Automated analysis of high‐content microscopy data with deep learning

Kraus, Oren; Grys, Ben T.; Ba, Jimmy; Chong, Yolanda T.; Frey, Brendan J.; Boone, Charles; Andrews, Brenda

doi:10.15252/msb.20177551

Cited by 249 publications

(266 citation statements)

References 48 publications

(135 reference statements)

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“…Repositories of pre-trained models are already emerging (e.g. Caffe Model Zoo) and first examples of transfer learning have been successful [72,99], so we expect many more projects to make use of this idea in the near future.…”

Section: Discussionmentioning

confidence: 99%

Computational biology: deep learning

Jones

Alasoo

Fishman

et al. 2017

Emerging Topics in Life Sciences

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Deep learning is the trendiest tool in a computational biologist's toolbox. This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. Now, ideas for constructing and training networks and even off-the-shelf models have been adapted from the rapidly developing machine learning subfield to improve performance in a range of computational biology tasks. Here, we review some of these advances in the last 2 years.

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Section: Discussionmentioning

confidence: 99%

Computational biology: deep learning

Jones

Alasoo

Fishman

et al. 2017

Emerging Topics in Life Sciences

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“…Deep learning has shown a remarkable ability to extract information from images and it is increasingly being recognized that it is a natural fit for the image analysis needs of the life sciences 15,16 . As a result, deep learning is increasingly being applied to biological imaging data -applications include using classification to determine cellular phenotypes 17 , enhancing image resolution 18 , and extracting latent information from brightfield microscope images 19,20 . Of interest to those who use live-cell imaging has been the application of this technology to single-cell segmentation.…”

Section: Introductionmentioning

confidence: 99%

Caliban: Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Moen

Borba

Miller

et al. 2019

Preprint

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Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained on those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org.

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“…While these tools allow us to acquire substantially more data, it is still incumbent on the scientist to transform data into useful and actionable information. Manual image processing is unfeasible for those datasets, and automated data analysis techniques and methods are continuously being developed, even more now with the advent of machine learning tools, that often perform on par with human observers. Open‐source artificial intelligence (AI) software libraries such as Keras and TensorFlow, enable the power of neural networks and AI for the analysis of the datasets.…”

Section: Introductionmentioning

confidence: 99%

Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline

González

Evans

2019

BioEssays

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Here, a streamlined, scalable, laboratory approach is discussed that enables medium‐to‐large dataset analysis. The presented approach combines data management, artificial intelligence, containerization, cluster orchestration, and quality control in a unified analytic pipeline. The unique combination of these individual building blocks creates a new and powerful analysis approach that can readily be applied to medium‐to‐large datasets by researchers to accelerate the pace of research. The proposed framework is applied to a project that counts the number of plasmonic nanoparticles bound to peripheral blood mononuclear cells in dark‐field microscopy images. By using the techniques presented in this article, the images are automatically processed overnight, without user interaction, streamlining the path from experiment to conclusions.

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Automated analysis of high‐content microscopy data with deep learning

Cited by 249 publications

References 48 publications

Computational biology: deep learning

Computational biology: deep learning

Caliban: Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline

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