DL-CSPF: deep-learning-based cell segmentation with a physical framework for digital holographic microscopy

Abstract

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.