Keras R-CNN: library for cell detection in biological images using deep neural networks
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12859_2020_Article_3635.pdf
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Author(s)
Hung, Jane
Goodman, Allen
Ravel, Deepali
Lopes, Stefanie C P
Rangel, Gabriel W
Nery, Odailton A
Malleret, Benoit
Nosten, Francois
Lacerda, Marcus V G
Ferreira, Marcelo U
Date Issued
July 11, 2020
Publisher
BioMed Central
Citation
BMC Bioinformatics. 2020 Jul 11;21(1):300
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s12859-020-03635-x
