Nuclei segmentation in colon histology images by using the deep CNNs: A U-Net based multi-class segmentation analysis

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dc.authorid0000-0003-2645-8071en_US
dc.authorscopusid55807729600en_US
dc.authorwosidDGZ-5701-2022en_US
dc.contributor.authorYıldız, Serdar
dc.contributor.authorMemiş, Abbas
dc.contributor.authorVarlı, Songül
dc.date.accessioned2023-01-03T07:35:33Z
dc.date.available2023-01-03T07:35:33Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractAs is known, pathologists visually examine the tissue distributions by using microscopes traditionally. The rise in digital image processing and machine learning also allows high-performance computerized analysis of histology images taken with modern imaging systems. In general, histological image segmentation is the first step in the quantitative analysis of histology images. Therefore, a high-accuracy segmentation is essential for histology image analysis in most cases. In this paper, we performed a deep Convolutional Neural Networks (CNNs) based nuclei segmentation study on colon histology images. By using the U-Net biomedical image segmentation model, it is aimed to classify each pixel in colon histology images into one of the following 6 types of nucleus: epithelial, lymphocyte, plasma, eosinophil, neutrophil, connective tissue or classify it as the image background. In comprehensive experimental tests performed on Colon Nuclei Identification and Counting (CoNIC) Challenge dataset, commonly used segmentation and classification metrics were measured, and promising segmentation performances were achieved.en_US
dc.identifier.citationYıldız, S., Memiş, A. & Varlı, S. (2022). Nuclei segmentation in colon histology images by using the deep CNNs: A U-Net based multi-class segmentation analysis. TIPTEKNO 2022 - Medical Technologies Congress. https://doi.org/10.1109/TIPTEKNO56568.2022.9960188en_US
dc.identifier.doi10.1109/TIPTEKNO56568.2022.9960188en_US
dc.identifier.scopus2-s2.0-85144079356en_US
dc.identifier.urihttps://doi.org/10.1109/TIPTEKNO56568.2022.9960188
dc.identifier.urihttps://hdl.handle.net/20.500.13055/360
dc.identifier.wosWOS:000903709700043en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorMemiş, Abbas
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofTIPTEKNO 2022 - Medical Technologies Congressen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectColon Histology Imagesen_US
dc.subjectConic Challenge Dataseten_US
dc.subjectDeep Learningen_US
dc.subjectNuclei Segmentationen_US
dc.subjectU-Neten_US
dc.titleNuclei segmentation in colon histology images by using the deep CNNs: A U-Net based multi-class segmentation analysisen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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