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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.08269 |
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| _version_ | 1866914713677332480 |
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| author | Gogoberidze, Nodar Cimini, Beth A. |
| author_facet | Gogoberidze, Nodar Cimini, Beth A. |
| contents | Segmentation, or the outlining of objects within images, is a critical step in the measurement and analysis of cells within microscopy images. While improvements continue to be made in tools that rely on classical methods for segmentation, deep learning-based tools increasingly dominate advances in the technology. Specialist models such as Cellpose continue to improve in accuracy and user-friendliness, and segmentation challenges such as the Multi-Modality Cell Segmentation Challenge continue to push innovation in accuracy across widely-varying test data as well as efficiency and usability. Increased attention on documentation, sharing, and evaluation standards are leading to increased user-friendliness and acceleration towards the goal of a truly universal method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_08269 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Defining the boundaries: challenges and advances in identifying cells in microscopy images Gogoberidze, Nodar Cimini, Beth A. Quantitative Methods Computer Vision and Pattern Recognition Segmentation, or the outlining of objects within images, is a critical step in the measurement and analysis of cells within microscopy images. While improvements continue to be made in tools that rely on classical methods for segmentation, deep learning-based tools increasingly dominate advances in the technology. Specialist models such as Cellpose continue to improve in accuracy and user-friendliness, and segmentation challenges such as the Multi-Modality Cell Segmentation Challenge continue to push innovation in accuracy across widely-varying test data as well as efficiency and usability. Increased attention on documentation, sharing, and evaluation standards are leading to increased user-friendliness and acceleration towards the goal of a truly universal method. |
| title | Defining the boundaries: challenges and advances in identifying cells in microscopy images |
| topic | Quantitative Methods Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.08269 |