TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910565661671424 |
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| author | Wan, Jia Li, Wanhua Adhinarta, Jason Ken Banerjee, Atmadeep Sjostedt, Evelina Wu, Jingpeng Lichtman, Jeff Pfister, Hanspeter Wei, Donglai |
| author_facet | Wan, Jia Li, Wanhua Adhinarta, Jason Ken Banerjee, Atmadeep Sjostedt, Evelina Wu, Jingpeng Lichtman, Jeff Pfister, Hanspeter Wei, Donglai |
| contents | While imaging techniques at macro and mesoscales have garnered substantial attention and resources, microscale Volume Electron Microscopy (vEM) imaging, capable of revealing intricate vascular details, has lacked the necessary benchmarking infrastructure. In this paper, we address a significant gap in this field of neuroimaging by introducing the first-in-class public benchmark, BvEM, designed specifically for cortical blood vessel segmentation in vEM images. Our BvEM benchmark is based on vEM image volumes from three mammals: adult mouse, macaque, and human. We standardized the resolution, addressed imaging variations, and meticulously annotated blood vessels through semi-automatic, manual, and quality control processes, ensuring high-quality 3D segmentation. Furthermore, we developed a zero-shot cortical blood vessel segmentation method named TriSAM, which leverages the powerful segmentation model SAM for 3D segmentation. To extend SAM from 2D to 3D volume segmentation, TriSAM employs a multi-seed tracking framework, leveraging the reliability of certain image planes for tracking while using others to identify potential turning points. This approach effectively achieves long-term 3D blood vessel segmentation without model training or fine-tuning. Experimental results show that TriSAM achieved superior performances on the BvEM benchmark across three species. Our dataset, code, and model are available online at \url{https://jia-wan.github.io/bvem}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13961 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images Wan, Jia Li, Wanhua Adhinarta, Jason Ken Banerjee, Atmadeep Sjostedt, Evelina Wu, Jingpeng Lichtman, Jeff Pfister, Hanspeter Wei, Donglai Computer Vision and Pattern Recognition While imaging techniques at macro and mesoscales have garnered substantial attention and resources, microscale Volume Electron Microscopy (vEM) imaging, capable of revealing intricate vascular details, has lacked the necessary benchmarking infrastructure. In this paper, we address a significant gap in this field of neuroimaging by introducing the first-in-class public benchmark, BvEM, designed specifically for cortical blood vessel segmentation in vEM images. Our BvEM benchmark is based on vEM image volumes from three mammals: adult mouse, macaque, and human. We standardized the resolution, addressed imaging variations, and meticulously annotated blood vessels through semi-automatic, manual, and quality control processes, ensuring high-quality 3D segmentation. Furthermore, we developed a zero-shot cortical blood vessel segmentation method named TriSAM, which leverages the powerful segmentation model SAM for 3D segmentation. To extend SAM from 2D to 3D volume segmentation, TriSAM employs a multi-seed tracking framework, leveraging the reliability of certain image planes for tracking while using others to identify potential turning points. This approach effectively achieves long-term 3D blood vessel segmentation without model training or fine-tuning. Experimental results show that TriSAM achieved superior performances on the BvEM benchmark across three species. Our dataset, code, and model are available online at \url{https://jia-wan.github.io/bvem}. |
| title | TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.13961 |