Medical Image Segmentation Review: The Success of U-Net

Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical im...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 46; H. 12; S. 10076 - 10095
Hauptverfasser:	Azad, Reza, Aghdam, Ehsan Khodapanah, Rauland, Amelie, Jia, Yiwei, Avval, Atlas Haddadi, Bozorgpour, Afshin, Karimijafarbigloo, Sanaz, Cohen, Joseph Paul, Adeli, Ehsan, Merhof, Dorit
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2024
Schlagworte:	Biomedical imaging Computer architecture Convolutional neural network deep learning Feature extraction Humans Image Interpretation, Computer-Assisted - methods Image Processing, Computer-Assisted - methods Image segmentation Machine Learning medical image segmentation Neural Networks, Computer Task analysis Taxonomy transformer Transformers U-Net
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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