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，即对图像中目标物体进行轮廓勾勒，是显微图像中细胞测量与分析的关键步骤。尽管基于经典方法的分割工具持续改进，基于深度学习的技术日益主导该领域的进展。诸如Cellpose等专用模型在准确性与用户友好性方面不断提升，而多模态细胞分割挑战赛等竞赛持续推动针对差异显著测试数据的精度提升，同时兼顾效率与易用性。对文档记录、共享与评估标准的日益重视，正推动工具更趋用户友好，并加速实现真正通用方法的终极目标。