A GAN Based Multi-Contrast Modalities Medical Image Registration Approach

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 moda...

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Bibliographic Details
Published in2020 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
LanguageEnglish
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
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Summary: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.
ISSN:2381-8549
DOI:10.1109/ICIP40778.2020.9191024