SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909091630153728 |
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| author | Liu, Yihao Zhang, Jiaming She, Zhangcong Kheradmand, Amir Armand, Mehran |
| author_facet | Liu, Yihao Zhang, Jiaming She, Zhangcong Kheradmand, Amir Armand, Mehran |
| contents | The Segment Anything Model (SAM) is a new image segmentation tool trained with the largest available segmentation dataset. The model has demonstrated that, with prompts, it can create high-quality masks for general images. However, the performance of the model on medical images requires further validation. To assist with the development, assessment, and application of SAM on medical images, we introduce Segment Any Medical Model (SAMM), an extension of SAM on 3D Slicer - an image processing and visualization software extensively used by the medical imaging community. This open-source extension to 3D Slicer and its demonstrations are posted on GitHub (https://github.com/bingogome/samm). SAMM achieves 0.6-second latency of a complete cycle and can infer image masks in nearly real-time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_05622 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM Liu, Yihao Zhang, Jiaming She, Zhangcong Kheradmand, Amir Armand, Mehran Image and Video Processing Computer Vision and Pattern Recognition Machine Learning The Segment Anything Model (SAM) is a new image segmentation tool trained with the largest available segmentation dataset. The model has demonstrated that, with prompts, it can create high-quality masks for general images. However, the performance of the model on medical images requires further validation. To assist with the development, assessment, and application of SAM on medical images, we introduce Segment Any Medical Model (SAMM), an extension of SAM on 3D Slicer - an image processing and visualization software extensively used by the medical imaging community. This open-source extension to 3D Slicer and its demonstrations are posted on GitHub (https://github.com/bingogome/samm). SAMM achieves 0.6-second latency of a complete cycle and can infer image masks in nearly real-time. |
| title | SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2304.05622 |