Deep Learning-Based Approaches for Nucleus SegmentationDeep Learning-Based Approaches for Nucleus Segmentation
- Other Titles
- Deep Learning-Based Approaches for Nucleus Segmentation
- Authors
- Duy Cuong Bui; 유명식
- Issue Date
- Apr-2024
- Publisher
- 한국통신학회
- Keywords
- Deep Learning; CNN; Nuclei Segmentation; Image Segmentation; U-Net
- Citation
- 한국통신학회논문지, v.49, no.4, pp 620 - 629
- Pages
- 10
- Journal Title
- 한국통신학회논문지
- Volume
- 49
- Number
- 4
- Start Page
- 620
- End Page
- 629
- URI
- https://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/49551
- DOI
- 10.7840/kics.2024.49.4.620
- ISSN
- 1226-4717
2287-3880
- 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.
- Files in This Item
-
Go to Link
- Appears in
Collections - ETC > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.