GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency

We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-based methods, we do not directly penalize transformation irregularities but instead pr...

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Vydáno v:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) Ročník 2023; s. 18084 - 18094
Hlavní autoři: Tian, Lin, Greer, Hastings, Vialard, Francois-Xavier, Kwitt, Roland, Estepar, Raul San Jose, Rushmore, Richard Jarrett, Makris, Nikolaos, Bouix, Sylvain, Niethammer, Marc
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.06.2023
Témata:
cell microscopy
Image registration
Jacobian matrices
Medical and biological vision
Protocols
Task analysis
Three-dimensional displays
Training
Tuning
ISSN:1063-6919, 1063-6919
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Shrnutí:We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-based methods, we do not directly penalize transformation irregularities but instead promote transformation regularity via an inverse consistency penalty. We use a neural network to predict a map between a source and a target image as well as the map when swapping the source and target images. Different from existing approaches, we compose these two resulting maps and regularize deviations of the Jacobian of this composition from the identity matrix. This regularizer - GradICON - results in much better convergence when training registration models compared to promoting inverse consistency of the composition of maps directly while retaining the desirable implicit regularization effects of the latter. We achieve state-of-the-art registration performance on a variety of real-world medical image datasets using a single set of hyperparameters and a single non-dataset-specific training protocol. Code is available at https://github.com/uncbiag/ICON.
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ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR52729.2023.01734