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

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...

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Veröffentlicht in:BMC bioinformatics Jg. 21; H. 1; S. 300 - 7
Hauptverfasser: 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.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 11.07.2020
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Accuracy
Algorithms
Annotations
Applications programming
Artificial neural networks
Bioinformatics
Biological effects
Biomedical and Life Sciences
Cell cycle
Cell Nucleus
Classification
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer applications
Convolutional networks
Datasets
Deep Learning
Drug screening
Experts
Fluorescence
Genotype & phenotype
Humans
Image analysis
Image detection
Image processing
Image Processing, Computer-Assisted - methods
Keras
Libraries
Life Sciences
Machine learning
Machine Learning and Artificial Intelligence in Bioinformatics
Malaria
Microarrays
Neural networks
Object detection
Object recognition
Parasites
Phenotypes
Plasmodium vivax - growth & development
Software
Vector-borne diseases
Deep learning
Keras
Malaria
Convolutional networks
Object detection
ISSN:1471-2105, 1471-2105
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-020-03635-x