Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914942997757952 |
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| author | Mansoori, Mobina Shahabodini, Sajjad Abouei, Jamshid Plataniotis, Konstantinos N. Mohammadi, Arash |
| author_facet | Mansoori, Mobina Shahabodini, Sajjad Abouei, Jamshid Plataniotis, Konstantinos N. Mohammadi, Arash |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05892 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection Mansoori, Mobina Shahabodini, Sajjad Abouei, Jamshid Plataniotis, Konstantinos N. Mohammadi, Arash Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2408.05892 |