Polyp SAM 2: Advancing Zero-Shot Polyp Segmentation in Colorectal Cancer Detection

The segmentation of polyps is crucial to the early detection and diagnosis of colorectal cancer. However, obtaining accurate segmentation often requires labor-intensive annotations and specialized models. Lately, Meta AI Research released the Segment Anything Model 2 (SAM 2), which provides bright results in a number of segmentation assignments. In this report, we analyze the capabilities of SAM 2 in segmenting polyps under various prompted conditions. We expect that this report will contribute to the improvement of polyp segmentation and stimulate further research into this field. This project is publicly available at https://github.com/sajjad-sh33/Polyp-SAM-2.

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Publication Details

Published
2025-05-26
DOI
https://doi.org/10.1109/ichms65439.2025.11154309
Citations
3
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
4.08
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article

Polyp SAM 2: Advancing Zero-Shot Polyp Segmentation in Colorectal Cancer Detection

Jamshid Abouei, Mobina Mansoori, Sajjad Shahabodini, Arash Mohammadi et al.
3 citations
Radiomics and Machine Learning in Medical Imaging
4.08
article

Polyp SAM 2: Advancing Zero-Shot Polyp Segmentation in Colorectal Cancer Detection

Jamshid Abouei, Mobina Mansoori, Sajjad Shahabodini, Arash Mohammadi, Konstantinos N. Plataniotis
article en
3 citations

Abstract

The segmentation of polyps is crucial to the early detection and diagnosis of colorectal cancer. However, obtaining accurate segmentation often requires labor-intensive annotations and specialized models. Lately, Meta AI Research released the Segment Anything Model 2 (SAM 2), which provides bright results in a number of segmentation assignments. In this report, we analyze the capabilities of SAM 2 in segmenting polyps under various prompted conditions. We expect that this report will contribute to the improvement of polyp segmentation and stimulate further research into this field. This project is publicly available at https://github.com/sajjad-sh33/Polyp-SAM-2.

Yazd University (IR), University of Toronto (CA), Concordia University (US)
Openalex Percentile: Top 6%
Radiomics and Machine Learning in Medical Imaging
4.08
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