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|a 10.1002/ima.22527
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|a eng
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| 100 |
1 |
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|a Zhou, Tongxue
|e verfasserin
|4 aut
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|a Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism
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|c 2021
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|a Text
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|a ƒaComputermedien
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|a Date Revised 05.06.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2020 Wiley Periodicals LLC.
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|a The coronavirus disease (COVID-19) pandemic has led to a devastating effect on the global public health. Computed Tomography (CT) is an effective tool in the screening of COVID-19. It is of great importance to rapidly and accurately segment COVID-19 from CT to help diagnostic and patient monitoring. In this paper, we propose a U-Net based segmentation network using attention mechanism. As not all the features extracted from the encoders are useful for segmentation, we propose to incorporate an attention mechanism including a spatial attention module and a channel attention module, to a U-Net architecture to re-weight the feature representation spatially and channel-wise to capture rich contextual relationships for better feature representation. In addition, the focal Tversky loss is introduced to deal with small lesion segmentation. The experiment results, evaluated on a COVID-19 CT segmentation dataset where 473 CT slices are available, demonstrate the proposed method can achieve an accurate and rapid segmentation result on COVID-19. The method takes only 0.29 second to segment a single CT slice. The obtained Dice Score and Hausdorff Distance are 83.1% and 18.8, respectively
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|a Journal Article
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|a COVID‐19
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| 650 |
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|a CT
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| 650 |
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|a attention mechanism
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| 650 |
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|a deep learning
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| 650 |
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|a focal tversky loss
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| 650 |
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4 |
|a segmentation
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| 700 |
1 |
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|a Canu, Stéphane
|e verfasserin
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1 |
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|a Ruan, Su
|e verfasserin
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| 773 |
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|i Enthalten in
|t International journal of imaging systems and technology
|d 1998
|g 31(2021), 1 vom: 24. März, Seite 16-27
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|g volume:31
|g year:2021
|g number:1
|g day:24
|g month:03
|g pages:16-27
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|u http://dx.doi.org/10.1002/ima.22527
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