Cellpose+, a morphological analysis tool for feature extraction of stained cell images
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866929557200699392 |
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| author | Huaman, Israel A. Ghorabe, Fares D. E. Chumakova, Sofya S. Pisarenko, Alexandra A. Dudaev, Alexey E. Volova, Tatiana G. Ryltseva, Galina A. Ulasevich, Sviatlana A. Shishatskaya, Ekaterina I. Skorb, Ekaterina V. Zun, Pavel S. |
| author_facet | Huaman, Israel A. Ghorabe, Fares D. E. Chumakova, Sofya S. Pisarenko, Alexandra A. Dudaev, Alexey E. Volova, Tatiana G. Ryltseva, Galina A. Ulasevich, Sviatlana A. Shishatskaya, Ekaterina I. Skorb, Ekaterina V. Zun, Pavel S. |
| contents | Advanced image segmentation and processing tools present an opportunity to study cell processes and their dynamics. However, image analysis is often routine and time-consuming. Nowadays, alternative data-driven approaches using deep learning are potentially offering automatized, accurate, and fast image analysis. In this paper, we extend the applications of Cellpose, a state-of-the-art cell segmentation framework, with feature extraction capabilities to assess morphological characteristics. We also introduce a dataset of DAPI and FITC stained cells to which our new method is applied. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18738 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cellpose+, a morphological analysis tool for feature extraction of stained cell images Huaman, Israel A. Ghorabe, Fares D. E. Chumakova, Sofya S. Pisarenko, Alexandra A. Dudaev, Alexey E. Volova, Tatiana G. Ryltseva, Galina A. Ulasevich, Sviatlana A. Shishatskaya, Ekaterina I. Skorb, Ekaterina V. Zun, Pavel S. Computer Vision and Pattern Recognition Artificial Intelligence 68T07 Advanced image segmentation and processing tools present an opportunity to study cell processes and their dynamics. However, image analysis is often routine and time-consuming. Nowadays, alternative data-driven approaches using deep learning are potentially offering automatized, accurate, and fast image analysis. In this paper, we extend the applications of Cellpose, a state-of-the-art cell segmentation framework, with feature extraction capabilities to assess morphological characteristics. We also introduce a dataset of DAPI and FITC stained cells to which our new method is applied. |
| title | Cellpose+, a morphological analysis tool for feature extraction of stained cell images |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T07 |
| url | https://arxiv.org/abs/2410.18738 |