Data-Efficient Alignment in Medical Imaging via Reconfigurable Generative Networks

Recent advances in deep learning have witnessed many successful medical image translation models that learn correspondences between two visual domains. However, building robust mappings between domains is a significant challenge when handling misalignments caused by factors such as respiratory motion and anatomical changes. This issue is further exacerbated in scenarios with limited data availability, leading to a significant degradation in translation quality. In this paper, we introduce a novel data-efficient framework for aligning medical images via Reconfigurable Generative Network (Reconfig-MIT) for high-quality image translation. The key idea of Reconfig-MIT is to adaptively expand the generative network width within a Generative Adversarial Networks (GAN) architecture, initially expanding rapidly to capture low-level features and then slowing to refine high-level complexities. This dynamic network adaptation mechanism allows to adaptively learn at different rates, thus the model can better respond to deviations in the data caused by misalignments, while maintaining an effective equilibrium with the discriminator (D). We also introduce the Recursive Cycle-Consistency Loss (R-CCL), which extends the cycle consistency loss to effectively preserve key anatomical structures and their spatial relationships, improving translation quality. Extensive experiments show that Reconfig-MIT is a generic framework that enables easy integration with existing image translation methods, including those incorporating registration networks used for correcting misalignments, and provides robust and high-quality translation on paired and unpaired misaligned data in both data-rich and data-limited scenarios. https://github.com/IntellicentAI-Lab/Reconfig-MIT.