Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images

@article{Ho2019CenterExtractionBasedTD,
  title={Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images},
  author={David Joon Ho and Shuo Han and Chichen Fu and Paul Salama and Kenneth W. Dunn and Edward J. Delp},
  journal={2019 IEEE EMBS International Conference on Biomedical \& Health Informatics (BHI)},
  year={2019},
  pages={1-4}
}
  • D. J. Ho, Shuo Han, +3 authors E. Delp
  • Published 2019
  • Engineering, Computer Science
  • 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue. An important step in the characterization of tissue involves nuclei segmentation. In this paper, a two-stage method for segmentation of nuclei using convolutional neural networks (CNNs) is described. In particular, since creating labeled volumes manually for training purposes is not practical due to the size and complexity of the 3D data sets, the paper describes a method for generating… Expand
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