In BMC bioinformatics
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.
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, Rénia Laurent, Duraisingh Manoj T, Costa Fabio T M, Marti Matthias, Carpenter Anne E
Convolutional networks, Deep learning, Keras, Malaria, Object detection