GhostMorph: a computationally efficient model for deformable inter-subject registration

Mingzhe Hu; Shaoyan Pan; Xiaofeng Yang

doi:10.1117/12.3006851

29 March 2024 GhostMorph: a computationally efficient model for deformable inter-subject registration

Mingzhe Hu, Shaoyan Pan, Xiaofeng Yang

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 129280W (2024) https://doi.org/10.1117/12.3006851
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingzhe Hu, Shaoyan Pan, and Xiaofeng Yang "GhostMorph: a computationally efficient model for deformable inter-subject registration", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 129280W (29 March 2024); https://doi.org/10.1117/12.3006851

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