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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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We created<jats:italic>Keras R-CNN<\/jats:italic>to bring leading computational research to the everyday practice of bioimage analysts.<jats:italic>Keras R-CNN<\/jats:italic>implements deep learning object detection techniques using Keras and Tensorflow (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/broadinstitute\/keras-rcnn\">https:\/\/github.com\/broadinstitute\/keras-rcnn<\/jats:ext-link>). We demonstrate the command line tool\u2019s 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p><jats:italic>Keras R-CNN<\/jats:italic>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.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-020-03635-x","type":"journal-article","created":{"date-parts":[[2020,7,11]],"date-time":"2020-07-11T04:52:57Z","timestamp":1594443177000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["Keras R-CNN: library for cell detection in biological images using deep neural networks"],"prefix":"10.1186","volume":"21","author":[{"given":"Jane","family":"Hung","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Allen","family":"Goodman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepali","family":"Ravel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefanie C. 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All patients signed an informed consent form, and no children were included in the study. Sample collection was approved by Funda\u00e7\u00e3o de Medicina Tropical Ethical Board under the number CAAE 0044.0.114.000\u201311.Samples from Acre, Brazil. Informed consent was obtained from all patients or, in case of children, from their parents\/guardians. Study protocols for parasite sample collection were approved by the Institutional Review Board of the Institute of Biomedical Sciences, University of S\u00e3o Paulo, Brazil (1169\/CEPSH, 2014).Samples from Thailand. All patients signed an informed consent form, and no children were included in the study. The clinical samples were collected and tested in accordance with protocols approved by The Center for Clinical Vaccinology and Tropical Medicine at University of Oxford (OXTREC 17\u201311). Five mL of whole blood were collected in lithium heparin collection tubes. Samples were cryopreserved in Glycerolyte 57 (Baxter) after leukocyte depletion using cellulose columns (Sigma cat #C6288) (Sriprawat K, et al. Effective and cheap removal of leukocytes and platelets from <i>Plasmodium vivax<\/i> infected blood. Malar J 8, 115; 2009).","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not Applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"300"}}