Deep histological synthesis from mass spectrometry imaging for multimodal registration
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866918046620188672 |
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| author | Bird, Kimberley M. Ye, Xujiong Race, Alan M. Brown, James M. |
| author_facet | Bird, Kimberley M. Ye, Xujiong Race, Alan M. Brown, James M. |
| contents | Registration of histological and mass spectrometry imaging (MSI) allows for more precise identification of structural changes and chemical interactions in tissue. With histology and MSI having entirely different image formation processes and dimensionalities, registration of the two modalities remains an ongoing challenge. This work proposes a solution that synthesises histological images from MSI, using a pix2pix model, to effectively enable unimodal registration. Preliminary results show promising synthetic histology images with limited artifacts, achieving increases in mutual information (MI) and structural similarity index measures (SSIM) of +0.924 and +0.419, respectively, compared to a baseline U-Net model. Our source code is available on GitHub: https://github.com/kimberley/MIUA2025. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05441 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep histological synthesis from mass spectrometry imaging for multimodal registration Bird, Kimberley M. Ye, Xujiong Race, Alan M. Brown, James M. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning I.2; I.4 Registration of histological and mass spectrometry imaging (MSI) allows for more precise identification of structural changes and chemical interactions in tissue. With histology and MSI having entirely different image formation processes and dimensionalities, registration of the two modalities remains an ongoing challenge. This work proposes a solution that synthesises histological images from MSI, using a pix2pix model, to effectively enable unimodal registration. Preliminary results show promising synthetic histology images with limited artifacts, achieving increases in mutual information (MI) and structural similarity index measures (SSIM) of +0.924 and +0.419, respectively, compared to a baseline U-Net model. Our source code is available on GitHub: https://github.com/kimberley/MIUA2025. |
| title | Deep histological synthesis from mass spectrometry imaging for multimodal registration |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning I.2; I.4 |
| url | https://arxiv.org/abs/2506.05441 |