Summary of Lacoste: Exploiting Stereo and Temporal Contexts For Surgical Instrument Segmentation, by Qiyuan Wang and Shang Zhao and Zikang Xu and S Kevin Zhou
LACOSTE: Exploiting stereo and temporal contexts for surgical instrument segmentation
by Qiyuan Wang, Shang Zhao, Zikang Xu, S Kevin Zhou
First submitted to arxiv on: 14 Sep 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 The proposed LACOSTE model addresses the limitations of existing methods for surgical instrument segmentation in minimally invasive surgeries and related applications. By exploiting location-agnostic contexts in stereo and temporal images, the model improves the robustness against appearance variation through temporal motion and view change. The LACOSTE model combines a query-based segmentation core with three performance-enhancing modules: disparity-guided feature propagation, pseudo stereo scheme for monocular videos, and stereo-temporal set classifier to mitigate transient failures. Finally, the location-agnostic classifier decouples location bias from mask prediction and enhances feature semantics. Experimental results on three public surgical video datasets, including EndoVis Challenge benchmarks and a real radical prostatectomy surgery dataset GraSP, demonstrate the promising performances of the LACOSTE model, which achieves comparable or favorable results with state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to find instruments in videos from surgeries. Right now, most methods only look at one frame at a time and don’t use information from other frames or different views. This can make them less accurate if the instrument moves or the view changes. The LACOSTE model is better because it uses information from multiple frames and views to help find instruments. It also has special modules to deal with tricky situations where the instrument might be hard to see. The model was tested on three different datasets of surgical videos, and it performed well compared to other state-of-the-art methods. |
Keywords
» Artificial intelligence » Mask » Semantics
