在显微镜中的光学偏差下的细胞细分模型的实用指南
Boyuan Peng1,2,3, Jiaju Chen1,2, P Bilha Githinji1,2
1Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
Computational and structural biotechnology journal
|October 10, 2024
概括
这项研究在显微镜光学偏差下评估深度学习细胞细分模型. 使用SwinS骨干的Cellpose 2.0和FPN显示出强度,新的模型 (PLCM) 帮助识别异常,以便更好地进行细胞分析.
科学领域:
- 生物医学成像学 生物医学成像学
- 计算生物学是一种计算生物学.
- 显微镜的使用方法
背景情况:
- 细胞细分对于分析生物医学研究中的细胞形态和行为至关重要.
- 深度学习,特别是卷积神经网络 (CNN),在细胞细分方面表现出色,但与光学偏差作斗争.
- 在各种异常下评估模型稳定性对于可靠的生物图像分析至关重要.
研究的目的:
- 评估模拟光学偏差下的细胞图像分割模型的性能.
- 为了比较不同深度学习模型和光和明亮场显微镜数据集的传统方法的稳定性.
- 引入一种用于识别偏差类型和幅度的新型模型,以指导细分模型选择.
主要方法:
- 模拟了各种光学偏差 (白,昏迷,球形偏差,三叶草,混合).
- 在DynamicNuclearNet和LIVECell数据集上评估了Otsu值,Mask R-CNN (配有FPN/C3头,ResNet/VGG/Swin变压器骨干),以及Cellpose 2.0.
- 开发并验证了点传播函数图像标签分类模型 (PLCM) 用于异常表征.
主要成果:
- 与SwinS骨干组合的FPN显示出对具有轻微偏差的简单细胞图像的优越稳定性.
- 在类似的异常条件下,Cellpose 2.0证明了复杂细胞图像的有效性.
- 从点传播函数 (PSF) 图像中,PLCM准确地识别了偏差类型和幅度.
结论:
- 特定的深度学习架构在细胞细分中的光学偏差方面提供不同程度的稳定性.
- 推Cellpose 2.0用于复杂的细胞图像与异常.
- 通过特征光学偏差,PLCM帮助研究人员选择适当的细分策略,提高细胞分析的可靠性.
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