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)

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 > School of Infection & Immunity
• 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