Back to Search
Start Over
DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Vol 16, Iss 4, p e1007673 (2020)
- Publication Year :
- 2020
- Publisher :
- Public Library of Science, 2020.
-
Abstract
- Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a "mother machine" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human intervention. Further, the algorithm is fast, with complete analysis of a typical frame containing ~150 cells taking<br />Author summary Automated microscopy experiments can generate massive data sets, allowing for detailed analysis of cell physiology and properties such as gene expression. In particular, dynamic measurements of gene expression with time-lapse microscopy have proved invaluable for understanding how gene regulatory networks operate. However, image processing remains a key bottleneck in the analysis pipeline, typically requiring human intervention and a posteriori processing. Recently, machine learning-based approaches have ushered in a new era of rapid, autonomous image analysis. In this work, we use and repurpose the U-Net deep learning algorithm to develop an image processing pipeline that can not only accurately identify the location of cells in an image, but also track them over time as they grow and divide. As an application, we focus on multi-hour time-lapse movies of bacteria growing in a microfluidic device. Our algorithm is accurate and fast, with error rates near 1% and requiring less than a second to analyze a typical movie frame. This increase in speed and fidelity has the potential to open new experimental avenues, e.g. where images are analyzed on-the-fly so that experimental conditions can be updated in real time.
- Subjects :
- 0301 basic medicine
Computer science
Pipeline (computing)
Microfluidics
Word error rate
Biochemistry
Machine Learning
0302 clinical medicine
Lab-On-A-Chip Devices
Morphogenesis
Image Processing, Computer-Assisted
Segmentation
Computer vision
Cell Cycle and Cell Division
Biology (General)
Throughput (business)
Cell Analysis
0303 health sciences
Ecology
Applied Mathematics
Simulation and Modeling
Bioassays and Physiological Analysis
Computational Theory and Mathematics
Cell Division Analysis
Cell Processes
Cell Tracking
Modeling and Simulation
Physical Sciences
Engineering and Technology
Fluidics
Single-Cell Analysis
Algorithms
Research Article
Computer and Information Sciences
QH301-705.5
Imaging Techniques
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Cell segmentation
Image processing
Image Analysis
Research and Analysis Methods
Green Fluorescent Protein
Bottleneck
Cellular and Molecular Neuroscience
03 medical and health sciences
Machine Learning Algorithms
Deep Learning
Artificial Intelligence
Genetics
Escherichia coli
Molecular Biology
Ecology, Evolution, Behavior and Systematics
030304 developmental biology
Electronic Data Processing
business.industry
Deep learning
Frame (networking)
Biology and Life Sciences
Proteins
Computational Biology
Cell Biology
Morphogenic Segmentation
Luminescent Proteins
030104 developmental biology
Microscopy, Fluorescence
Artificial intelligence
business
030217 neurology & neurosurgery
Mathematics
Software
Developmental Biology
Subjects
Details
- Language :
- English
- ISSN :
- 15537358 and 1553734X
- Volume :
- 16
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....6142ab1193d065b5cc42271c6f18df16