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<!DOCTYPE html>
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<title>BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models</title>
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<h1 class="title is-1 publication-title">BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models</h1>
<div class="is-size-5 publication-authors">
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<a href="https://diaoquesang.github.io/" target="_blank">Yifei Sun</a><sup>1</sup>,
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<a href="https://benny0323.github.io/bio/" target="_blank">Zhanghao Chen</a><sup>1</sup>,
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<a href="https://github.com/267588" target="_blank">Hao Zheng<sup>1</sup>,
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<a href="https://kns.cnki.net/kcms2/author/detail?v=l4Uq_U3qQ8NAz-p1GsOEs87-yehTmkuEXYDNgN2a_E6CzF42pXOpcHLpv7eNDkyDT3491KnPItE_13tVfQKHpTue-maMYd10_Iutmquo2rP0RaW93D5ce0IUmbGjhurz&uniplatform=NZKPT&language=CHS" target="_blank">Wenming Deng<sup>2</sup>,
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<a href="https://scholar.google.com/citations?user=yfS3HzYAAAAJ&hl" target="_blank">Jin Liu</a><sup>3</sup>,
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<a href="https://wenwenmin.github.io" target="_blank">Wenwen Min</a><sup>4</sup>,
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<a href="https://scholar.google.com/citations?user=C_k66KkAAAAJ&hl=en" target="_blank">Ahmed Elazab</a><sup>5</sup>,
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<a href="https://scholar.google.com/citations?user=e3_kWigAAAAJ&hl=en" target="_blank">Xiang Wan</a><sup>6</sup>,
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<a href="https://scholar.google.com/citations?user=47KhMXEAAAAJ&hl=en" target="_blank">Changmiao Wang</a><sup>6*</sup>,
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<a href="https://scholar.google.com/citations?user=OmSuBoYAAAAJ&hl=en" target="_blank">Ruiquan Ge</a><sup>1*</sup>
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<div class="author-affiliations">
<sup>1</sup> Hangzhou Dianzi University,
<sup>2</sup> National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital&Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College,
<sup>3</sup> Central South University,
<sup>4</sup> Yunnan University,
<sup>5</sup> Shenzhen University,
<sup>6</sup> Shenzhen Research Institute of Big Data
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<h2 class="title is-2">What Is Bone Suppression?</h2>
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<h2 class="title is-2">Abstract</h2>
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<p>
Lung diseases represent a significant global health challenge, with Chest X-Ray (CXR) being a key diagnostic tool due to their accessibility and affordability. Nonetheless, the detection of pulmonary lesions is often hindered by overlapping bone structures in CXR images, leading to potential misdiagnoses. To address this issue, we developed an end-to-end framework called BS-LDM, designed to effectively suppress bone in high-resolution CXR images. This framework is based on conditional latent diffusion models and incorporates a multi-level hybrid loss-constrained vector-quantized generative adversarial network which is crafted for perceptual compression, ensuring the preservation of details. To further enhance the framework's performance, we introduce offset noise and a temporal adaptive thresholding strategy. These additions help minimize discrepancies in generating low-frequency information, thereby improving the clarity of the generated soft tissue images. Additionally, we have compiled a high-quality bone suppression dataset named SZCH-X-Rays. This dataset includes 818 pairs of high-resolution CXR and dual-energy subtraction soft tissue images collected from a partner hospital. Moreover, we processed 241 data pairs from the JSRT dataset into negative images, which are more commonly used in clinical practice. Our comprehensive experimental and clinical evaluations reveal that BS-LDM excels in bone suppression, underscoring its significant clinical value.
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<h2 class="subtitle has-text-centered">
<b>The overview framework of our proposed BS-LDM.</b><br>
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Highlights</h2>
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<p>
🌟 An <b style="background: linear-gradient(to right, red, orange, blue, indigo, violet); -webkit-background-clip: text; -webkit-text-fill-color: transparent; font-size: 20px;">end-to-end</b> LDM-based framework for <b style="background: linear-gradient(to right, blue, violet, indigo, red, orange); -webkit-background-clip: text; -webkit-text-fill-color: transparent; font-size: 20px;">high-resolution</b> bone suppression to consistently generates soft tissue images with high levels of bone suppression while <b style="background: linear-gradient(to right, indigo, violet, red, blue, orange); -webkit-background-clip: text; -webkit-text-fill-color: transparent; font-size: 20px;">preserving fine details and critical lung lesions</b>.<br><br>
🌟 Incorporate <b style="background: linear-gradient(to right, red, indigo, violet, blue); -webkit-background-clip: text; -webkit-text-fill-color: transparent; font-size: 20px;">offset noise and a temporal adaptive thresholding strategy</b> to help minimize discrepancies in <b style="background: linear-gradient(to right, violet, red, indigo, blue); -webkit-background-clip: text; -webkit-text-fill-color: transparent; font-size: 20px;">low-frequency information</b> and enhance the quality of generated images.<br><br>
🌟 Compile <b style="background: linear-gradient(to right, indigo, red, blue, violet); -webkit-background-clip: text; -webkit-text-fill-color: transparent; font-size: 20px;">a comprehensive bone suppression dataset, SZCH-X-Rays</b>, including 818 pairs of high-resolution CXR and DES soft tissue images from our partner hospital.
</p>
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<h2 class="title is-2">Experiments</h2>
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<h2 class="subtitle has-text-centered">
<b>Visulization of different methods comparison on SZCH-X-Rays and JSRT datasets.</b>
<img src="static/images/abl_00.jpg" alt="MY ALT TEXT", width="1000"/>
<b>Visulization of abalation study on SZCH-X-Rays and JSRT datasets with histograms given to visualize the pixel intensity distribution.</b>
<img src="static/images/hyp_00.jpg" alt="MY ALT TEXT", width="1000"/>
<b>Visualization of hyperparameter analysis of offset noise in (a) and the temporal adaptive thresholding strategy in (b) on SZCH-X-Rays and JSRT datasets.</b>
</h2>
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<h2 class="title is-2">Clinical Evaluation</h2>
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<img src="static/images/tabel.png" alt="MY ALT TEXT", width="1000"/>
<h2 class="subtitle has-text-centered">
<b>Image quality assessment of BS-LDM scored by three radiologists with various levels of experience.</b>
</h2>
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<h2 class="title">BibTeX</h2>
<pre><code>@article{sun2024bsldmeffectivebonesuppression,
title={BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models},
author={Yifei Sun and Zhanghao Chen and Hao Zheng and Wenming Deng and Jin Liu and Wenwen Min and Ahmed Elazab and Xiang Wan and Changmiao Wang and Ruiquan Ge},
year={2024},
eprint={2412.15670},
archivePrefix={arXiv},
primaryClass={eess.IV},
url={https://arxiv.org/abs/2412.15670},
}</code></pre>
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