Application of semantic segmentation based on convolutional neural network in medical images

Yuchao WU; Lan LIN; Jingxuan WANG; Shuicai WU

doi:10.7507/1001-5515.201906067

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Mar 2020

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JOURNAL OF MECHANICAL ENGINEERING > 2020 > 37(3): 533-540.

Yuchao WU, Lan LIN, Jingxuan WANG, Shuicai WU. Application of semantic segmentation based on convolutional neural network in medical images[J]. JOURNAL OF MECHANICAL ENGINEERING, 2020, 37(3): 533-540. doi: 10.7507/1001-5515.201906067

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Yuchao WU, Lan LIN, Jingxuan WANG, Shuicai WU. Application of semantic segmentation based on convolutional neural network in medical images[J]. JOURNAL OF MECHANICAL ENGINEERING, 2020, 37(3): 533-540. doi: 10.7507/1001-5515.201906067

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Application of semantic segmentation based on convolutional neural network in medical images

doi: 10.7507/1001-5515.201906067

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Corresponding author: LIN lan, Email：lanlin@bjut.edu.cn
Received Date: 24 Jun 2019
Rev Recd Date: 29 Jan 2020
Publish Date: 17 Mar 2020

Abstract

Abstract

With the rapid development of network structure, convolutional neural networks (CNN) consolidated its position as a leading machine learning tool in the field of image analysis. Therefore, semantic segmentation based on CNN has also become a key high-level task in medical image understanding. This paper reviews the research progress on CNN-based semantic segmentation in the field of medical image. A variety of classical semantic segmentation methods are reviewed, whose contributions and significance are highlighted. On this basis, their applications in the segmentation of some major physiological and pathological anatomical structures are further summarized and discussed. Finally, the open challenges and potential development direction of semantic segmentation based on CNN in the area of medical image are discussed.
- convolutional neural network,
- semantic segmentation,
- medical application

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References(34)

References

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Application of semantic segmentation based on convolutional neural network in medical images

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