Honeycomb lung segmentation network based on P2T with CNN two-branch parallelism
Aiming at the problem that honeycomb lung lesions are difficult to accurately segment due to diverse morphology and complex distribution, a network with parallel two-branch structure is proposed. In the encoder, the Pyramid Pooling Transformer (P2T) backbone is used as the Transformer branch to obta...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-12-01
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| Series: | Intelligent and Converged Networks |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.23919/ICN.2024.0023 |
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| _version_ | 1850091377492230144 |
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| author | Zhichao Li Gang Li Ling Zhang Guijuan Cheng Shan Wu |
| author_facet | Zhichao Li Gang Li Ling Zhang Guijuan Cheng Shan Wu |
| author_sort | Zhichao Li |
| collection | DOAJ |
| description | Aiming at the problem that honeycomb lung lesions are difficult to accurately segment due to diverse morphology and complex distribution, a network with parallel two-branch structure is proposed. In the encoder, the Pyramid Pooling Transformer (P2T) backbone is used as the Transformer branch to obtain the global features of the lesions, the convolutional branch is used to extract the lesions’ local feature information, and the feature fusion module is designed to effectively fuse the features in the dual branches; subsequently, in the decoder, the channel prior convolutional attention is used to enhance the localization ability of the model to the lesion region. To resolve the problem of model accuracy degradation caused by the class imbalance of the dataset, an adaptive weighted hybrid loss function is designed for model training. Finally, extensive experimental results show that the method in this paper performs well on the Honeycomb Lung Dataset, with Intersection over Union (IoU), mean Intersection over Union (mIoU), Dice coefficient, and Precision (Pre) of 0.8750, 0.9363, 0.9298, and 0.9012, respectively, which are better than other methods. In addition, its IoU and Dice coefficient of 0.7941 and 0.8875 on the Covid dataset further prove its excellent performance. |
| format | Article |
| id | doaj-art-79d1e8134ce84863bced9072ddab89d0 |
| institution | DOAJ |
| issn | 2708-6240 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Intelligent and Converged Networks |
| spelling | doaj-art-79d1e8134ce84863bced9072ddab89d02025-08-20T02:42:24ZengTsinghua University PressIntelligent and Converged Networks2708-62402024-12-015433635510.23919/ICN.2024.0023Honeycomb lung segmentation network based on P2T with CNN two-branch parallelismZhichao Li0Gang Li1Ling Zhang2Guijuan Cheng3Shan Wu4College of Software, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Software, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Software, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Software, Taiyuan University of Technology, Taiyuan 030024, ChinaCT Div, Shanxi Bethune Hospital, Taiyuan 030024, ChinaAiming at the problem that honeycomb lung lesions are difficult to accurately segment due to diverse morphology and complex distribution, a network with parallel two-branch structure is proposed. In the encoder, the Pyramid Pooling Transformer (P2T) backbone is used as the Transformer branch to obtain the global features of the lesions, the convolutional branch is used to extract the lesions’ local feature information, and the feature fusion module is designed to effectively fuse the features in the dual branches; subsequently, in the decoder, the channel prior convolutional attention is used to enhance the localization ability of the model to the lesion region. To resolve the problem of model accuracy degradation caused by the class imbalance of the dataset, an adaptive weighted hybrid loss function is designed for model training. Finally, extensive experimental results show that the method in this paper performs well on the Honeycomb Lung Dataset, with Intersection over Union (IoU), mean Intersection over Union (mIoU), Dice coefficient, and Precision (Pre) of 0.8750, 0.9363, 0.9298, and 0.9012, respectively, which are better than other methods. In addition, its IoU and Dice coefficient of 0.7941 and 0.8875 on the Covid dataset further prove its excellent performance.https://www.sciopen.com/article/10.23919/ICN.2024.0023honeycomb lung segmentationparallel two-branch architectureglobal-local feature integrationchannel prior convolutional attention |
| spellingShingle | Zhichao Li Gang Li Ling Zhang Guijuan Cheng Shan Wu Honeycomb lung segmentation network based on P2T with CNN two-branch parallelism Intelligent and Converged Networks honeycomb lung segmentation parallel two-branch architecture global-local feature integration channel prior convolutional attention |
| title | Honeycomb lung segmentation network based on P2T with CNN two-branch parallelism |
| title_full | Honeycomb lung segmentation network based on P2T with CNN two-branch parallelism |
| title_fullStr | Honeycomb lung segmentation network based on P2T with CNN two-branch parallelism |
| title_full_unstemmed | Honeycomb lung segmentation network based on P2T with CNN two-branch parallelism |
| title_short | Honeycomb lung segmentation network based on P2T with CNN two-branch parallelism |
| title_sort | honeycomb lung segmentation network based on p2t with cnn two branch parallelism |
| topic | honeycomb lung segmentation parallel two-branch architecture global-local feature integration channel prior convolutional attention |
| url | https://www.sciopen.com/article/10.23919/ICN.2024.0023 |
| work_keys_str_mv | AT zhichaoli honeycomblungsegmentationnetworkbasedonp2twithcnntwobranchparallelism AT gangli honeycomblungsegmentationnetworkbasedonp2twithcnntwobranchparallelism AT lingzhang honeycomblungsegmentationnetworkbasedonp2twithcnntwobranchparallelism AT guijuancheng honeycomblungsegmentationnetworkbasedonp2twithcnntwobranchparallelism AT shanwu honeycomblungsegmentationnetworkbasedonp2twithcnntwobranchparallelism |