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NuSeT: A Deep Learning Tool for Reliably Separating and Analyzing Crowded Cells

Linfeng Yang, Rajarshi. P. Ghosh, J. Matthew Franklin, Chenyu You, Jan T. Liphardt
doi: https://doi.org/10.1101/749754
Linfeng Yang
1Bioengineering, Stanford University, Stanford, CA 94305, USA
2BioX Institute, Stanford University, Stanford, CA 94305, USA
3ChEM-H, Stanford University, Stanford, CA 94305, USA
4Cell Biology Division, Stanford Cancer Institute, Stanford, CA 94305, USA
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Rajarshi. P. Ghosh
1Bioengineering, Stanford University, Stanford, CA 94305, USA
2BioX Institute, Stanford University, Stanford, CA 94305, USA
3ChEM-H, Stanford University, Stanford, CA 94305, USA
4Cell Biology Division, Stanford Cancer Institute, Stanford, CA 94305, USA
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  • For correspondence: rajarshi@stanford.edu jan.liphardt@stanford.edu
J. Matthew Franklin
1Bioengineering, Stanford University, Stanford, CA 94305, USA
2BioX Institute, Stanford University, Stanford, CA 94305, USA
3ChEM-H, Stanford University, Stanford, CA 94305, USA
4Cell Biology Division, Stanford Cancer Institute, Stanford, CA 94305, USA
5Chemical Engineering, Stanford University, Stanford, CA 94305, USA
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Chenyu You
6Electrical Engineering, Stanford University, Stanford, CA 94305, USA
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Jan T. Liphardt
1Bioengineering, Stanford University, Stanford, CA 94305, USA
2BioX Institute, Stanford University, Stanford, CA 94305, USA
3ChEM-H, Stanford University, Stanford, CA 94305, USA
4Cell Biology Division, Stanford Cancer Institute, Stanford, CA 94305, USA
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  • For correspondence: rajarshi@stanford.edu jan.liphardt@stanford.edu
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Abstract

Segmenting cell nuclei within microscopy images is a ubiquitous task in biological research and clinical applications. Unfortunately, segmenting low-contrast overlapping objects that may be tightly packed is a major bottleneck in standard deep learning-based models. We report a Nuclear Segmentation Tool (NuSeT) based on deep learning that accurately segments nuclei across multiple types of fluorescence imaging data. Using a hybrid network consisting of U-Net and Region Proposal Networks (RPN), followed by a watershed step, we have achieved superior performance in detecting and delineating nuclear boundaries in 2D and 3D images of varying complexities. By using foreground normalization and additional training on synthetic images containing non-cellular artifacts, NuSeT improves nuclear detection and reduces false positives. NuSeT addresses common challenges in nuclear segmentation such as variability in nuclear signal and shape, limited training sample size, and sample preparation artifacts. Compared to other segmentation models, NuSeT consistently fares better in generating accurate segmentation masks and assigning boundaries for touching nuclei.

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Posted August 28, 2019.
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NuSeT: A Deep Learning Tool for Reliably Separating and Analyzing Crowded Cells
Linfeng Yang, Rajarshi. P. Ghosh, J. Matthew Franklin, Chenyu You, Jan T. Liphardt
bioRxiv 749754; doi: https://doi.org/10.1101/749754
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NuSeT: A Deep Learning Tool for Reliably Separating and Analyzing Crowded Cells
Linfeng Yang, Rajarshi. P. Ghosh, J. Matthew Franklin, Chenyu You, Jan T. Liphardt
bioRxiv 749754; doi: https://doi.org/10.1101/749754

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