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| 面向细胞显微成像的虚拟染色技术的研究 |
| Virtual staining techniques for cellular microscopic imaging |
| 投稿时间：2022-08-12 |
| DOI：10.3969/j.issn.1005-5630.2023.001.003 |
| 中文关键词: 深度学习 细胞成像 虚拟染色 |
| 英文关键词:deep learning cellular imaging virtual staining |
| 基金项目:上海市科技创新支持项目(2021-cyxt1-kj06) |
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| 摘要点击次数: 46 |
| 全文下载次数: 51 |
| 中文摘要: |
| 细胞显微成像是生物学研究中进行细胞表型检测、获取细胞特征信息的重要手段。传统荧光成像技术是目前主要的细胞成像手段，但是荧光成像系统结构复杂、成本较高，而且特异性染色会对细胞造成损伤。针对此问题，研究了一种虚拟染色技术，使用多模态配准算法执行严格配准明场和荧光图像数据集，改进网络架构、损失函数、后处理、硬件适应性用于训练优化，并且通过虚拟染色评价标准对染色转换偏差进行验证。该方法可以降低荧光成像对荧光成像设备的依赖，无需各种复杂的染色操作，将会减轻生物研究、病理分析、疾病诊断流程的负担。 |
| 英文摘要: |
| Cell microscopic imaging is an important tool for cell phenotype detection. Traditional fluorescence imaging techniques are widely used in the cell imaging. However, the fluorescence imaging instruments have complex structure and high cost. Besides, staining could cause damage to cells. To address this problem, this paper proposes a virtual staining technique that performs strict alignment of bright field and fluorescence image datasets using a multimodal alignment algorithm, and improves network architecture, loss function, post-processing, and hardware adaptation for training optimization. The staining conversion bias is calculated by the evaluation criteria of the virtual staining. The method presented in this paper could simplify the fluorescence imaging equipment and eliminates the need for various staining operations, which could reduce the burden of research and diagnostic processes for biologists and pathologists. |
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