Diffusion-based Virtual Staining from Polarimetric Mueller Matrix Imaging
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917943265198080 |
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| author | Zheng, Xiaoyu Wen, Jing Zhuang, Jiaxin Du, Yao Cong, Jing Guo, Limei He, Chao Luo, Lin Chen, Hao |
| author_facet | Zheng, Xiaoyu Wen, Jing Zhuang, Jiaxin Du, Yao Cong, Jing Guo, Limei He, Chao Luo, Lin Chen, Hao |
| contents | Polarization, as a new optical imaging tool, has been explored to assist in the diagnosis of pathology. Moreover, converting the polarimetric Mueller Matrix (MM) to standardized stained images becomes a promising approach to help pathologists interpret the results. However, existing methods for polarization-based virtual staining are still in the early stage, and the diffusion-based model, which has shown great potential in enhancing the fidelity of the generated images, has not been studied yet. In this paper, a Regulated Bridge Diffusion Model (RBDM) for polarization-based virtual staining is proposed. RBDM utilizes the bidirectional bridge diffusion process to learn the mapping from polarization images to other modalities such as H\&E and fluorescence. And to demonstrate the effectiveness of our model, we conduct the experiment on our manually collected dataset, which consists of 18,000 paired polarization, fluorescence and H\&E images, due to the unavailability of the public dataset. The experiment results show that our model greatly outperforms other benchmark methods. Our dataset and code will be released upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01352 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffusion-based Virtual Staining from Polarimetric Mueller Matrix Imaging Zheng, Xiaoyu Wen, Jing Zhuang, Jiaxin Du, Yao Cong, Jing Guo, Limei He, Chao Luo, Lin Chen, Hao Image and Video Processing Computer Vision and Pattern Recognition Polarization, as a new optical imaging tool, has been explored to assist in the diagnosis of pathology. Moreover, converting the polarimetric Mueller Matrix (MM) to standardized stained images becomes a promising approach to help pathologists interpret the results. However, existing methods for polarization-based virtual staining are still in the early stage, and the diffusion-based model, which has shown great potential in enhancing the fidelity of the generated images, has not been studied yet. In this paper, a Regulated Bridge Diffusion Model (RBDM) for polarization-based virtual staining is proposed. RBDM utilizes the bidirectional bridge diffusion process to learn the mapping from polarization images to other modalities such as H\&E and fluorescence. And to demonstrate the effectiveness of our model, we conduct the experiment on our manually collected dataset, which consists of 18,000 paired polarization, fluorescence and H\&E images, due to the unavailability of the public dataset. The experiment results show that our model greatly outperforms other benchmark methods. Our dataset and code will be released upon acceptance. |
| title | Diffusion-based Virtual Staining from Polarimetric Mueller Matrix Imaging |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.01352 |