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

By Nahian Alam Siddique, Sidike Paheding, Colin P. Elkin, V. Devabhaktuni. IEEE Access, 2020

1,569 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/840540040e46ab6fd30e93e21aa756cacedd4662

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Citations in the corpus

Channel prior convolutional attention for medical image segmentation (2023, methodology): “CNN-based methods, such as U-Net (Ronneberger et al., 2015), have gained widespread popularity in medical image segmentation (Siddique et al., 2021).”
Medical Image Segmentation Review: The Success of U-Net (2022, methodology): “Due to the modular design of U-Net, we proposed our taxonomy to cope with the inheritance design of U-net rather than the conceptual taxonomies offered in [33].”
CellViT: Vision Transformers for Precise Cell Segmentation and Classification (2023, background): “…invention of the U-Net architecture by Ronneberger et al. [33] has significantly impacted medical image analysis by enabling accurate and e ffi cient segmentation of complex structures, contributing to advancements in various medical domains such as radiology [34, 35] and digital pathology [36].”