We’re excited to share our latest research on the Segment Anything Model (SAM) 2. This empirical evaluation uncovers SAM 2’s robustness and generalization capabilities in surgical image/video segmentation, a critical component for enhancing precision and safety in the operating room.
๐ฌ **Key Findings**:
– In general, SAM 2 outperforms its predecessor in instrument segmentation, showing a much improved zero-shot generalization capability to the surgical domain.
– Utilizing bounding box prompts, SAM 2 achieves remarkable results, setting a new benchmark in the surgical image segmentation.
– With a single point as the prompt on the first frame, SAM 2 demonstrates substantial improvements on video segmentation over SAM, which requires point prompts on every frames. This suggests great potential in addressing video-based surgical tasks.
– Resilience Under Common Corruptions: SAM 2 shows impressive robustness against real-world image corruption, maintaining performance under various challenges such as compression, noise, and blur.
๐ง **Practical Implications**:
– With faster inference speeds, SAM 2 is poised to provide quick, accurate segmentation, making it a valuable asset in the clinical setting.
๐ **Learn More**:
For those interested in the technical depth, our paper is available on [arXiv](https://lnkd.in/gHfdrvj3).
We’re eager to engage with the community and explore how SAM 2 can revolutionize surgical applications.
Thanks to the team contributions of Jieming YU, An Wang, Wenzhen Dong, Mengya Xu, Jie Wang, Long Bai, Hongliang Ren from Department of Electronic Engineering, The Chinese University of Hong Kong and Shenzhen Research Institute of CUHK, and Mobarakol Islam from WEISS – Wellcome / EPSRC Centre for Interventional and Surgical Sciences, UCL.
