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DeepCLEM:使用深度学习对相关光和电子显微镜进行自动注册

Rick Seifert^1,2, Sebastian M Markert², Sebastian Britz²

¹Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, 97074, Germany.

|July 3, 2023

概括

DeepCLEM通过从EM图像中预测光度来自动化相关光和电子显微镜 (CLEM) 注册. 这种新的方法消除了手动对齐,提高了生物成像工作流程的精度.

科学领域:

显微镜的使用方法
生物技术是生物技术.
计算生物学计算生物学

背景情况:

相对光和电子显微镜 (CLEM) 需要在光和电子显微镜 (EM) 数据集之间进行精确的图像注册.
目前的注册方法往往是手动或半自动的,依赖光斑或信托标记,这可能耗时并引入不准确性.
光和EM模式之间的图像对比差异阻碍了直接的自动对齐.

研究的目的:

在CLEM中开发一个完全自动化的工作流程,用于精确的图像注册.
克服手动和半自动注册方法在CLEM中的局限性.
为了提供一个强大的和适应性的解决方案,用于对比成像数据.

主要方法:

介绍了DeepCLEM,这是一个全新的,完全自动化的CLEM注册工作流程.
使用卷积神经网络 (CNN) 来直接从EM图像中预测光信号.
使用基于关联的对齐来记录预测的光信号与实验测量的染色体信号.

主要成果:

DeepCLEM实现了光和EM图像的全自动注册.
该工作流准确地预测来自EM数据的光信号.
使用基于相关性的对准与染色质信号来证明成功注册.

结论:

在自动化CLEM图像注册方面,DeepCLEM提供了显著的进步.
斐济插件促进了可访问性和集成到现有的显微镜工作流程.
该方法具有适应其他成像模式和3D数据集的潜力.

关键词:

相对显微镜学相对显微镜学深度学习 (Deep Learning) 是一种深度学习.图像注册图像注册图像注册在-silico标签的标签.

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