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

Siddique, Nahian ; Paheding, Sidike ; Elkin, Colin P. ; Devabhaktuni, Vijay

IEEE access, 2021, Vol.9, p.82031-82057 [Periódico revisado por pares]

Piscataway: IEEE

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Título:
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications
Autor: Siddique, Nahian ; Paheding, Sidike ; Elkin, Colin P. ; Devabhaktuni, Vijay
Assuntos: 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
É parte de: IEEE access, 2021, Vol.9, p.82031-82057
Descrição: 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.
Editor: Piscataway: IEEE
Idioma: Inglês

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

Siddique, Nahian ; Paheding, Sidike ; Elkin, Colin P. ; Devabhaktuni, Vijay

Piscataway: IEEE

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