Learning a Metric for Multimodal Medical Image Registration without Supervision Based on Cycle Constraints.

ABSTRACT: Deep learning based medical image registration remains very difficult and often fails to improve over its classical counterparts where comprehensive supervision is not available, in particular for large transformations-including rigid alignment. The use of unsupervised, metric-based registration networks has become popular, but so far no universally applicable similarity metric is available for multimodal medical registration, requiring a trade-off between local contrast-invariant edge features or more global statistical metrics. In this work, we aim to improve over the use of handcrafted metric-based losses. We propose to use synthetic three-way (triangular) cycles that for each pair of images comprise two multimodal transformations to be estimated and one known synthetic monomodal transf

SUBMITTER: Siebert H 

PROVIDER: S-EPMC8840694 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature