| 韩阳光,李奇峰,程嘉明,杨云鹏,张鹏飞,撒昱.衍射流式细胞仪中高速TDI相机的设计[J].电子测量与仪器学报,2025,39(7):107-114 |
| 衍射流式细胞仪中高速TDI相机的设计 |
| Design of high-speed TDI camera for diffraction flow cytometry |
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| DOI： |
| 中文关键词: 衍射流式细胞仪 TDI相机 FPGA 机器学习 |
| 英文关键词:diffraction flow cytometry time delay integration camera FPGA machine learning |
| 基金项目:国家自然科学基金项目（12474297）资助 |
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| Author | Institution |
| Han Yangguang | School of Precision Instrument and OptoElectronics Engineering, Tianjin University,Tianjin 300072,China |
| Li Qifeng | School of Precision Instrument and OptoElectronics Engineering, Tianjin University,Tianjin 300072,China |
| Cheng Jiaming | Medical School of Tianjin University,Tianjin 300072,China |
| 2Yang Yunpeng | School of Precision Instrument and OptoElectronics Engineering, Tianjin University,Tianjin 300072,China |
| Zhang Pengfei | School of Precision Instrument and OptoElectronics Engineering, Tianjin University,Tianjin 300072,China |
| Sa Yu | Medical School of Tianjin University,Tianjin 300072,China |
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| 中文摘要: |
| 开发了基于衍射成像的新型无染色流式细胞分析方法，此方法借助时间延迟积分（time delay integration, TDI）相机收集衍射图像，并通过机器学习算法识别细胞。然而，该方法的检测通量受TDI相机扫描频率的限制。为此，设计了一种TDI相机改进方案，旨在提高其扫描频率并验证其实用效果。首先对TDI相机时序控制进行优化，成功将TDI相机扫描频率从50 kHz提升至100 kHz。在验证实验中，基于改进后的相机采集衍射图像后，运用灰度共生矩阵（Gray-level co-occurrence matrix, GLCM）提取特征值，再采用支持向量机（support vector machine, SVM）和随机森林（random forest, RF）分类器开展机器学习训练，分别针对培养的正常肝脏细胞与HepG2肝癌细胞进行两分类识别，以及对3种肺癌细胞系（A549，NCI-H378 和 NCI-H446）进行三分类识别，所得测试集识别准确率分别为94.14%和95.20%。设计改进的系统在使细胞流速提高到原来的2倍的同时，能够采集到符合识别要求的图像，为高速成像领域提供了创新性的技术支持，具备显著的科学意义与应用价值。 |
| 英文摘要: |
| Our research group has previously developed a novel label-free flow cytometry method based on diffractive imaging, which utilizes a time delay integration (TDI) camera to capture diffraction images and employs machine learning algorithms for cell identification. However, the detection throughput is limited by the scanning frequency of the TDI camera. To address this limitation, we designed an TDI camera optimization scheme to increase the scanning frequency and verify its practical effectiveness. In this study, we optimized the timing control of the TDI camera, successfully increasing its scanning frequency from 50 kHz to 100 kHz. In the validation experiments, after capturing diffraction images with the optimized camera, we extracted feature values using the Gray-level co-occurrence matrix (GLCM) and conducted machine learning training with support vector machine (SVM) and random forest (RF) classifiers. The classifiers were used to distinguish between cultured normal liver cells and hepatocarcinoma HepG2 cells, and to classify three lung cancer cell lines (A549, NCI-H378, and NCI-H446) in a three-class identification task, achieving test set recognition accuracies of 94.14% and 95.20%, respectively. Our optimized system not only doubled the cell flow rate but also ensured the acquisition of images that meet the recognition requirements. This innovation provides a novel technical support for high-speed imaging, with significant scientific and practical value. |
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