使用无标记4D显微镜进行自动细胞谱系重建

Automated Cell Lineage Reconstruction using Label-Free 4D Microscopy.

作者信息

Waliman Matthew, Johnson Ryan L, Natesan Gunalan, Tan Shiqin, Santella Anthony, Hong Ray L, Shah Pavak K

机构信息

Department of Electrical and Computer Engineering, University of California, Los Angeles, California, United States of America.

Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, California, United State of America.

出版信息

bioRxiv. 2024 Jan 22:2024.01.20.576449. doi: 10.1101/2024.01.20.576449.

DOI:10.1101/2024.01.20.576449

PMID:38328064

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10849476/

Abstract

Here we describe embGAN, a deep learning pipeline that addresses the challenge of automated cell detection and tracking in label-free 3D time lapse imaging. embGAN requires no manual data annotation for training, learns robust detections that exhibits a high degree of scale invariance and generalizes well to images acquired in multiple labs on multiple instruments.

摘要

在此，我们介绍embGAN，这是一种深度学习流程，可应对无标记三维延时成像中自动细胞检测和跟踪的挑战。embGAN在训练时无需人工数据标注，能学习到强大的检测方法，这些检测方法具有高度的尺度不变性，并且能很好地推广到在多个实验室使用多种仪器获取的图像上。