A GAN Based Multi-Contrast Modalities Medical Image Registration Approach

• Published in: 2020 IEEE International Conference on Image Processing (ICIP) pp. 3000 - 3004
• Main Authors: Qiao, Jinhao, Lai, Qirong, Li, Ying, Lan, Ting, Yu, Chunyan, Wang, Xiu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2020
• Subjects: Generators; Image registration; Measurement; Medical diagnostic imaging; Medical image registration; Multi-contrast; Multi-modalities; Star-Reg net; Task analysis
• DOI: 10.1109/ICIP40778.2020.9191024

Most current multi modalities medical image registration approaches are concerned about registering one modality image to another. However, in the real world, medical image registration may be involved in multiple modes, not just two specific modalities. To this end, we propose a multi-contrast modalities medical image registration modal (Star-Reg net). It uses a single generator and discriminator for all contrasts of registrations amount several modalities. Furthermore, the proposed approach is trained in an unsupervised way, which alleviates the requirement of manual annotation data. The experiment on the IXI dataset demonstrates the Star-Reg net effectiveness in multi-contrast modalities medical image registration.