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Bibliographic Details
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:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence 68T07
Online Access:	https://arxiv.org/abs/2410.18738
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Table of 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.

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