U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

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 o...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.82031-82057
Main Authors: Siddique, Nahian, Paheding, Sidike, Elkin, Colin P., Devabhaktuni, Vijay
Format: Article
Language:English
Subjects:
Biomedical imaging
Computed tomography
Computer architecture
Convolution
Deep learning
Image enhancement
Image segmentation
Literature reviews
Logic gates
Magnetic resonance imaging
Medical imaging
neural network architecture
segmentation
Three-dimensional displays
U-net
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Summary: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 X-rays 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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3086020