在宽带CARS显微镜中使用深度学习算法去除非共振背景
Federico Vernuccio1,2, Elia Broggio3, Salvatore Sorrentino4
1Department of Physics, Politecnico di Milano, P.zza Leonardo da Vinci 32, 20133, Milan, Italy. federico.vernuccio@fresnel.fr.
Scientific reports
|October 13, 2024
概括
深度学习模型在相干抗斯托克斯拉曼 (CARS) 显微镜中有效地去除非共振背景 (NRB). 新的CNN+GRU和GAN架构显示出高精度和实时处理能力,用于改进生物成像.
科学领域:
- 生物医学光学 生物医学光学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 宽带连贯抗斯托克斯拉曼 (BCARS) 显微镜可以快速,完整地获取生物样本的拉曼光谱.
- 在CARS信号中的非共振背景 (NRB) 扭曲了光谱数据,并降低了化学对比度.
- 传统的NRB删除需要人工算法和专家知识.
研究的目的:
- 审查现有的深度学习模型,以在BCARS中删除NRB.
- 引入和评估两个新的深度学习架构 (CNN+GRU和GAN) 用于NRB删除.
- 通过使用改进的,通用化的培训数据集来评估各种模型的性能.
主要方法:
- 对现有的深度学习模型 (SpecNet,VECTOR,LSTM,Bi-LSTM) 的审查.
- 开发和实施CNN+GRU和GAN架构.
- 在一般化数据集和实验BCARS数据上的培训和测试模型.
- 模型在光谱分离管道中的应用,用于BCARS图像.
主要成果:
- CNN+GRU和VECTOR模型在NRB去除方面表现出最高的准确性.
- 在实验数据中,GAN模型识别了最多的真正峰值.
- 对于实时BCARS图像处理,GAN和VECTOR模型是最适合的.
结论:
- 深度学习在BCARS显微镜中显著增强了NRB的去除.
- 新的CNN+GRU和GAN架构提供了竞争或优越的性能.
- 优化的模型可以在生物样本中进行更准确,更有效的化学对比成像.
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