SIRU-Net : Smoothing Inception Residual U-Net for Deformable Medical Image Registration

Volumetric medical image registration plays a vital role in computer-aided diagnosis and has recently benefited from Transformer-based learning techniques. Despite their effective global modeling capabilities, Transformers require a large number of parameters and significant computational resources, which limits their use in low-resource environments. Furthermore, they depend heavily on large datasets for optimal performance. Conversely, CNN-based methods excel in capturing detailed local information but face difficulties with global modeling and long-distance interactions. This study aims to develop a method that offers an extended receptive field, uses fewer parameters, and achieves notable results with a limited training dataset. We introduce the SIRU-Net (Smoothing Inception Residual U-Net) architecture, which combines an inception module to extend the receptive field, a residual module to enhance feature quality, and Gaussian convolution to improve information quality in the deeper stages of the network. Evaluations on inter-patient and atlas-patient datasets with limited data show that our approach outperforms transformer-based methods while utilizing only 5 % of their parameters.