Suppr超能文献

基于深度学习的变形器官和运动动物中的细胞跟踪。

Deep Learning-Based Cell Tracking in Deforming Organs and Moving Animals.

机构信息

RIKEN Center for Biodynamic Research, Kobe, Japan.

出版信息

Methods Mol Biol. 2024;2800:203-215. doi: 10.1007/978-1-0716-3834-7_14.

Abstract

Cell tracking is an essential step in extracting cellular signals from moving cells, which is vital for understanding the mechanisms underlying various biological functions and processes, particularly in organs such as the brain and heart. However, cells in living organisms often exhibit extensive and complex movements caused by organ deformation and whole-body motion. These movements pose a challenge in obtaining high-quality time-lapse cell images and tracking the intricate cell movements in the captured images. Recent advances in deep learning techniques provide powerful tools for detecting cells in low-quality images with densely packed cell populations, as well as estimating cell positions for cells undergoing large nonrigid movements. This chapter introduces the challenges of cell tracking in deforming organs and moving animals, outlines the solutions to these challenges, and presents a detailed protocol for data preparation, as well as for performing cell segmentation and tracking using the latest version of 3DeeCellTracker. This protocol is expected to enable researchers to gain deeper insights into organ dynamics and biological processes.

摘要

细胞追踪是从移动细胞中提取细胞信号的关键步骤,对于理解各种生物功能和过程的机制至关重要,特别是在大脑和心脏等器官中。然而,活生物体中的细胞经常表现出广泛而复杂的运动,这是由器官变形和全身运动引起的。这些运动给获得高质量的细胞延时图像和跟踪所捕获图像中复杂的细胞运动带来了挑战。深度学习技术的最新进展为检测高密度细胞群体中的低质量图像中的细胞以及估计发生大非刚性运动的细胞的位置提供了强大的工具。本章介绍了在变形器官和运动动物中进行细胞追踪的挑战,概述了解决这些挑战的方法,并提供了一个详细的协议,用于使用最新版本的 3DeeCellTracker 进行数据准备以及执行细胞分割和跟踪。该协议有望使研究人员能够更深入地了解器官动态和生物过程。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验