Rapid Bone Scintigraphy Enhancement via Semantic Prior Distillation from Segment Anything Model
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909636004675584 |
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| author | Liang, Pengchen Shi, Leijun Yao, Huiping Pu, Bin Chen, Jianguo Zhao, Lei Huang, Haishan Chen, Zhuangzhuang Xu, Zhaozhao Xu, Lite Chang, Qing Li, Yiwei |
| author_facet | Liang, Pengchen Shi, Leijun Yao, Huiping Pu, Bin Chen, Jianguo Zhao, Lei Huang, Haishan Chen, Zhuangzhuang Xu, Zhaozhao Xu, Lite Chang, Qing Li, Yiwei |
| contents | Rapid bone scintigraphy is crucial for diagnosing skeletal disorders and detecting tumor metastases in children, as it shortens scan duration and reduces discomfort. However, accelerated acquisition often degrades image quality, impairing the visibility of fine anatomical details and potentially compromising diagnosis. To overcome this limitation, we introduce the first application of SAM-based semantic priors for medical image restoration, utilizing the Segment Anything Model (SAM) to enhance pediatric rapid bone scintigraphy. Our approach employs two cascaded networks, $f^{IR1}$ and $f^{IR2}$, supported by three specialized modules: a Semantic Prior Integration (SPI) module, a Semantic Knowledge Distillation (SKD) module, and a Semantic Consistency Module (SCM). The SPI and SKD modules inject domain-specific semantic cues from a fine-tuned SAM, while the SCM preserves coherent semantic feature representations across both cascaded stages. Moreover, we present RBS, a novel Rapid Bone Scintigraphy dataset comprising paired standard (20 cm/min) and rapid (40 cm/min) scans from 137 pediatric patients aged 0.5 - 16 years, making it the first dataset tailored for pediatric rapid bone scintigraphy restoration. Extensive experiments on both a public endoscopic dataset and our RBS dataset demonstrate that our method consistently surpasses existing techniques in PSNR, SSIM, FID, and LPIPS metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02321 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rapid Bone Scintigraphy Enhancement via Semantic Prior Distillation from Segment Anything Model Liang, Pengchen Shi, Leijun Yao, Huiping Pu, Bin Chen, Jianguo Zhao, Lei Huang, Haishan Chen, Zhuangzhuang Xu, Zhaozhao Xu, Lite Chang, Qing Li, Yiwei Image and Video Processing Computer Vision and Pattern Recognition Rapid bone scintigraphy is crucial for diagnosing skeletal disorders and detecting tumor metastases in children, as it shortens scan duration and reduces discomfort. However, accelerated acquisition often degrades image quality, impairing the visibility of fine anatomical details and potentially compromising diagnosis. To overcome this limitation, we introduce the first application of SAM-based semantic priors for medical image restoration, utilizing the Segment Anything Model (SAM) to enhance pediatric rapid bone scintigraphy. Our approach employs two cascaded networks, $f^{IR1}$ and $f^{IR2}$, supported by three specialized modules: a Semantic Prior Integration (SPI) module, a Semantic Knowledge Distillation (SKD) module, and a Semantic Consistency Module (SCM). The SPI and SKD modules inject domain-specific semantic cues from a fine-tuned SAM, while the SCM preserves coherent semantic feature representations across both cascaded stages. Moreover, we present RBS, a novel Rapid Bone Scintigraphy dataset comprising paired standard (20 cm/min) and rapid (40 cm/min) scans from 137 pediatric patients aged 0.5 - 16 years, making it the first dataset tailored for pediatric rapid bone scintigraphy restoration. Extensive experiments on both a public endoscopic dataset and our RBS dataset demonstrate that our method consistently surpasses existing techniques in PSNR, SSIM, FID, and LPIPS metrics. |
| title | Rapid Bone Scintigraphy Enhancement via Semantic Prior Distillation from Segment Anything Model |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.02321 |