BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images

• URL: http://arxiv.org/abs/2311.15328v3
• Date: Thu, 29 Feb 2024 01:49:26 GMT
• Title: BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images
• Authors: Zhanghao Chen, Yifei Sun, Wenjian Qin, Ruiquan Ge, Cheng Pan, Wenming Deng, Zhou Liu, Wenwen Min, Ahmed Elazab, Xiang Wan, Changmiao Wang
• Abstract summary: Chest X-rays (CXRs) are commonly utilized as a low-dose modality for lung screening. Approximately 75% of the lung area overlaps with bone, which in turn hampers the detection and diagnosis of diseases. Bone suppression techniques have been introduced, but the current dual-energy subtraction imaging technique in the clinic requires costly equipment and subjects being exposed to high radiation. 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.
• Score: 21.19843479423806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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