Summary of Label-efficient Data Augmentation with Video Diffusion Models For Guidewire Segmentation in Cardiac Fluoroscopy, by Shaoyan Pan et al.
Label-Efficient Data Augmentation with Video Diffusion Models for Guidewire Segmentation in Cardiac Fluoroscopy
by Shaoyan Pan, Yikang Liu, Lin Zhao, Eric Z. Chen, Xiao Chen, Terrence Chen, Shanhui Sun
First submitted to arxiv on: 20 Dec 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 This paper proposes a novel deep learning model called the Segmentation-guided Frame-consistency Video Diffusion Model (SF-VD) to generate large collections of labeled fluoroscopy videos for guidewire segmentation tasks. The SF-VD model leverages videos with limited annotations by independently modeling scene distribution and motion distribution, generating 2D fluoroscopy images with wires positioned according to a specified input mask. The model then progressively generates subsequent frames while ensuring frame-to-frame coherence through a frame-consistency strategy. A segmentation-guided mechanism further refines the process by adjusting wire contrast, resulting in diverse ranges of visibility in the synthesized image. Evaluation on a fluoroscopy dataset confirms the superior quality of the generated videos and shows significant improvements in guidewire segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make more labeled videos for training computer models that help navigate during heart procedures. They need these labeled videos because current deep learning methods are very good, but they only work well if they have lots of data to learn from. The researchers developed an AI model called SF-VD that generates these labeled videos by combining information about the scene and motion in a video. This helps the model make better predictions when segmenting guidewires in fluoroscopy videos. |
Keywords
» Artificial intelligence » Deep learning » Diffusion model » Mask
