Nuclei Segmentation in Colon Histology Images by Using the Deep CNNs: A U-Net Based Multi-class Segmentation Analysis - Yildiz, Serdar; Memis, Abbas; Varli, Songul (IEEE, 2022)
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.
Keywords: nuclei segmentation; colon histology images; deep learning; U-Net; CoNIC challenge dataset; Cell Biology; Engineering