Deep Learning-Based Approaches for Nucleus Segmentation 


Vol. 49,  No. 4, pp. 620-629, Apr.  2024
10.7840/kics.2024.49.4.620


PDF
  Abstract

The accurate identification of cell nuclei is a critical aspect of various analyses, given that human cells, numbering around 30 trillion, contain DNA as their genetic code. In this research paper, we provide a comprehensive overview of deep learning-based techniques for nucleus segmentation. We have replicated and assessed the state-of-the-art methods using datasets like FCN, SegNet, U-net, and DoubleU-net, with a focus on the Data Science Bowl 2018 dataset comprising 670 training data folders and 65 testing data folders. Our experimental findings reveal that DoubleU-Net surpasses U-Net and other baseline models, yielding more precise segmentation masks. This promising outcome suggests that DoubleU-Net could serve as a robust model for addressing various challenges in medical image segmentation.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Related Articles
  Cite this article

[IEEE Style]

D. C. Bui and M. Yoo, "Deep Learning-Based Approaches for Nucleus Segmentation," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 4, pp. 620-629, 2024. DOI: 10.7840/kics.2024.49.4.620.

[ACM Style]

Duy Cuong Bui and Myungsik Yoo. 2024. Deep Learning-Based Approaches for Nucleus Segmentation. The Journal of Korean Institute of Communications and Information Sciences, 49, 4, (2024), 620-629. DOI: 10.7840/kics.2024.49.4.620.

[KICS Style]

Duy Cuong Bui and Myungsik Yoo, "Deep Learning-Based Approaches for Nucleus Segmentation," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 4, pp. 620-629, 4. 2024. (https://doi.org/10.7840/kics.2024.49.4.620)
Vol. 49, No. 4 Index