Weakly-Supervised Biomechanically-Constrained CT/MRI Registration of the Spine
Jian B, Azampour MF, De Benetti F, Oberreuter J, Bukas C, Gersing AS, Foreman SC, Dietrich AS, Rischewski J, Kirschke JS, Navab N, Wendler T (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13436 LNCS
Pages Range: 227-236
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Singapore, SGP
ISBN: 9783031164453
DOI: 10.1007/978-3-031-16446-0_22
Abstract
Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are two of the most informative modalities in spinal diagnostics and treatment planning. CT is useful when analysing bony structures, while MRI gives information about the soft tissue. Thus, fusing the information of both modalities can be very beneficial. Registration is the first step for this fusion. While the soft tissues around the vertebra are deformable, each vertebral body is constrained to move rigidly. We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. To achieve this goal, we introduce anatomy-aware losses for training the network. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. We evaluate our method on an in-house dataset of 167 patients. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.
Involved external institutions
Germany (DE)How to cite
APA:
Jian, B., Azampour, M.F., De Benetti, F., Oberreuter, J., Bukas, C., Gersing, A.S.,... Wendler, T. (2022). Weakly-Supervised Biomechanically-Constrained CT/MRI Registration of the Spine. In Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 227-236). Singapore, SGP: Springer Science and Business Media Deutschland GmbH.
MLA:
Jian, Bailiang, et al. "Weakly-Supervised Biomechanically-Constrained CT/MRI Registration of the Spine." Proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, SGP Ed. Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li, Springer Science and Business Media Deutschland GmbH, 2022. 227-236.
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