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.
|Title of host publication
|Machine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
|Mingxia Liu, Chunfeng Lian, Pingkun Yan, Xiaohuan Cao
|Springer Science and Business Media Deutschland GmbH
|Number of pages
|Published - 2020
|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 - Lima, Peru
Duration: 2020 Oct 4 → 2020 Oct 4
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|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
|20/10/4 → 20/10/4
Bibliographical noteFunding 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.
© 2020, Springer Nature Switzerland AG.
- Deformable registration
- Label-driven learning
- MR brain images
- Structural similarity
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science