Dynamic Stream Network for Combinatorial Explosion Problem in Deformable Medical Image Registration

摘要

Combinatorial explosion problem caused by dual inputs presents a critical challenge in Deformable Medical Image Registration (DMIR). Since DMIR processes two images simultaneously as input, the combination relationships between features grow exponentially, ultimately the model considers more irrelevant features during the feature modeling process. Introducing dynamics in the receptive fields and weights of the network enables the model to eliminate the irrelevant features combination and model the potential feature combination relationships. In this paper, we propose the Dynamic Stream Network (DySNet), which enables the receptive fields and weights to be dynamically adjusted. This ultimately enables the model to ignore irrelevant feature combinations and model the potential feature relationships. With two key innovations: 1) Adaptive Stream Basin (AdSB) module dynamically adjusts the shape of the receptive field, thereby enabling the model to focus on the highly correlated feature relationships. 2) Dynamic Stream Attention (DySA) mechanism generates dynamic weights to search for more valuable feature relationships. Extensive experiments have shown that DySNet consistently outperforms the most advanced DMIR methods, highlighting its outstanding generalization ability. Our code is released on the website: https://github.com/ShaochenBi/DySNet.