Semantic similarity metrics for learned image registration

• URL: http://arxiv.org/abs/2104.10051v1
• Date: Tue, 20 Apr 2021 15:23:58 GMT
• Title: Semantic similarity metrics for learned image registration
• Authors: Steffen Czolbe, Oswin Krause and 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. We train both an unsupervised approach using an auto-encoder, and a semi-supervised approach using supplemental segmentation data to extract semantic features for image registration.
• Score: 10.355938901584565
• 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 approach learns dataset-specific features that drive the optimization of a learning-based registration model. We train both an unsupervised approach using an auto-encoder, and a semi-supervised approach using supplemental segmentation data to extract semantic features for image registration. Comparing to existing methods across multiple image modalities and applications, we achieve consistently high registration accuracy. A learned invariance to noise gives smoother transformations on low-quality images.

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