A Short Review and Evaluation of SAM2's Performance in 3D CT Image Segmentation
<|reference_start|>A Short Review and Evaluation of SAM2's Performance in 3D CT Image Segmentation: Since the release of Segment Anything 2 (SAM2), the medical imaging community has been actively evaluating its performance for 3D medical image segmentation. However, different studies have employed varying evaluation pipelines, resulting in conflicting outcomes that obscure a clear understanding of SAM2's capabilities and potential applications. We shortly review existing benchmarks and point out that the SAM2 paper clearly outlines a zero-shot evaluation pipeline, which simulates user clicks iteratively for up to eight iterations. We reproduced this interactive annotation simulation on 3D CT datasets and provided the results and code~\url{https://github.com/Project-MONAI/VISTA}. Our findings reveal that directly applying SAM2 on 3D medical imaging in a zero-shot manner is far from satisfactory. It is prone to generating false positives when foreground objects disappear, and annotating more slices cannot fully offset this tendency. For smaller single-connected objects like kidney and aorta, SAM2 performs reasonably well but for most organs it is still far behind state-of-the-art 3D annotation methods. More research and innovation are needed for 3D medical imaging community to use SAM2 correctly.<|reference_end|>
@article{he2024a,
    title={A Short Review and Evaluation of SAM2's Performance in 3D CT Image
  Segmentation},
    author={Yufan He, Pengfei Guo, Yucheng Tang, Andriy Myronenko, Vishwesh Nath,
  Ziyue Xu, Dong Yang, Can Zhao, Daguang Xu, Wenqi Li},
    journal={arXiv preprint arXiv:2408.11210},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11210},
    primaryClass={cs.CV}
}