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 i...

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Main Authors 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
Format Journal Article
LanguageEnglish
Published 24.10.2024
Subjects
Computer Science - Artificial Intelligence
Computer Science - Computer Vision and Pattern Recognition
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Summary: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.
DOI:10.48550/arxiv.2410.18738