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 i...
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th-mahidol.576972020-08-25T17:19:04Z Keras R-CNN: library for cell detection in biological images using deep neural networks Jane Hung Allen Goodman Deepali Ravel Stefanie C.P. Lopes Gabriel W. Rangel Odailton A. Nery Benoit Malleret Francois Nosten Marcus V.G. Lacerda Marcelo U. Ferreira Laurent Rénia Manoj T. Duraisingh Fabio T.M. Costa Matthias Marti Anne E. Carpenter A-Star, Singapore Immunology Network Fundacao de Medicina Tropical do Amazonas Shoklo Malaria Research Unit Harvard T.H. Chan School of Public Health Universidade Estadual de Campinas Yong Loo Lin School of Medicine Fundacao Oswaldo Cruz Massachusetts Institute of Technology Nuffield Department of Medicine Universidade de Sao Paulo - USP College of Medical, Veterinary & Life Sciences Broad Institute Biochemistry, Genetics and Molecular Biology Computer Science Mathematics 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. 2020-08-25T09:02:45Z 2020-08-25T09:02:45Z 2020-07-11 Article BMC bioinformatics. Vol.21, No.1 (2020), 300 10.1186/s12859-020-03635-x 14712105 2-s2.0-85087872385 https://repository.li.mahidol.ac.th/handle/123456789/57697 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85087872385&origin=inward |
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Biochemistry, Genetics and Molecular Biology Computer Science Mathematics Jane Hung Allen Goodman Deepali Ravel Stefanie C.P. Lopes Gabriel W. Rangel Odailton A. Nery Benoit Malleret Francois Nosten Marcus V.G. Lacerda Marcelo U. Ferreira Laurent Rénia Manoj T. Duraisingh Fabio T.M. Costa Matthias Marti Anne E. Carpenter Keras R-CNN: library for cell detection in biological images using deep neural networks |
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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. |
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A-Star, Singapore Immunology Network |
author_facet |
A-Star, Singapore Immunology Network Jane Hung Allen Goodman Deepali Ravel Stefanie C.P. Lopes Gabriel W. Rangel Odailton A. Nery Benoit Malleret Francois Nosten Marcus V.G. Lacerda Marcelo U. Ferreira Laurent Rénia Manoj T. Duraisingh Fabio T.M. Costa Matthias Marti Anne E. Carpenter |
format |
Article |
author |
Jane Hung Allen Goodman Deepali Ravel Stefanie C.P. Lopes Gabriel W. Rangel Odailton A. Nery Benoit Malleret Francois Nosten Marcus V.G. Lacerda Marcelo U. Ferreira Laurent Rénia Manoj T. Duraisingh Fabio T.M. Costa Matthias Marti Anne E. Carpenter |
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Jane Hung |
title |
Keras R-CNN: library for cell detection in biological images using deep neural networks |
title_short |
Keras R-CNN: library for cell detection in biological images using deep neural networks |
title_full |
Keras R-CNN: library for cell detection in biological images using deep neural networks |
title_fullStr |
Keras R-CNN: library for cell detection in biological images using deep neural networks |
title_full_unstemmed |
Keras R-CNN: library for cell detection in biological images using deep neural networks |
title_sort |
keras r-cnn: library for cell detection in biological images using deep neural networks |
publishDate |
2020 |
url |
https://repository.li.mahidol.ac.th/handle/123456789/57697 |
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1763488450431942656 |