@article{MTMT:35657803,
	title = {DL-CSPF: deep-learning-based cell segmentation with a physical framework for digital holographic microscopy},
	url = {https://m2.mtmt.hu/api/publication/35657803},
	author = {Li, Zhuoshi and Gu, Haojie and Lu, Linpeng and Shen, Qian and Sun, Jiasong and Chen, Qian and Zuo, Chao},
	doi = {10.1364/AO.546044},
	journal-iso = {APPL OPTICS},
	journal = {APPLIED OPTICS},
	volume = {64},
	unique-id = {35657803},
	issn = {1559-128X},
	abstract = {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.},
	year = {2024},
	eissn = {2155-3165},
	pages = {B20-B30},
	orcid-numbers = {Lu, Linpeng/0000-0001-9102-2368; Shen, Qian/0000-0002-6826-805X; Sun, Jiasong/0000-0002-1936-1645; Chen, Qian/0000-0002-1909-302X; Zuo, Chao/0000-0002-1461-0032}
}