A review accepted by PhotoniX Life examines how deep learning is transforming cell segmentation in live-cell microscopy. The article traces the field from thresholding and watershed algorithms to U-Net, Mask R-CNN, StarDist, Cellpose, Mesmer, transformer-based models, and emerging generalist segmentation frameworks. It highlights why segmentation is central to quantitative cell biology, where current models succeed, and what remains difficult, including cross-modality generalization, 3D volumetric data, annotation scarcity, user-friendly deployment, and organelle-level analysis.

The study reconceptualizes classroom assessment as a driver of teacher reflection and professional growth, introducing a structured reflection method that makes teachers' thinking visible and open to change.