Deep Learning Approaches for Deformable Medical Image Registration: An In‐Depth Review and Analysis

ABSTRACT Deformable Medical Image Registration (DMIR) is integral to clinical workflows, aiding disease diagnosis, image‐guided interventions, and treatment monitoring. Recent advances in Deep Learning (DL) have propelled DMIR by enhancing registration efficiency and accuracy. This review systematically categorizes DL–based DMIR methods along three key dimensions: training strategies (fully supervised, weakly supervised, unsupervised), network architectures (single‐stage, multistage, inverse‐consistent), and imaging modalities (monomodal, multimodal). In addition, commonly used datasets and evaluation metrics are comprehensively summarized to facilitate fair comparisons across different approaches. Finally, this review discusses the persisting challenges from three perspectives: standardized datasets and evaluation criteria, registration framework design, and clinical translation, and outlines future research directions in light of recent advances in the field. DL‐based DMIR methods demonstrate rapid registration and robust performance across multiple datasets and metrics. Notably, unsupervised and multistage registration approaches achieve high accuracy and maintain structural plausibility, particularly in complex multimodal scenarios. Despite the notable achievements of DL‐based DMIR, several limitations hinder further advancements, including the scarcity of annotated datasets, fixed registration frameworks, and the ‘black box’ nature of DL models. Therefore, future research directions should focus on multistage model architectures and generative adversarial network (GAN) structures, combined with strategies for nonsupervision, lightweight, and automated learning of registration frameworks.