in aligning critical structures such as airways and vessel branches in the lung, compared to the existing methods.
KW - Convolution neural network
KW - Lung CT follow-up
KW - Medical image registration
UR - http://www.scopus.com/inward/record.url?scp=85092708224&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59716-0_18
DO - 10.1007/978-3-030-59716-0_18
M3 - Conference contribution
AN - SCOPUS:85092708224
SN - 9783030597153
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 181
EP - 189
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
A2 - Martel, Anne L.
A2 - Abolmaesumi, Purang
A2 - Stoyanov, Danail
A2 - Mateus, Diana
A2 - Zuluaga, Maria A.
A2 - Zhou, S. Kevin
A2 - Racoceanu, Daniel
A2 - Joskowicz, Leo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Y2 - 4 October 2020 through 8 October 2020
ER -