Nuclei segmentation in colon histology images by using the deep CNNs: A U-Net based multi-class segmentation analysis
Yükleniyor...
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
As 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.
Açıklama
Anahtar Kelimeler
Colon Histology Images, Conic Challenge Dataset, Deep Learning, Nuclei Segmentation, U-Net
Kaynak
TIPTEKNO 2022 - Medical Technologies Congress
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Yıldız, 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/TIPTEKNO56568.2022.9960188
