Summary of Data-centric Learning Framework For Real-time Detection Of Aiming Beam in Fluorescence Lifetime Imaging Guided Surgery, by Mohamed Abul Hassan et al.
Data-Centric Learning Framework for Real-Time Detection of Aiming Beam in Fluorescence Lifetime Imaging Guided Surgery
by Mohamed Abul Hassan, Pu Sun, Xiangnan Zhou, Lisanne Kraft, Kelsey T Hadfield, Katjana Ehrlich, Jinyi Qi, Andrew Birkeland, Laura Marcu
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The paper introduces a novel data-centric approach to improve real-time surgical guidance using fiber-based fluorescence lifetime imaging (FLIm). The primary challenge is detecting the aiming beam accurately, which is essential for localizing FLIm measurements onto the tissue region within the surgical field. To overcome this challenge, an instance segmentation model was developed using a data-centric training strategy that improves accuracy by minimizing label noise and enhancing detection robustness. The model was evaluated on 40 in vivo surgical videos, demonstrating a median detection rate of 85%. This performance was maintained when integrated into a clinical system, achieving a similar detection rate during TORS procedures conducted in patients. The system’s computational efficiency, measured at approximately 24 frames per second (FPS), was sufficient for real-time surgical guidance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps doctors perform surgery better using special imaging technology called FLIm. They developed a new way to detect the aiming beam, which is important for making sure the imaging is accurate. The new method uses machine learning and training data to make it more reliable. They tested this approach on 40 videos of real surgeries and found that it worked well, with an accuracy rate of 85%. This means doctors can use this technology in real-time during surgery without worrying about getting mixed up. |
Keywords
» Artificial intelligence » Instance segmentation » Machine learning
