分析测试学报2026，Vol.45Issue(8)：1687-1696,10.DOI:10.12452/j.fxcsxb.26050103
基于光学显微成像和深度学习的无标记肺癌细胞活力检测方法
Label-free Lung Cancer Cell Viability Detection Method Based on Optical Microscopy Imaging and Deep Learning
王慧慧 1梅正龙 2张宁 1邹雪 2沈成银1
作者信息
- 1. 安徽医科大学第一附属医院,安徽 合肥 230022||中国科学院合肥物质科学研究院健康与医学技术研究所,中国科学院合肥肿瘤医院,安徽 合肥 230031||安徽省医学物理与技术重点实验室,安徽 合肥 230031
- 2. 中国科学院合肥物质科学研究院健康与医学技术研究所,中国科学院合肥肿瘤医院,安徽 合肥 230031||安徽省医学物理与技术重点实验室,安徽 合肥 230031
- 折叠
摘要
Abstract
Lung cancer cell viability detection is widely used in anticancer drug development.How-ever,conventional detection methods can hardly meet the demand for continuous and dynamic moni-toring.In this study,a novel label-free detection method based on microscopic imaging and deep learning is developed.We take the non-small cell lung cancer A549 cell line as the object and con-struct a dataset containing bright-field images and corresponding cell viability annotations.For the al-gorithm,a dual-branch multi-scale feature fusion network Enhanced-UNet is designed,which com-bines the detail recovery capability of UNet++and the context modeling capability of DeepLabV3+.Spatial and channel squeeze-and-excitation and atrous spatial pyramid pooling modules are introduced to strengthen the extraction of detailed and global contextual features.The results show that the pro-posed model achieves a mean intersection over union(mIoU)of 0.762 3 and a mean dice coefficient(mDice)of 0.855 6 on the self-built dataset,with a prediction correlation coefficient r=0.912,out-performing five mainstream models including UNet,SegNet,FCN,PSPNet and LinkNet.Without chemical reagents,this method avoids interference to cell status and enables continuous and dynamic monitoring of A549 cell survival,providing a valuable reference for developing label-free lung cancer cell viability detection technology.关键词
细胞活力检测/光学显微/肺癌细胞/深度学习Key words
cell viability detection/optical microscopy/lung cancer cells/deep learning分类
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王慧慧,梅正龙,张宁,邹雪,沈成银..基于光学显微成像和深度学习的无标记肺癌细胞活力检测方法[J].分析测试学报,2026,45(8):1687-1696,10.
