Document Type
Article
Publication Date
6-3-2021
Department
College of Computing
Abstract
U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.
Publication Title
IEEE Access
Recommended Citation
Siddique, N.,
Sidike, P.,
Elkin, C.,
&
Devabhaktuni, V.
(2021).
U-net and its variants for medical image segmentation: A review of theory and applications.
IEEE Access,
9, 82031-82057.
http://doi.org/10.1109/ACCESS.2021.3086020
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15040
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
©2021. Publisher’s version of record: https://doi.org/10.1109/ACCESS.2021.3086020