SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916296316157952 |
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| author | Trinh, Quoc-Huy Nguyen, Hai-Dang Ngoc, Bao-Tram Nguyen Jha, Debesh Bagci, Ulas Tran, Minh-Triet |
| author_facet | Trinh, Quoc-Huy Nguyen, Hai-Dang Ngoc, Bao-Tram Nguyen Jha, Debesh Bagci, Ulas Tran, Minh-Triet |
| contents | Polyp segmentation, a critical concern in medical imaging, has prompted numerous proposed methods aimed at enhancing the quality of segmented masks. While current state-of-the-art techniques produce impressive results, the size and computational cost of these models pose challenges for practical industry applications. Recently, the Segment Anything Model (SAM) has been proposed as a robust foundation model, showing promise for adaptation to medical image segmentation. Inspired by this concept, we propose SAM-EG, a framework that guides small segmentation models for polyp segmentation to address the computation cost challenge. Additionally, in this study, we introduce the Edge Guiding module, which integrates edge information into image features to assist the segmentation model in addressing boundary issues from current segmentation model in this task. Through extensive experiments, our small models showcase their efficacy by achieving competitive results with state-of-the-art methods, offering a promising approach to developing compact models with high accuracy for polyp segmentation and in the broader field of medical imaging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14819 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation Trinh, Quoc-Huy Nguyen, Hai-Dang Ngoc, Bao-Tram Nguyen Jha, Debesh Bagci, Ulas Tran, Minh-Triet Computer Vision and Pattern Recognition Polyp segmentation, a critical concern in medical imaging, has prompted numerous proposed methods aimed at enhancing the quality of segmented masks. While current state-of-the-art techniques produce impressive results, the size and computational cost of these models pose challenges for practical industry applications. Recently, the Segment Anything Model (SAM) has been proposed as a robust foundation model, showing promise for adaptation to medical image segmentation. Inspired by this concept, we propose SAM-EG, a framework that guides small segmentation models for polyp segmentation to address the computation cost challenge. Additionally, in this study, we introduce the Edge Guiding module, which integrates edge information into image features to assist the segmentation model in addressing boundary issues from current segmentation model in this task. Through extensive experiments, our small models showcase their efficacy by achieving competitive results with state-of-the-art methods, offering a promising approach to developing compact models with high accuracy for polyp segmentation and in the broader field of medical imaging. |
| title | SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.14819 |