Deterministic approaches using iterative optimisation have been historically successful in diffeomorphic image registration (DiffIR). Although these approaches are highly accurate, they typically carry a significant computational burden. Recent developments in stochastic approaches based on deep learning have achieved sub-second runtimes for DiffIR with competitive registration accuracy, offering a fast alternative to conventional iterative methods. In this paper, we attempt to reduce this difference in speed whilst retaining the performance advantage of iterative approaches in DiffIR. We first propose a simple iterative scheme that functionally composes intermediate non-stationary velocity fields to handle large deformations in images whilst guaranteeing diffeomorphisms in the resultant deformation. We then propose a convex optimisation model that uses a regularisation term of arbitrary order to impose smoothness on these velocity fields and solve this model with a fast algorithm that combines Nesterov gradient descent and the alternating direction method of multipliers (ADMM). Finally, we leverage the computational power of GPU to implement this accelerated ADMM solver on a 3D cardiac MRI dataset, further reducing runtime to less than 2 s. In addition to producing strictly diffeomorphic deformations, our methods outperform both state-of-the-art deep learning-based and iterative DiffIR approaches in terms of dice and Hausdorff scores, with speed approaching the inference time of deep learning-based methods.
|Title of host publication||Medical Image Computing and Computer Assisted Intervention – MICCAI 2021|
|Subtitle of host publication||24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part IV|
|Editors||Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert|
|Number of pages||11|
|Publication status||Published - 21 Sep 2021|
|Event||24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online|
Duration: 27 Sep 2021 → 1 Oct 2021
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021|
|Period||27/09/21 → 1/10/21|
Bibliographical noteFunding Information:
Acknowledgements. The research is supported by the BHF Accelerator Award (AA/18/2/34218), the Ramsay Research Fund from the School of Computer Science at the University of Birmingham and the Wellcome Trust Institutional Strategic Support Fund: Digital Health Pilot Grant.
© 2021, Springer Nature Switzerland AG.
- Image registration
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)