用质谱成像生成组织切片图像，解决多模态图像配准难题。
Deep histological synthesis from mass spectrometry imaging for multimodal registration
- 用pix2pix模型从质谱成像合成组织切片图像。
- 合成图像使互信息和结构相似性分别提升0.924和0.419。
- 适合医学图像分析、多模态配准研究者参考。
组织学与质谱成像（MSI）的配准有助于更精确地识别组织结构变化与化学相互作用。由于两者成像原理和维度差异大，模态配准仍是挑战。本文提出一种方法，通过pix2pix模型从MSI合成组织切片图像，实现单模态配准。初步结果表明，合成图像伪影少，相比基线U-Net模型，互信息（MI）提升0.924，结构相似性指数（SSIM）提升0.419。源代码已开源：https://github.com/kimberley/MIUA2025。
原文摘要 · Abstract (English)
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.
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