Uncertainty-Aware Deep Learning Based Deformable Registration
Grigorescu I, Uus A, Christiaens D, Cordero-Grande L, Hutter J, Batalle D, Edwards AD, Hajnal JV, Modat M, Deprez M (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12959 LNCS
Pages Range: 54-63
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783030877347
DOI: 10.1007/978-3-030-87735-4_6
Abstract
We introduce an uncertainty-aware deep learning deformable image registration solution for magnetic resonance imaging multi-channel data. In our proposed framework, the contributions of structural and microstructural data to the displacement field are weighted with spatially varying certainty maps. We produce certainty maps by employing a conditional variational autoencoder image registration network, which enables us to generate uncertainty maps in the deformation field itself. Our approach is quantitatively evaluated on pairwise registrations of 36 neonates to a standard structural and/or microstructural template, and compared with models trained on either single modality, or both modalities together. Our results show that by incorporating uncertainty while fusing the two modalities, we achieve superior alignment in cortical gray matter and white matter regions, while also achieving a good alignment of the white matter tracts. In addition, for each of our trained models, we show examples of average uncertainty maps calculated for 10 neonates scanned at 40 weeks post-menstrual age.
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United Kingdom (GB)How to cite
APA:
Grigorescu, I., Uus, A., Christiaens, D., Cordero-Grande, L., Hutter, J., Batalle, D.,... Deprez, M. (2021). Uncertainty-Aware Deep Learning Based Deformable Registration. In Carole H. Sudre, Roxane Licandro, Christian Baumgartner, Andrew Melbourne, Adrian Dalca, Jana Hutter, Ryutaro Tanno, Esra Abaci Turk, Koen Van Leemput, Jordina Torrents Barrena, William M. Wells, Christopher Macgowan (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 54-63). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Grigorescu, Irina, et al. "Uncertainty-Aware Deep Learning Based Deformable Registration." Proceedings of the 3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Carole H. Sudre, Roxane Licandro, Christian Baumgartner, Andrew Melbourne, Adrian Dalca, Jana Hutter, Ryutaro Tanno, Esra Abaci Turk, Koen Van Leemput, Jordina Torrents Barrena, William M. Wells, Christopher Macgowan, Springer Science and Business Media Deutschland GmbH, 2021. 54-63.
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