Diffeomorphic unsupervised deep learning model for mono- and multi-modality registration

Theljani, Anis and Chen, Ke (2020) Diffeomorphic unsupervised deep learning model for mono- and multi-modality registration. Journal of Algorithms and Computational Technology, 14. pp. 1-10. ISSN 1748-3026 (https://doi.org/10.1177/1748302620973528)

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Abstract

Different from image segmentation, developing a deep learning network for image registration is less straightforward because training data cannot be prepared or supervised by humans unless they are trivial (e.g. pre-designed affine transforms). One approach for an unsupervised deep leaning model is to self-train the deformation fields by a network based on a loss function with an image similarity metric and a regularisation term, just with traditional variational methods. Such a function consists in a smoothing constraint on the derivatives and a constraint on the determinant of the transformation in order to obtain a spatially smooth and plausible solution. Although any variational model may be used to work with a deep learning algorithm, the challenge lies in achieving robustness. The proposed algorithm is first trained based on a new and robust variational model and tested on synthetic and real mono-modal images. The results show how it deals with large deformation registration problems and leads to a real time solution with no folding. It is then generalised to multi-modal images. Experiments and comparisons with learning and non-learning models demonstrate that this approach can deliver good performances and simultaneously generate an accurate diffeomorphic transformation.

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• Item metadata

  Item type:	Article
  ID code:	87259
  Dates:	Date Event 9 December 2020 Published 9 December 2020 Published Online 26 October 2020 Accepted 2 January 2020 Submitted
  Subjects:	Science > Mathematics
  Department:	UNSPECIFIED
  Depositing user:	Pure Administrator
  Date deposited:	09 Nov 2023 11:45
  Last modified:	18 Apr 2024 00:38
  URI:	https://strathprints.strath.ac.uk/id/eprint/87259