Accurate nuclei segmentation in breast cancer tumour biopsies

SHENFIELD, Alex, KASTURI, Surya and TRAN, William (2022). Accurate nuclei segmentation in breast cancer tumour biopsies. In: 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE.

[img]
Preview
PDF
IEEE_CIBCB_2022_Final_Submission.pdf - Accepted Version
All rights reserved.

Download (4MB) | Preview
Official URL: https://ieeexplore.ieee.org/document/9863023
Link to published version:: https://doi.org/10.1109/CIBCB55180.2022.9863023
Related URLs:

    Abstract

    Breast cancer identification is a arduous process and diagnosing it using Haematoxylin and Eosin (H&E) stained pathology images is a significant challenge, with pathologists struggling to segment cancer nuclei accurately. This study will evaluate the efficacy of different methods utilising deep learning techniques for breast cancer nuclei segmentation, with a particular emphasis on U-Net architectures. The proposed methodology is divided into four stages: image enhancement, individual nuclei segmentation, feature extraction, and whole image binary segmentation. This work then conducts a rigorous comparison of different segmentation techniques.

    Item Type: Book Section
    Identification Number: https://doi.org/10.1109/CIBCB55180.2022.9863023
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 26 Jul 2022 15:43
    Last Modified: 31 Aug 2022 10:37
    URI: https://shura.shu.ac.uk/id/eprint/30487

    Actions (login required)

    View Item View Item

    Downloads

    Downloads per month over past year

    View more statistics