U-net and its variants for medical image segmentation: A review of theory and applications

Authors

Nahian Siddique, Purdue University Northwest
Paheding Sidike, Michigan Technological UniversityFollow
Colin P. Elkin, Purdue University Northwest
Vijay Devabhaktuni, Purdue University Northwest

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.

Publisher's Statement

©2021. Publisher’s version of record: https://doi.org/10.1109/ACCESS.2021.3086020

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