Key Learnings
- Quantitative label free cell imaging with phase and polarization can separate cellular structures through physical properties while providing a gentle environment for cells
- 2.5D UNet based deep learning models and quantitative label free imaging are computationally more efficient and almost as accurate as 3D UNets in virtual staining applications
- Cellular morphological states and transitions can be extracted from quantitative label free images using DynaMorph, a deep-learning framework
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