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Convolutive Attention for Image Registration

Tim J. Parbs; Philipp Koch; Alfred Mertins
In: Proc. European Signal Processing Conference. European Signal Processing Conference (EUSIPCO-2022), August 28 - September 2, Belgrade, Serbia, Pages 1348-1352, IEEE, 2022.


Elastic registration of deformed images is a vitalcomponent of many computer vision tasks, especially whenconsidering medical image data. Deep learning techniques, particularlyU-Nets, offer state-of-the-art performance, but do notyet use the rich spatial information context available in naturalimages. We propose an augmentation based on the recentlyintroduced attention mechanism to allow a U-Net to use spatialimage context. A dedicated convolutive attention scheme hasbeen developed by calculating local similarity scores of themultidimensional inputs. Additionally, a dedicated compositeerror function based on common image similarity measures isintroduced in order to further improve the registration results.To evaluate our approach, we conducted several experiments onan augmented real-world dataset containing cardiac cine MRIscans. The comparison with state-of-the-art registration schemeshighlights the potential of our approach.