TY - JOUR
T1 - A state-of-the-art survey of U-Net in microscopic image analysis
T2 - from simple usage to structure mortification
AU - Wu, Jian
AU - Liu, Wanli
AU - Li, Chen
AU - Jiang, Tao
AU - Shariful, Islam Mohammad
AU - Yao, Yudong
AU - Sun, Hongzan
AU - Li, Xiaoqi
AU - Li, Xintong
AU - Huang, Xinyu
AU - Grzegorzek, Marcin
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.
PY - 2024/3
Y1 - 2024/3
N2 - 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.
AB - 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.
KW - Convolutional neural network
KW - Deep learning
KW - Image segmentation
KW - Microscopic image analysis
KW - U-Net
UR - http://www.scopus.com/inward/record.url?scp=85179311539&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85179311539&partnerID=8YFLogxK
U2 - 10.1007/s00521-023-09284-4
DO - 10.1007/s00521-023-09284-4
M3 - Review article
AN - SCOPUS:85179311539
SN - 0941-0643
VL - 36
SP - 3317
EP - 3346
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 7
ER -