TY  - JOUR
AU  - Li, Zhuoshi
AU  - Gu, Haojie
AU  - Lu, Linpeng
AU  - Shen, Qian
AU  - Sun, Jiasong
AU  - Chen, Qian
AU  - Zuo, Chao
TI  - DL-CSPF: deep-learning-based cell segmentation with a physical framework for digital holographic microscopy
JF  - APPLIED OPTICS
J2  - APPL OPTICS
VL  - 64
PY  - 2024
IS  - 7
SP  - B20
EP  - B30
SN  - 1559-128X
DO  - 10.1364/AO.546044
UR  - https://m2.mtmt.hu/api/publication/35657803
ID  - 35657803
AB  - Digital holographic microscopy(DHM) offers label-free, full-field imaging of live-cell samples bycapturing optical path differences to produce quantitative phaseimages. Accurate cell segmentation from phase images is crucial forlong-term quantitative analysis. However, complicated cellular states(e.g., cell adhesion, proliferation, and apoptosis) and imagingconditions (e.g., noise and magnification) pose significantchallenge to the accuracy of cell segmentation. Here, we introduceDL-CSPF, a deep-learning-based cell segmentation method with aphysical framework designed for high-precision live-cell analysis.DL-CSPF utilizes two neural networks for foreground-backgroundsegmentation and cell detection, generating foreground edges and “seedpoints.” These features serve as input for a marker-controlledwatershed algorithm to segment cells. By focusing on foreground edgesand “seed points”, which have lower information entropy than completecell contours, DL-CSPF achieves accurate segmentation with a reduceddataset and without manual parameter tuning. We validated thefeasibility and generalization of DL-CSPF using various open-sourceand DHM-collected datasets, including HeLa, pollen, and COS-7 cells.Long-term live-cell imaging results further demonstrate that DL-CSPFreliably characterized and quantitatively analyzed the morphologicalmetrics across the cellular lifecycle, rendering it a promising toolfor biomedical research.
LA  - English
DB  - MTMT
ER  -