GhostMorph: A Computationally Efficient Model for Deformable Inter-Subject Registration

This work presents GhostMorph, an innovative model for deformable inter-subject registration in medical imaging, inspired by GhostNet's principles. GhostMorph addresses the computational challenges inherent in medical image registration, particularly in deformable registration where complex local and global deformations are prevalent. By integrating Ghost modules and 3D depth-wise separable convolutions into its architecture, GhostMorph significantly reduces computational demands while maintaining high performance. The study benchmarks GhostMorph against state-of-the-art registration methods using the Liver Tumor Segmentation Benchmark (LiTS) dataset, demonstrating its comparable accuracy and improved computational efficiency. GhostMorph emerges as a viable, scalable solution for real-time and resource-constrained clinical scenarios, marking a notable advancement in medical image registration technology.