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深度学习在肾脏免疫荧光图像中的应用进展
Application progress on deep learning in renal immunofluorescence image

广西医学 页码:210-216

作者机构:孙锦洲,在读本科生,研究方向为医学信息工程。

基金信息:浙江省教育厅一般科研项目(Y202351408)

DOI:10.11675/j.issn.0253⁃4304.2024.02.06

  • 中文简介
  • 英文简介
  • 参考文献

慢性肾脏病等肾脏疾病不同程度地危害着全球人类的健康,加重社会的经济负担。因此,许多针对肾脏疾病的诊断技术应运而生,包括肾脏免疫荧光图像诊断。目前,深度学习被广泛应用于各种医学图像处理任务,特别是在肾脏免疫荧光图像处理中已经取得了不错的应用效果。本文基于深度学习在肾脏免疫荧光图像上的应用进行概述,探讨深度学习在肾小球目标检测、目标分割、形态分类中的应用进展,旨在挖掘深度学习在肾脏疾病研究中的应用潜力,为肾脏疾病诊断技术的发展提供新思路。

Kidney diseases such as chronic kidney disease endanger human health globally and increase the economic burden of society to varying degrees. Therefore, many diagnostic techniques for kidney diseases have emerged, including renal immunofluorescence image diagnosis. Currently, deep learning is widely applied to various medical image processing tasks, especially in renal immunofluorescence image processing, which has achieved favorable application effects. In this paper, the application of deep learning to renal immunofluorescence image is reviewed, and the application progress on deep learning in glomerular target detection, target segmentation, and morphological classification is explored, aiming at exploring application potentiality of deep learning in kidney disease research, and providing new ideas for the development of diagnostic techniques of kidney diseases. 

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