Summary of Segment Anything Model 2: An Application to 2d and 3d Medical Images, by Haoyu Dong and Hanxue Gu and Yaqian Chen and Jichen Yang and Yuwen Chen and Maciej A. Mazurowski
Segment anything model 2: an application to 2D and 3D medical images
by Haoyu Dong, Hanxue Gu, Yaqian Chen, Jichen Yang, Yuwen Chen, Maciej A. Mazurowski
First submitted to arxiv on: 1 Aug 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 Segment Anything Model (SAM) has expanded its capabilities to process video inputs with SAM 2, enabling applications in medical imaging. This paper evaluates SAM 2’s performance on both 2D and 3D medical images by collecting 21 datasets. Two evaluation settings are considered: multi-frame 3D segmentation for videos and 3D modalities, and single-frame 2D segmentation for all datasets. Results show similar performance to SAM under single-frame 2D segmentation, but variable performance under multi-frame 3D segmentation depending on slice annotation choices and propagation directions. The study enhances understanding of SAM 2’s behavior in the medical field and provides directions for future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SAM 2 can now process video inputs, which opens up new possibilities in medical imaging. Researchers collected 21 datasets from different medical imaging modalities to test SAM 2’s performance. They looked at how well SAM 2 did on both 2D and 3D images by comparing its results under two different settings: one for videos and 3D images, and another for all other types of images. The results show that SAM 2 is good at processing single frames from 2D images, but it’s not as consistent when processing video or 3D images. This study helps us understand how SAM 2 works in the medical field and gives ideas for future improvements. |
Keywords
» Artificial intelligence » Sam
