Segmentation, or the outlining of objects within images, is a critical step in the measurement and analysis of cells within microscopy images. While improvements continue to be made in tools that rely on classical methods for segmentation, deep learning-based tools increasingly dominate advances in the technology. Specialist models such as Cellpose continue to improve in accuracy and user-friendliness, and segmentation challenges such as the Multi-Modality Cell Segmentation Challenge continue to push innovation in accuracy across widely-varying test data as well as efficiency and usability. Increased attention on documentation, sharing, and evaluation standards are leading to increased user-friendliness and acceleration towards the goal of a truly universal method.

翻译：分割，即在图像中勾画对象轮廓，是显微图像中细胞测量与分析的关键步骤。尽管依赖经典方法的分割工具持续改进，但基于深度学习的方法日益主导该技术的进步。Cellpose等专用模型在准确性和用户友好性方面不断提升，而多模态细胞分割挑战赛等赛事持续推动创新，旨在提升广泛多样化测试数据上的准确性、效率及可用性。对文档记录、共享和评估标准的日益重视，正推动工具更易使用，并加速实现真正通用方法的目标。