Deep Learning-Based Medical Image Registration for Multi-Modal Fusion

Abstract

Medical image registration plays a critical role in integrating information from multiple imaging modalities for diagnosis, treatment planning, and monitoring in healthcare. Traditional registration methods often face challenges in handling the complexity and variability of medical images. Deep learning has shown promising results in various medical image analysis tasks, including registration. This paper proposes deep learning approaches for medical image registration to facilitate multi-modal data fusion in healthcare. We present a comprehensive review of deep learning-based registration methods, discuss their advantages and limitations, and provide insights into future research directions. Experimental results demonstrate the effectiveness of the proposed approaches in achieving accurate and robust registration for multi-modal fusion applications.