Cells Detection and Segmentation in ER-IHC Stained Breast Histopathology Images


Jamaluddin, Mohammad F. and Ahmad Fauzi, Mohammad Faizal and Abas, Fazly Salleh and Lee, Jenny T. H. and See, Y. Khor and Keah, H. Teoh and Lam, M. Looi (2020) Cells Detection and Segmentation in ER-IHC Stained Breast Histopathology Images. In: 2020 IEEE REGION 10 CONFERENCE (TENCON), 16-19 Nov. 2020, Osaka, Japan.

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In this paper, we present our recent work on cells detection and segmentation in estrogen receptor immunohistochemistry (ER-IHC)-stained breast carcinoma images. The proposed cell detection and segmentation is very useful in the predictive scoring of hormone receptor status in ER-IHC stained whole-slide images, which helps pathologists to decide whether a patient should be offered hormonal therapy or other treatments. The proposed method is based on deep convolutional neural network, followed by watershed-based post-processing. The cell detection results are compared and evaluated objectively against the ground truth provided by our collaborating pathologists. The cell segmentation results, on the other hand, are evaluated visually by overlaying the computer segmented boundaries on the ER-IHC images for comparison. The automated cell detection algorithm recorded precision and recall rates of 95% and 91% respectively. The very promising performances for both the detection and segmentation paves the way for an automated system for hormone receptor scoring in ER-IHC stained whole-slide breast carcinoma images

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Image segmentation, Breast, Receptor (biochemistry), Biochemistry, Training, Histopathology, Convolutional neural networks
Subjects: R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 12 Oct 2021 05:20
Last Modified: 08 Dec 2022 09:15
URII: http://shdl.mmu.edu.my/id/eprint/8536


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