Summary of Handling Geometric Domain Shifts in Semantic Segmentation Of Surgical Rgb and Hyperspectral Images, by Silvia Seidlitz et al.
Handling Geometric Domain Shifts in Semantic Segmentation of Surgical RGB and Hyperspectral Images
by Silvia Seidlitz, Jan Sellner, Alexander Studier-Fischer, Alessandro Motta, Berkin Özdemir, Beat P. Müller-Stich, Felix Nickel, Lena Maier-Hein
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 addresses the challenge of robust semantic segmentation in intraoperative image data, a crucial step towards enabling automatic surgical scene understanding and autonomous robotic surgery. The authors analyze state-of-the-art (SOA) semantic segmentation models when faced with geometric out-of-distribution (OOD) data, which is common in real-world open surgeries. They also propose an augmentation technique called “Organ Transplantation” to enhance generalizability. Experimental results show a significant performance drop of SOA organ segmentation models on geometric OOD data, both in conventional RGB and hyperspectral imaging (HSI) data. The proposed augmentation method improves SOA model performance by up to 67% for RGB data and 90% for HSI data, achieving performance at the level of in-distribution performance on real OOD test data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that computers can understand medical images taken during surgery, so robots can help doctors do surgeries better. Right now, computers have trouble understanding these images if they’re a little different from what they’ve seen before. The authors looked at how well some of the best computer models do when faced with these “different” images and found that they don’t do very well. They also came up with a way to make these models better by mixing in some extra information, which helped them do much better. |
Keywords
» Artificial intelligence » Scene understanding » Semantic segmentation
