Accurate nuclei segmentation in breast cancer tumour biopsies

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.