DeepSim: Semantic similarity metrics for learned image registration

• URL: http://arxiv.org/abs/2011.05735v1
• Date: Wed, 11 Nov 2020 12:35:07 GMT
• Title: DeepSim: Semantic similarity metrics for learned image registration
• Authors: Steffen Czolbe, Oswin Krause, Aasa Feragen
• Abstract summary: We propose a semantic similarity metric for image registration. Our approach learns dataset-specific features that drive the optimization of a learning-based registration model.
• Score: 6.789370732159177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a semantic similarity metric for image registration. Existing metrics like euclidean distance or normalized cross-correlation focus on aligning intensity values, giving difficulties with low intensity contrast or noise. Our semantic approach learns dataset-specific features that drive the optimization of a learning-based registration model. Comparing to existing unsupervised and supervised methods across multiple image modalities and applications, we achieve consistently high registration accuracy and faster convergence than state of the art, and the learned invariance to noise gives smoother transformations on low-quality images.

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