000			02136nam a2200241Ia 4500
003			MX-MdCICY
005			20251009160706.0
040			_cCICY
090			_aB-21804
245	1	0	_aA state-of-the-art survey of U-Net in microscopic image analysis: from simple usage to structure mortification
490	0		_aNeural Computing and Applications, 36(7), p.3317-3346, 2024
500			_aArtículo
520	3		_aMicroscopic 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.
650	1	4	_aMICROSCOPIC IMAGE ANALYSIS
650	1	4	_aU-NET
650	1	4	_aIMAGE SEGMENTATION
650	1	4	_aDEEP LEARNING
650	1	4	_aCONVOLUTIONAL NEURAL NETWORK
700	1	2	_aWu, J., Liu;W., Li, C.;Jiang, T.;Shariful, I. M.;Yao, Y.;Grzegorzek, M.
856	4	0	_uhttps://drive.google.com/file/d/1TNJkjAJM_UzNsO7arNF8pVYmkbH-3M6i/view?usp=drive_link _zPara ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx
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