Diffusion tensor driven image registration: a deep learning approach
Grigorescu I, Uus A, Christiaens D, Cordero-Grande L, Hutter J, Edwards AD, Hajnal JV, Modat M, Deprez M (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer
Book Volume: 12120 LNCS
Pages Range: 131-140
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Portoroz, SVN
ISBN: 9783030501198
DOI: 10.1007/978-3-030-50120-4_13
Abstract
Tracking microsctructural changes in the developing brain relies on accurate inter-subject image registration. However, most methods rely on either structural or diffusion data to learn the spatial correspondences between two or more images, without taking into account the complementary information provided by using both. Here we propose a deep learning registration framework which combines the structural information provided by $$T:2$$-weighted ($$T:2$$w) images with the rich microstructural information offered by diffusion tensor imaging (DTI) scans. This allows our trained network to register pairs of images in a single pass. We perform a leave-one-out cross-validation study where we compare the performance of our multi-modality registration model with a baseline model trained on structural data only, in terms of Dice scores and differences in fractional anisotropy (FA) maps. Our results show that in terms of average Dice scores our model performs better in subcortical regions when compared to using structural data only. Moreover, average sum-of-squared differences between warped and fixed FA maps show that our proposed model performs better at aligning the diffusion data.
Involved external institutions
Belgium (BE)How to cite
APA:
Grigorescu, I., Uus, A., Christiaens, D., Cordero-Grande, L., Hutter, J., Edwards, A.D.,... Deprez, M. (2020). Diffusion tensor driven image registration: a deep learning approach. In Ziga Spiclin, Jamie McClelland, Jan Kybic, Orcun Goksel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 131-140). Portoroz, SVN: Springer.
MLA:
Grigorescu, Irina, et al. "Diffusion tensor driven image registration: a deep learning approach." Proceedings of the 9th International Workshop on Biomedical Image Registration, WBIR 2020, Portoroz, SVN Ed. Ziga Spiclin, Jamie McClelland, Jan Kybic, Orcun Goksel, Springer, 2020. 131-140.
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