A state-of-the-art survey of U-Net in microscopic image analysis: from simple usage to structure mortification

Jian Wu, Wanli Liu, Chen Li, Tao Jiang, Islam Mohammad Shariful, Yudong Yao, Hongzan Sun, Xiaoqi Li, Xintong Li, Xinyu Huang, Marcin Grzegorzek

Research output: Contribution to journalReview articlepeer-review

7 Scopus citations

Abstract

Microscopic image analysis technology helps solve the inadvertences of artificial traditional methods in disease, wastewater treatment, and environmental change monitoring analysis. Convolutional neural network (CNN) play an important role in microscopic image analysis. Image segmentation, in which U-Net is increasingly applied in microscopic image segmentation, is a crucial step in detection, tracking, monitoring, feature extraction, modelling, and analysis. This paper comprehensively reviews the development history of U-Net, analyses several research results of various segmentation methods since the emergence of U-Net, and conducts a comprehensive review of related papers. This paper summarised the improved methods of U-Net and then listed the existing significance of image segmentation techniques and their improvements introduced over the years. Finally, focusing on the different improvement strategies of U-Net in different papers, the related work of each application target is reviewed according to detailed technical categories to facilitate future research. Researchers can see the dynamics of the transmission of technological development and keep up with future trends in this interdisciplinary field.

Original languageEnglish
Pages (from-to)3317-3346
Number of pages30
JournalNeural Computing and Applications
Volume36
Issue number7
DOIs
StatePublished - Mar 2024

Keywords

  • Convolutional neural network
  • Deep learning
  • Image segmentation
  • Microscopic image analysis
  • U-Net

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