Keras R-CNN: library for cell detection in biological images using deep neural networks

Hung, J. et al. (2020) Keras R-CNN: library for cell detection in biological images using deep neural networks. BMC Bioinformatics, 21, 300. (doi: 10.1186/s12859-020-03635-x) (PMID:32652926) (PMCID:PMC7353739)

[img]
Preview
Text
220387.pdf - Published Version
Available under License Creative Commons Attribution.

613kB

Abstract

Background: A common yet still manual task in basic biology research, high-throughput drug screening and digital pathology is identifying the number, location, and type of individual cells in images. Object detection methods can be useful for identifying individual cells as well as their phenotype in one step. State-of-the-art deep learning for object detection is poised to improve the accuracy and efficiency of biological image analysis. Results: We created Keras R-CNN to bring leading computational research to the everyday practice of bioimage analysts. Keras R-CNN implements deep learning object detection techniques using Keras and Tensorflow (https://github.com/broadinstitute/keras-rcnn). We demonstrate the command line tool’s simplified Application Programming Interface on two important biological problems, nucleus detection and malaria stage classification, and show its potential for identifying and classifying a large number of cells. For malaria stage classification, we compare results with expert human annotators and find comparable performance. Conclusions: Keras R-CNN is a Python package that performs automated cell identification for both brightfield and fluorescence images and can process large image sets. Both the package and image datasets are freely available on GitHub and the Broad Bioimage Benchmark Collection.

Item Type:Articles
Additional Information:Funding was provided by the National Institute of General Medical Sciences of the National Institutes of Health under R01 GM089652 and MIRA R35 GM122547 (to AEC). MM is supported by a career development award from BWF and a Wolfson Merit award from the Royal Society. DR was supported by a graduate fellowship (DGE1144152) from the U.S. National Science Foundation. FTMC is supported by FAPESP Grant 2017/18611–7. MVGL, MUF and FTMC are CNPq research fellows. GWR was supported by the HHMI Gilliam Fellowship for Advanced Study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Keywords:Software, Machine Learning and Artificial Intelligence in Bioinformatics, Deep learning, Keras, Convolutional networks, Malaria, Object detection
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Marti, Professor Matthias
Authors: Hung, J., Goodman, A., Ravel, D., Lopes, S. C.P., Rangel, G. W., Nery, O. A., Malleret, B., Nosten, F., Lacerda, M. V.G., Ferreira, M. U., Rénia, L., Duraisingh, M. T., Costa, F. T.M., Marti, M., and Carpenter, A. E.
College/School:College of Medical Veterinary and Life Sciences > Institute of Infection Immunity and Inflammation
Journal Name:BMC Bioinformatics
Publisher:BioMed Central
ISSN:1471-2105
ISSN (Online):1471-2105
Copyright Holders:Copyright © 2020 The Author(s)
First Published:First published in BMC Bioinformatics 21:300
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record