Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection
This work addresses the need for accurate polyp segmentation without labor-intensive annotations, but it is incremental as it applies an existing model to a new medical domain.
The paper evaluates the Segment Anything Model 2 (SAM 2) for zero-shot polyp segmentation in colorectal cancer detection, assessing its performance under various prompted settings to provide insights for advancing the field.
Polyp segmentation plays a crucial role in the early detection and diagnosis of colorectal cancer. However, obtaining accurate segmentations often requires labor-intensive annotations and specialized models. Recently, Meta AI Research released a general Segment Anything Model 2 (SAM 2), which has demonstrated promising performance in several segmentation tasks. In this manuscript, we evaluate the performance of SAM 2 in segmenting polyps under various prompted settings. We hope this report will provide insights to advance the field of polyp segmentation and promote more interesting work in the future. This project is publicly available at https://github.com/ sajjad-sh33/Polyp-SAM-2.