Unsupervised deformable registration model based on multi-scale pyramid and accurate similarity measurement

The deformable registration is a critical task in medical image processing. Due to significant differences in texture patterns and intensity information between modalities, current multimodal registration algorithms fail to extract multimodal features accurately and lack effective similarity measures in complex regions. To address these challenges, we propose a multilevel pyramid large deformation multimodality registration elastic network (MPLD). The framework adopts a global-to-local strategy for registration and is divided into three stages: level0 and level2 stage (global registration stage) and level1 stage (local registration stage). We propose an accurate similarity measurement evaluator to measure the spatial difference between two images, this method combines morphological and deep learning, and then optimize registration by minimizing the errors predicted by the evaluator. In addition, we propose a pyramid multi-level registration network (PM-Net), the module includes two independent encoders to extract image features of different modes, and share the same decoder, using progressive deformation field estimation in the decoder. The proposed method was validated on publicly available datasets LPBA40, OASIS, and hospitals clinical CT/MR data. In clinical data registration, our method achieved an average DSC of 0.816 +/- 0.016, average ASSD of 0.894 +/- 0.128 mm, average Std. Jacobian of 0.289 +/- 0.012. Our algorithm achieved a higher registration accuracy compared with state-of-the-art registration methods. This method adopts a coarse-to-fine strategy for progressive deformation field prediction and leverages multi-scale feature aggregation to enhance feature extraction capability. It effectively handles large deformation registration tasks, and comparative experiments confirm the superior registration performance and generalization ability.