Nucleinet: A Convolutional Encoder-Decoder Network For Bio-Image Denoising
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)(2017)
摘要
Generic and scalable data analysis procedures are highly demanded by the increasing number of multi-dimensional biomedical data. However, especially for time-lapse biological data, the high level of noise prevents for automated high-throughput analysis methods. The rapid developing of machine-learning methods and particularly deep-learning methods provide new tools and methodologies that can help in the denoising of such data. Using a convolutional encoder-decoder network, one can provide a scalable bio-image platform, called NucleiNet, to automatically segment, classify and track cell nuclei. The proposed method can achieve 0.99 F-score and 0.99 pixel-wise accuracy on C. elegans dataset, which means that over 99% of nuclei can be successfully detected with no merging nuclei found.
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关键词
Animals,Caenorhabditis elegans,Cell Nucleus,Machine Learning
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