Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation
Abstract Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due to the wide variation in lung appearance caused by disease progression and differing X-ray settings. While deep learning models have shown remarkable success in segmenting lungs from CXR imag...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-79494-w |
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| author | Md. Shariful Alam Dadong Wang Yulia Arzhaeva Jesse Alexander Ende Joanna Kao Liz Silverstone Deborah Yates Olivier Salvado Arcot Sowmya |
| author_facet | Md. Shariful Alam Dadong Wang Yulia Arzhaeva Jesse Alexander Ende Joanna Kao Liz Silverstone Deborah Yates Olivier Salvado Arcot Sowmya |
| author_sort | Md. Shariful Alam |
| collection | DOAJ |
| description | Abstract Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due to the wide variation in lung appearance caused by disease progression and differing X-ray settings. While deep learning models have shown remarkable success in segmenting lungs from CXR images with normal or mildly abnormal findings, their performance declines when faced with complex structures, such as pulmonary opacifications. In this study, we propose AMRU++, an attention-based multi-residual UNet++ network designed for robust and accurate lung segmentation in CXR images with both normal and severe abnormalities. The model incorporates attention modules to capture relevant spatial information and multi-residual blocks to extract rich contextual and discriminative features of lung regions. To further enhance segmentation performance, we introduce a data augmentation technique that simulates the features and characteristics of CXR pathologies, addressing the issue of limited annotated data. Extensive experiments on public and private datasets comprising 350 cases of pneumoconiosis, COVID-19, and tuberculosis validate the effectiveness of our proposed framework and data augmentation technique. |
| format | Article |
| id | doaj-art-35d2b4d8068744e7b639bbf5d220194f |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-35d2b4d8068744e7b639bbf5d220194f2025-08-20T02:22:33ZengNature PortfolioScientific Reports2045-23222024-11-0114111110.1038/s41598-024-79494-wAttention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentationMd. Shariful Alam0Dadong Wang1Yulia Arzhaeva2Jesse Alexander Ende3Joanna Kao4Liz Silverstone5Deborah Yates6Olivier Salvado7Arcot Sowmya8School of Computer Science and Engineering, University of New South WalesCSIRO Data61CSIRO Data61Department of Radiology, St Vincent’s Hospital SydneyDepartment of Radiology, St Vincent’s Hospital SydneyDepartment of Radiology, St Vincent’s Hospital SydneyDepartment of Thoracic Medicine, St Vincent’s Hospital SydneySchool of Electrical Engineering & Robotics, Queensland University of TechnologySchool of Computer Science and Engineering, University of New South WalesAbstract Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due to the wide variation in lung appearance caused by disease progression and differing X-ray settings. While deep learning models have shown remarkable success in segmenting lungs from CXR images with normal or mildly abnormal findings, their performance declines when faced with complex structures, such as pulmonary opacifications. In this study, we propose AMRU++, an attention-based multi-residual UNet++ network designed for robust and accurate lung segmentation in CXR images with both normal and severe abnormalities. The model incorporates attention modules to capture relevant spatial information and multi-residual blocks to extract rich contextual and discriminative features of lung regions. To further enhance segmentation performance, we introduce a data augmentation technique that simulates the features and characteristics of CXR pathologies, addressing the issue of limited annotated data. Extensive experiments on public and private datasets comprising 350 cases of pneumoconiosis, COVID-19, and tuberculosis validate the effectiveness of our proposed framework and data augmentation technique.https://doi.org/10.1038/s41598-024-79494-w |
| spellingShingle | Md. Shariful Alam Dadong Wang Yulia Arzhaeva Jesse Alexander Ende Joanna Kao Liz Silverstone Deborah Yates Olivier Salvado Arcot Sowmya Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation Scientific Reports |
| title | Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation |
| title_full | Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation |
| title_fullStr | Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation |
| title_full_unstemmed | Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation |
| title_short | Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation |
| title_sort | attention based multi residual network for lung segmentation in diseased lungs with custom data augmentation |
| url | https://doi.org/10.1038/s41598-024-79494-w |
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