Digital holographic microscopy
(DHM) offers label-free, full-field imaging of live-cell samples by
capturing optical path differences to produce quantitative phase
images. Accurate cell segmentation from phase images is crucial for
long-term quantitative analysis. However, complicated cellular states
(e.g., cell adhesion, proliferation, and apoptosis) and imaging
conditions (e.g., noise and magnification) pose significant
challenge to the accuracy of cell segmentation. Here, we introduce
DL-CSPF, a deep-learning-based cell segmentation method with a
physical framework designed for high-precision live-cell analysis.
DL-CSPF utilizes two neural networks for foreground-background
segmentation and cell detection, generating foreground edges and “seed
points.” These features serve as input for a marker-controlled
watershed algorithm to segment cells. By focusing on foreground edges
and “seed points”, which have lower information entropy than complete
cell contours, DL-CSPF achieves accurate segmentation with a reduced
dataset and without manual parameter tuning. We validated the
feasibility and generalization of DL-CSPF using various open-source
and DHM-collected datasets, including HeLa, pollen, and COS-7 cells.
Long-term live-cell imaging results further demonstrate that DL-CSPF
reliably characterized and quantitatively analyzed the morphological
metrics across the cellular lifecycle, rendering it a promising tool
for biomedical research.