Assessment of Cell Nuclei AI Foundation Models in Kidney Pathology
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909481497001984 |
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| author | Guo, Junlin Lu, Siqi Cui, Can Deng, Ruining Yao, Tianyuan Tao, Zhewen Lin, Yizhe Lionts, Marilyn Liu, Quan Xiong, Juming Wang, Yu Zhao, Shilin Chang, Catie Wilkes, Mitchell Yin, Mengmeng Yang, Haichun Huo, Yuankai |
| author_facet | Guo, Junlin Lu, Siqi Cui, Can Deng, Ruining Yao, Tianyuan Tao, Zhewen Lin, Yizhe Lionts, Marilyn Liu, Quan Xiong, Juming Wang, Yu Zhao, Shilin Chang, Catie Wilkes, Mitchell Yin, Mengmeng Yang, Haichun Huo, Yuankai |
| contents | Cell nuclei instance segmentation is a crucial task in digital kidney pathology. Traditional automatic segmentation methods often lack generalizability when applied to unseen datasets. Recently, the success of foundation models (FMs) has provided a more generalizable solution, potentially enabling the segmentation of any cell type. In this study, we perform a large-scale evaluation of three widely used state-of-the-art (SOTA) cell nuclei foundation models (Cellpose, StarDist, and CellViT). Specifically, we created a highly diverse evaluation dataset consisting of 2,542 kidney whole slide images (WSIs) collected from both human and rodent sources, encompassing various tissue types, sizes, and staining methods. To our knowledge, this is the largest-scale evaluation of its kind to date. Our quantitative analysis of the prediction distribution reveals a persistent performance gap in kidney pathology. Among the evaluated models, CellViT demonstrated superior performance in segmenting nuclei in kidney pathology. However, none of the foundation models are perfect; a performance gap remains in general nuclei segmentation for kidney pathology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06381 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Assessment of Cell Nuclei AI Foundation Models in Kidney Pathology Guo, Junlin Lu, Siqi Cui, Can Deng, Ruining Yao, Tianyuan Tao, Zhewen Lin, Yizhe Lionts, Marilyn Liu, Quan Xiong, Juming Wang, Yu Zhao, Shilin Chang, Catie Wilkes, Mitchell Yin, Mengmeng Yang, Haichun Huo, Yuankai Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Cell nuclei instance segmentation is a crucial task in digital kidney pathology. Traditional automatic segmentation methods often lack generalizability when applied to unseen datasets. Recently, the success of foundation models (FMs) has provided a more generalizable solution, potentially enabling the segmentation of any cell type. In this study, we perform a large-scale evaluation of three widely used state-of-the-art (SOTA) cell nuclei foundation models (Cellpose, StarDist, and CellViT). Specifically, we created a highly diverse evaluation dataset consisting of 2,542 kidney whole slide images (WSIs) collected from both human and rodent sources, encompassing various tissue types, sizes, and staining methods. To our knowledge, this is the largest-scale evaluation of its kind to date. Our quantitative analysis of the prediction distribution reveals a persistent performance gap in kidney pathology. Among the evaluated models, CellViT demonstrated superior performance in segmenting nuclei in kidney pathology. However, none of the foundation models are perfect; a performance gap remains in general nuclei segmentation for kidney pathology. |
| title | Assessment of Cell Nuclei AI Foundation Models in Kidney Pathology |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.06381 |