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.

IEEE_CIBCB_2022_Final_Submission.pdf - Accepted Version
All rights reserved.

Download (4MB) | Preview
Official URL:
Link to published version::


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:
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 26 Jul 2022 15:43
Last Modified: 12 Oct 2023 10:32

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics