Deep Learning-Based Approaches for Nucleus Segmentation 


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


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  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.

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[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)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880