Evaluating Design Choices for Deep Learning Registration Networks: Architecture Matters

Hanna Siebert, Lasse Hansen, Mattias P. Heinrich
Universität zu Lübeck, Institut für Medizinische Informatik


The variety of recently proposed deep learning models for deformable pairwise image registration leads to the question how beneficial certain architectural design considerations are for the registration performance. This paper aims to take a closer look at the impact of some basic network design choices, i.e. the number of feature channels, the number of convolutions per resolution level and the differences between partially independent processing streams for fixed and moving images and direct concatenation of input scans. Starting from a simple single-stream U-Net architecture, we investigate extensions and modifications and propose a model for 3D abdominal CT registration evaluated on data from the Learn2Reg challenge that outperforms the baseline network VoxelMorph used for comparison.

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