Summary of Self-supervised Learning For Interventional Image Analytics: Towards Robust Device Trackers, by Saahil Islam et al.
Self-Supervised Learning for Interventional Image Analytics: Towards Robust Device Trackers
by Saahil Islam, Venkatesh N. Murthy, Dominik Neumann, Badhan Kumar Das, Puneet Sharma, Andreas Maier, Dorin Comaniciu, Florin C. Ghesu
First submitted to arxiv on: 2 May 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 In this paper, researchers develop a novel approach to detect and track devices such as guiding catheters in live X-ray images. This is crucial for endovascular cardiac interventions, enabling procedural guidance and ensuring safety and efficacy. To overcome challenges like device obscuration, changes in field-of-view, and continuous motion, the authors leverage self-supervision on a massive dataset of over 16 million interventional X-ray frames. Their masked image modeling technique learns spatio-temporal features using frame interpolation-based reconstruction, which is fine-tuned downstream. The proposed method achieves state-of-the-art performance, with a 66.31% reduction in maximum tracking error and a success score of 97.95%, while maintaining an inference speed of 42 frames-per-second (on GPU). This approach has the potential to be used in various interventional image analytics tasks that require understanding spatio-temporal semantics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to track devices like guiding catheters in X-ray images. This is important for heart procedures, helping doctors guide their tools and ensure safe and effective treatment. The authors solve problems like blocked views or moving objects by using a huge dataset of over 16 million X-ray frames. Their method learns patterns from the data and improves performance. It’s faster and more accurate than existing methods! |
Keywords
» Artificial intelligence » Inference » Semantics » Tracking
