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NucleiNet: A convolutional encoder-decoder network for bio-image denoising | IEEE Conference Publication | IEEE Xplore

NucleiNet: A convolutional encoder-decoder network for bio-image denoising


Abstract:

Generic and scalable data analysis procedures are highly demanded by the increasing number of multi-dimensional biomedical data. However, especially for time-lapse biolog...Show More

Abstract:

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.
Date of Conference: 11-15 July 2017
Date Added to IEEE Xplore: 14 September 2017
ISBN Information:

ISSN Information:

PubMed ID: 29060284
Conference Location: Jeju, Korea (South)

References

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