Summary of Boosting Medical Image Synthesis Via Registration-guided Consistency and Disentanglement Learning, by Chuanpu Li et al.
Boosting Medical Image Synthesis via Registration-guided Consistency and Disentanglement Learning
by Chuanpu Li, Zeli Chen, Yiwen Zhang, Liming Zhong, Wei Yang
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medical image synthesis, a crucial task for various medical applications, is hindered by misalignment noise during training. Despite efforts to address this issue with registration-guided modules, existing methods overlook task-specific constraints on synthetic and registration modules. This paper proposes a novel approach that incorporates disentanglement learning to overcome these limitations. The proposed architecture, called registration-guided consistency, applies identical deformation fields before and after synthesis, while enforcing output consistency through an alignment loss. Additionally, the synthetic module is designed to disentangle anatomical structures and styles across various modalities, ensuring geometrical integrity within latent spaces. An anatomy consistency loss is introduced to further preserve anatomical correctness. Experimental results on abdominal CECT-CT and pelvic MR-CT datasets demonstrate the superiority of this method over existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical image synthesis is important for medical applications, but it’s hard because training data can be noisy and misaligned. Researchers have tried to fix this by adding special modules that help align images, but these methods don’t fully consider what they’re trying to achieve. This new approach takes a different tack by combining two techniques: one helps the computer generate more realistic images, and another makes sure those images are aligned correctly. The results show that this method is better than others at generating high-quality medical images. |
Keywords
» Artificial intelligence » Alignment » Image synthesis
