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.

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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

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