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Papers

Cellpose+, a morphological analysis tool for feature extraction of stained cell images

2024-10-24 · Israel A. Huaman, Fares D. E. Ghorabe, Sofya S. Chumakova, Alexandra A. Pisarenko, Alexey E. Dudaev, Tatiana G. Volova, Galina A. Ryltseva, Sviatlana A. Ulasevich, Ekaterina I. Shishatskaya, Ekaterina V. Skorb, Pavel S. Zun

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.

📄 PDF Abstract BibTeX arXiv:2410.18738

Code (1)

itmo-mmrm-lab/cellpose 공식 구현 pytorch

Tasks

Cell SegmentationImage SegmentationMorphological AnalysisSegmentationSemantic Segmentation

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