Cellpose model for cell segmentation using deep learning

Abstract

Cell segmentation is a crucial task in biomedical image analysis, with applications ranging from cancer research to drug discovery. This study investigates the Cellpose model, a deep learning-based approach for cell segmentation that has gained significant attention in recent years. We analyze the performance of Cellpose across various cell types and imaging modalities, comparing it with traditional segmentation methods and other deep learning models. Our findings demonstrate that Cellpose outperforms conventional techniques in terms of accuracy and generalization, particularly in challenging scenarios with diverse cell morphologies and dense cell populations. We also explore the model's limitations and propose potential improvements for future research.