Cellpose+, a morphological analysis tool for feature extraction of stained cell images
This work addresses the need for automated and accurate morphological analysis in cell biology research, but it is incremental as it builds upon an existing framework.
The paper tackles the problem of time-consuming image analysis in cell biology by extending Cellpose, a state-of-the-art cell segmentation framework, to include feature extraction capabilities for assessing morphological characteristics, and introduces a new dataset of DAPI and FITC stained cells for application.
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.