U-Net-Based Medical Image Segmentation

Deep learning has been extensively applied to segmentation in medical imaging. U-Net proposed in 2015 shows the advantages of accurate segmentation of small targets and its scalable network architecture. With the increasing requirements for the performance of segmentation in medical imaging in recen...

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Bibliographic Details
Published inJournal of healthcare engineering Vol. 2022; pp. 1 - 16
Main Authors Yin, Xiao-Xia, Sun, Le, Fu, Yuhan, Lu, Ruiliang, Zhang, Yanchun
Format Journal Article
LanguageEnglish
Published England Hindawi 15.04.2022
Subjects
Diagnostic Imaging
Humans
Image Processing, Computer-Assisted - methods
Neural Networks, Computer
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Summary:Deep learning has been extensively applied to segmentation in medical imaging. U-Net proposed in 2015 shows the advantages of accurate segmentation of small targets and its scalable network architecture. With the increasing requirements for the performance of segmentation in medical imaging in recent years, U-Net has been cited academically more than 2500 times. Many scholars have been constantly developing the U-Net architecture. This paper summarizes the medical image segmentation technologies based on the U-Net structure variants concerning their structure, innovation, efficiency, etc.; reviews and categorizes the related methodology; and introduces the loss functions, evaluation parameters, and modules commonly applied to segmentation in medical imaging, which will provide a good reference for the future research.
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Academic Editor: Hangjun Che
ISSN:2040-2295
2040-2309
2040-2309
DOI:10.1155/2022/4189781