Optimization of U-shaped pure transformer medical image segmentation network
In recent years, neural networks have made pioneering achievements in the field of medical imaging. In particular, deep neural networks based on U-shaped structures are widely used in different medical image segmentation tasks. In order to improve the early diagnosis and clinical decision-making sys...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2023-08-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1515.pdf |
| _version_ | 1797740612179460096 |
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| author | Yongping Dan Weishou Jin Zhida Wang Changhao Sun |
| author_facet | Yongping Dan Weishou Jin Zhida Wang Changhao Sun |
| author_sort | Yongping Dan |
| collection | DOAJ |
| description | In recent years, neural networks have made pioneering achievements in the field of medical imaging. In particular, deep neural networks based on U-shaped structures are widely used in different medical image segmentation tasks. In order to improve the early diagnosis and clinical decision-making system of lung diseases, it has become a key step to use the neural network for lung segmentation to assist in positioning and observing the shape. There is still the problem of low precision. For the sake of achieving better segmentation accuracy, an optimized pure Transformer U-shaped segmentation is proposed in this article. The optimization segmentation network adopts the method of adding skip connections and performing special splicing processing, which reduces the information loss in the encoding process and increases the information in the decoding process, so as to achieve the purpose of improving the segmentation accuracy. The final experiment shows that our improved network achieves 97.86% accuracy in segmentation of the “Chest Xray Masks and Labels” dataset, which is better than the full convolutional network or the combination of Transformer and convolution. |
| first_indexed | 2024-03-12T14:14:53Z |
| format | Article |
| id | doaj.art-c9d46b65f3c647a8be67f6acfef2448d |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-12T14:14:53Z |
| publishDate | 2023-08-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-c9d46b65f3c647a8be67f6acfef2448d2023-08-20T15:05:13ZengPeerJ Inc.PeerJ Computer Science2376-59922023-08-019e151510.7717/peerj-cs.1515Optimization of U-shaped pure transformer medical image segmentation networkYongping DanWeishou JinZhida WangChanghao SunIn recent years, neural networks have made pioneering achievements in the field of medical imaging. In particular, deep neural networks based on U-shaped structures are widely used in different medical image segmentation tasks. In order to improve the early diagnosis and clinical decision-making system of lung diseases, it has become a key step to use the neural network for lung segmentation to assist in positioning and observing the shape. There is still the problem of low precision. For the sake of achieving better segmentation accuracy, an optimized pure Transformer U-shaped segmentation is proposed in this article. The optimization segmentation network adopts the method of adding skip connections and performing special splicing processing, which reduces the information loss in the encoding process and increases the information in the decoding process, so as to achieve the purpose of improving the segmentation accuracy. The final experiment shows that our improved network achieves 97.86% accuracy in segmentation of the “Chest Xray Masks and Labels” dataset, which is better than the full convolutional network or the combination of Transformer and convolution.https://peerj.com/articles/cs-1515.pdfPure transformerU-shapedMedical image segmentationSpecial splicingChest X-ray |
| spellingShingle | Yongping Dan Weishou Jin Zhida Wang Changhao Sun Optimization of U-shaped pure transformer medical image segmentation network PeerJ Computer Science Pure transformer U-shaped Medical image segmentation Special splicing Chest X-ray |
| title | Optimization of U-shaped pure transformer medical image segmentation network |
| title_full | Optimization of U-shaped pure transformer medical image segmentation network |
| title_fullStr | Optimization of U-shaped pure transformer medical image segmentation network |
| title_full_unstemmed | Optimization of U-shaped pure transformer medical image segmentation network |
| title_short | Optimization of U-shaped pure transformer medical image segmentation network |
| title_sort | optimization of u shaped pure transformer medical image segmentation network |
| topic | Pure transformer U-shaped Medical image segmentation Special splicing Chest X-ray |
| url | https://peerj.com/articles/cs-1515.pdf |
| work_keys_str_mv | AT yongpingdan optimizationofushapedpuretransformermedicalimagesegmentationnetwork AT weishoujin optimizationofushapedpuretransformermedicalimagesegmentationnetwork AT zhidawang optimizationofushapedpuretransformermedicalimagesegmentationnetwork AT changhaosun optimizationofushapedpuretransformermedicalimagesegmentationnetwork |