Research on U-Net medical image segmentation

Fangfang Song; Yimin Tian; Xue Gao; Shuai Yang; Meijun Zheng

doi:10.1117/12.2631325

18 March 2022 Research on U-Net medical image segmentation

Fangfang Song, Yimin Tian, Xue Gao, Shuai Yang, Meijun Zheng

Proceedings Volume 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021); 121680K (2022) https://doi.org/10.1117/12.2631325
Event: International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 2021, Harbin, China

Abstract

With the rapid development of deep learning technology and medical technology, neural networks are widely used in the field of medical image segmentation. Among them, U-Net neural network has gradually become a research hotspot in the field of image segmentation because of its good segmentation performance. It provides doctors with a consistent method of quantifying lesions and is widely used in the field of medical image semantic segmentation. This article studies the U-Net network, learns theoretically from the U-Net network model and its basic principles, and then conducts experiments on three typical medical images of liver medical images, fundus blood vessel images, and lung nodule images to explain various types of medical images. The characteristics of the image and the difficulty of segmentation, and the performance of the U-Net network in the relevant image segmentation is verified. Finally, the problems existing in U-Net network are discussed, and the future development is prospected.

Citation Download Citation

Fangfang Song, Yimin Tian, Xue Gao, Shuai Yang, and Meijun Zheng "Research on U-Net medical image segmentation", Proc. SPIE 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 121680K (18 March 2022); https://doi.org/10.1117/12.2631325

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KEYWORDS

Image segmentation

Medical imaging

Blood vessels

Liver

Lung

Convolution

Computer programming

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