Cellpose+, a Morphological Analysis Tool for Feature Extraction of Stained Cell Images
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 4′,6‐diamidino‐2‐phenylindole and fluorescein isothiocyanate stained cells to which our new method is applied.
Authors
- Israel Huaman (ORCID: https://orcid.org/0000-0003-4677-8259)
- Alexey E. Dudaev
- Tatiana G. Volova (ORCID: https://orcid.org/0000-0001-9392-156X)
- Sviatlana A. Ulasevich (ORCID: https://orcid.org/0000-0003-1753-911X)
- Galina A. Ryltseva (ORCID: https://orcid.org/0000-0001-7997-442X)
- Екатерина Владимировна Скорб (ORCID: https://orcid.org/0000-0003-0888-1693)
- Ekaterina I. Shishatskaya (ORCID: https://orcid.org/0000-0001-7967-243X)
- Pavel Zun (ORCID: https://orcid.org/0000-0001-6176-1143)
- Fares D.E. Ghorabe
- A. Писаренко
- Sofya S. Chumakova
Institutions
- ITMO University (RU)
- Institute of Biophysics (RU)
- Siberian Federal University (RU)
Publication Details
- Journal
- Advanced Intelligent Discovery
- Published
- 2025-06-18
- DOI
- https://doi.org/10.1002/aidi.202500005
- Citations
- 2
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.27