BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866909123106308096 |
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| author | Chen, Zhanghao Sun, Yifei Qin, Wenjian Ge, Ruiquan Pan, Cheng Deng, Wenming Liu, Zhou Min, Wenwen Elazab, Ahmed Wan, Xiang Wang, Changmiao |
| author_facet | Chen, Zhanghao Sun, Yifei Qin, Wenjian Ge, Ruiquan Pan, Cheng Deng, Wenming Liu, Zhou Min, Wenwen Elazab, Ahmed Wan, Xiang Wang, Changmiao |
| contents | Chest X-rays (CXRs) are commonly utilized as a low-dose modality for lung screening. Nonetheless, the efficacy of CXRs is somewhat impeded, given that approximately 75% of the lung area overlaps with bone, which in turn hampers the detection and diagnosis of diseases. As a remedial measure, bone suppression techniques have been introduced. The current dual-energy subtraction imaging technique in the clinic requires costly equipment and subjects being exposed to high radiation. To circumvent these issues, deep learning-based image generation algorithms have been proposed. However, existing methods fall short in terms of producing high-quality images and capturing texture details, particularly with pulmonary vessels. To address these issues, this paper proposes a new bone suppression framework, termed BS-Diff, that comprises a conditional diffusion model equipped with a U-Net architecture and a simple enhancement module to incorporate an autoencoder. Our proposed network cannot only generate soft tissue images with a high bone suppression rate but also possesses the capability to capture fine image details. Additionally, we compiled the largest dataset since 2010, including data from 120 patients with high-definition, high-resolution paired CXRs and soft tissue images collected by our affiliated hospital. Extensive experiments, comparative analyses, ablation studies, and clinical evaluations indicate that the proposed BS-Diff outperforms several bone-suppression models across multiple metrics. Our code can be accessed at https://github.com/Benny0323/BS-Diff. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_15328 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images Chen, Zhanghao Sun, Yifei Qin, Wenjian Ge, Ruiquan Pan, Cheng Deng, Wenming Liu, Zhou Min, Wenwen Elazab, Ahmed Wan, Xiang Wang, Changmiao Image and Video Processing Computer Vision and Pattern Recognition Chest X-rays (CXRs) are commonly utilized as a low-dose modality for lung screening. Nonetheless, the efficacy of CXRs is somewhat impeded, given that approximately 75% of the lung area overlaps with bone, which in turn hampers the detection and diagnosis of diseases. As a remedial measure, bone suppression techniques have been introduced. The current dual-energy subtraction imaging technique in the clinic requires costly equipment and subjects being exposed to high radiation. To circumvent these issues, deep learning-based image generation algorithms have been proposed. However, existing methods fall short in terms of producing high-quality images and capturing texture details, particularly with pulmonary vessels. To address these issues, this paper proposes a new bone suppression framework, termed BS-Diff, that comprises a conditional diffusion model equipped with a U-Net architecture and a simple enhancement module to incorporate an autoencoder. Our proposed network cannot only generate soft tissue images with a high bone suppression rate but also possesses the capability to capture fine image details. Additionally, we compiled the largest dataset since 2010, including data from 120 patients with high-definition, high-resolution paired CXRs and soft tissue images collected by our affiliated hospital. Extensive experiments, comparative analyses, ablation studies, and clinical evaluations indicate that the proposed BS-Diff outperforms several bone-suppression models across multiple metrics. Our code can be accessed at https://github.com/Benny0323/BS-Diff. |
| title | BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.15328 |