02136nam a2200241Ia 4500 MX-MdCICY 20260521092144.0 CICY B-21804 A state-of-the-art survey of U-Net in microscopic image analysis: from simple usage to structure mortification Neural Computing and Applications, 36(7), p.3317-3346, 2024 Artículo 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. MICROSCOPIC IMAGE ANALYSIS U-NET IMAGE SEGMENTATION DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK Wu, J., Liu;W., Li, C.;Jiang, T.;Shariful, I. M.;Yao, Y.;Grzegorzek, M.  https://drive.google.com/file/d/1TNJkjAJM_UzNsO7arNF8pVYmkbH-3M6i/view?usp=drive_link Para ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx Loc REF1 251009s9999 xx 000 0 und d 37825 37825 0 0 Loc 0 0 F1 CICY CICY RE 2025-10-09 0 B-21804 2025-10-09 16:07:06 2025-10-09 REF1