U-Net-Based Medical Image Segmentation

Overview

Journal J Healthc Eng

Specialty Biomedical Engineering

Date 2022 Apr 25

PMID 35463660

Authors

Xiao-Xia Yin

Le Sun

Yuhan Fu

Ruiliang Lu

Yanchun Zhang

Affiliations

Soon will be listed here.

Abstract

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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