摘要
病理组织图像中细胞核的分割与检测是对细胞核形态进行定性定量分析的基础。近年来,深度学习在计算机视觉领域,尤其是医学图像分析方面,展现出了强大的性能。同时,定性定量地分析病理图像中细胞核的结构特征和形态变化在理解人类疾病以及开发安全有效的治疗方法方面扮演着关键角色。本文回顾了数字病理图像中深度学习方法的应用,并重点介绍了在细胞核检测和分割领域中的深度学习方法。该领域面临的主要挑战包括实现准确且可重复的细胞检测,以及从病理图像中分割出细胞核实例最后,我们讨论了深度学习在细胞检测和分割中的潜在应用和发展趋势。
The segmentation and detection of cell nuclei in histopathological images form the basis for qualitative and quantitative analysis of nuclear morphology.In recent years,deep learning has demon-strated powerful performance in the field of computer vision,particularly in the analysis of medical images.Additionally,qualitative and quantitative analysis of the structural features and morphological changes of cell nuclei in pathological images plays a crucial role in understanding human diseases and developing safe and effective treatment methods.This paper reviews the application of deep learning methods in digital pathology images,with a focus on deep learning methods in the field of cell nucleus detection and segmentation.The main challenges in this field include achieving accurate and reproducible cell detection and accurately segmenting cell nuclei instances from pathological images.Finally,we discuss the potential improvements and future development trends of deep learning in cell detection and segmentation.
作者
张恒超
郑迪乙
杨洪钦
陈骐
Hengchao Zhang;Diyi Zheng;Hongqin Yang;Qi Chen(Biomedical Research Center of South China,Fujian Normal University,Fuzhou 350117,China;Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education,Fujian Normal University,Fuzhou 350117,China)
出处
《福光技术》
2023年第2期30-36,共7页
FUJIAN OPTICAL TECHNOLOGY
关键词
数字病理图像
深度学习
细胞核检测与分割
Digital Pathology Images
Deep Learning
Nucleus Detection and Segmentation
