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Erschienen in:
2020 | OriginalPaper | Buchkapitel
An Unsupervised Deep Learning Method for Diffeomorphic Mono-and Multi-modal Image Registration
verfasst von : Anis Theljani, Ke Chen
Erschienen in: Medical Image Understanding and Analysis
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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. In this paper we present an unsupervised deep leaning model in which the deformation fields are self-trained by an image similarity metric and a regularization term. The latter consists of a smoothing constraint on the derivatives and a constraint on the determinant of the transformation in order to obtain spatially smooth and plausible solution.
The proposed algorithm is first trained 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. Although any variational model may be used to work with our the deep learning algorithm, we present a new model using the reproducing Kernel Hilbert space theory, where an initial given pair of images, which are assumed non-linearly correlated, are first processed and optimized to serve the purpose of “intensity or edge correction” and to yield intermediate new images which are more strongly correlated and will be used for training the model. Experiments and comparisons with learning and non-learning models demonstrate that this approach can deliver good performance and simultaneously generate an accurate diffeomorphic transformation.