Can SAM Segment Polyps?
This is an incremental study applying an existing model to a new medical domain, which could inform future work in polyp segmentation.
The authors evaluated the Segment Anything Model (SAM) on polyp segmentation in medical imaging, finding it performed poorly without prompts, with a Dice score of only 0.15.
Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks. As we know, polyp segmentation is a fundamental task in the medical imaging field, which plays a critical role in the diagnosis and cure of colorectal cancer. In particular, applying SAM to the polyp segmentation task is interesting. In this report, we evaluate the performance of SAM in segmenting polyps, in which SAM is under unprompted settings. We hope this report will provide insights to advance this polyp segmentation field and promote more interesting works in the future. This project is publicly at https://github.com/taozh2017/SAMPolyp.