光散射成像模态扩张细胞计,用于深度学习的无标签单细胞分析
Zhi Li1, Xiaoyu Zhang2, Guosheng Li2
1School of Integrated Circuits, Shandong University, Jinan 250101, China; Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, China.
Computer methods and programs in biomedicine
|March 20, 2025
概括
本研究介绍了模态扩展细胞计,这是一种深度学习方法,可以从单模光散射数据创建多模态图像,用于无标签的单细胞分析. 这种技术增强了细胞可视化和分类准确性,特别是在癌症亚型.
科学领域:
- 生物医学成像技术 生物医学成像技术
- 计算生物学 计算生物学
- 细胞分析 细胞分析
背景情况:
- 单细胞成像对于药物开发,疾病诊断和个性化医学至关重要.
- 从无标签细胞获取多模式信息是一个重大挑战.
- 现有的方法通常需要标签或提供单细胞成像有限的数据.
研究的目的:
- 开发一种新的方法,即模态扩张细胞计,用于无标签的单细胞分析.
- 为了将单模光散射图像扩展到多模图像 (明亮场和光).
- 增强单细胞研究中的诊断和分析能力.
主要方法:
- 利用深度学习架构将单模光散射图像转换为多模表示.
- 采用一种新的网络优化方法,结合对抗性损失,L1距离损失和VGG感知损失.
- 使用模拟数据,标准球体和包括癌症和白血病细胞在内的各种细胞类型验证了该方法.
主要成果:
- 扩展的明亮场和光图像与传统显微镜的结果非常相匹配.
- 在整个细胞和细胞核中都获得了高的轮比精度 (接近1).
- 与单模成像相比,证明了宫癌细胞亚型的准确性 (92.85%) 的提高.
结论:
- 模态膨胀细胞计有效地从无标签的单细胞光散射数据生成人工多模态图像.
- 该技术提供了增强的细胞可视化和改进的细胞分类能力.
- 显示出在单细胞分析,包括癌症诊断中应用的巨大潜力.
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