骨植入物三维X射线组织学的虚拟染色
Virtual staining for 3D X-ray histology of bone implants
AI总结：
本研究将虚拟染色扩展到三维X射线成像，利用改进的CycleGAN对骨植入物显微CT数据进行跨模态转换，生成组织学上逼真的染色切片，在多项指标上优于基线方法，无需额外样本制备即可增强三维数据的可解释性。
摘要：
三维X射线组织学技术为传统的二维组织学提供了一种非侵入性的替代方案，能够在不进行物理切片或化学染色的情况下对生物组织进行体积成像。然而，与传统的组织学染色相比，X射线断层扫描固有的灰度图像对比度限制了其生化特异性。在数字病理学领域，基于深度学习的虚拟染色已显示出从无标记光学图像模拟染色外观的实用性。在本研究中，我们将虚拟染色扩展到X射线领域，通过应用跨模态图像转换，从基于同步辐射的显微CT扫描中生成人工染色的切片。利用来自骨植入物样本的超过50对共配准的显微CT和甲苯胺蓝染色组织学图像对，我们训练了一个针对有限配对数据量身定制的改进型CycleGAN网络。将全切片组织学图像降采样以匹配CT数据的体素大小，并采用即时数据增强进行基于补丁的训练。该模型结合了像素级监督和灰度一致性项，在保留高分辨率结构细节的同时，生成组织学上逼真的彩色输出。我们的方法在SSIM、PSNR和LPIPS指标上均优于Pix2Pix和标准CycleGAN基线。一旦训练完成，该模型可应用于完整的CT体积，生成虚拟染色的三维数据集，无需额外的样本制备即可增强可解释性。虽然新骨形成等特征能够被重现，但植入物降解层描绘中的一些变异性突显了进一步训练数据和优化的必要性。这项工作将虚拟染色引入三维X射线成像，并为生物医学研究中化学信息丰富、无标记的组织表征提供了一条可扩展的途径。
英文摘要：
Three-dimensional X-ray histology techniques offer a non-invasive alternative to conventional 2D histology, enabling volumetric imaging of biological tissues without the need for physical sectioning or chemical staining. However, the inherent greyscale image contrast of X-ray tomography limits its biochemical specificity compared to traditional histological stains. Within digital pathology, deep learning-based virtual staining has demonstrated utility in simulating stained appearances from label-free optical images. In this study, we extend virtual staining to the X-ray domain by applying cross-modality image translation to generate artificially stained slices from synchrotron-radiation-based micro-CT scans. Using over 50 co-registered image pairs of micro-CT and toluidine blue-stained histology from bone-implant samples, we trained a modified CycleGAN network tailored for limited paired data. Whole slide histology images were downsampled to match the voxel size of the CT data, with on-the-fly data augmentation for patch-based training. The model incorporates pixelwise supervision and greyscale consistency terms, producing histologically realistic colour outputs while preserving high-resolution structural detail. Our method outperformed Pix2Pix and standard CycleGAN baselines across SSIM, PSNR, and LPIPS metrics. Once trained, the model can be applied to full CT volumes to generate virtually stained 3D datasets, enhancing interpretability without additional sample preparation. While features such as new bone formation were able to be reproduced, some variability in the depiction of implant degradation layers highlights the need for further training data and refinement. This work introduces virtual staining to 3D X-ray imaging and offers a scalable route for chemically informative, label-free tissue characterisation in biomedical research.
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