数字病理学中用于精确图像配准的预处理技术的系统评估
Systematic Evaluation of Preprocessing Techniques for Accurate Image Registration in Digital Pathology
AI总结：
本文系统评估了多种颜色变换等预处理技术对数字病理学中H&E与多模态图像配准的影响，发现CycleGAN颜色变换在两种场景下均取得最低配准误差，证明配准前颜色变换能显著提升跨模态图像对齐精度。
摘要：
图像配准是指通过将两幅或多幅图像映射到一个共同的坐标系中，使其在空间上对齐，从而使图像间相应的解剖或组织结构得到匹配的过程。在数字病理学中，配准能够直接比较和整合来自不同染色或成像模态的信息，支持诸如生物标志物分析和组织重建等应用。来自不同模态的图像的精确配准是数字病理学中的一个关键步骤。在本研究中，我们考察了各种颜色变换技术如何影响苏木精-伊红（H&E）染色图像与非线性多模态图像之间的图像配准。我们使用了一个包含20对组织样本的数据集，每对样本经过若干预处理步骤，包括不同的颜色变换（CycleGAN、Macenko、Reinhard、Vahadane）、反相、对比度调整、强度归一化和去噪。所有图像均使用VALIS配准方法进行配准，该方法首先应用刚性配准，然后在低分辨率和高分辨率图像上分两步执行非刚性配准。配准性能使用相对目标配准误差（rTRE）进行评估。我们报告了每种方法的中位rTRE值的中位数（MMrTRE）和中位rTRE值的平均值（AMrTRE）。此外，我们使用十个手动选择的关键点进行了自定义的基于点的评估。配准分别针对两种场景进行，使用原始或反相的多模态图像。在这两种场景中，CycleGAN颜色变换均实现了最低的配准误差，而其他方法则显示出更高的误差。这些发现表明，在配准前应用颜色变换可以改善来自不同模态的图像之间的对齐，并支持数字病理学中更可靠的分析。
英文摘要：
Image registration refers to the process of spatially aligning two or more images by mapping them into a common coordinate system, so that corresponding anatomical or tissue structures are matched across images. In digital pathology, registration enables direct comparison and integration of information from different stains or imaging modalities, sup-porting applications such as biomarker analysis and tissue reconstruction. Accurate registration of images from different modalities is an essential step in digital pathology. In this study, we investigated how various color transformation techniques affect image registration between hematoxylin and eosin (H&E) stained images and non-linear multimodal images. We used a dataset of 20 tissue sample pairs, with each pair undergoing several preprocessing steps, including different color transformation (CycleGAN, Macenko, Reinhard, Vahadane), inversion, contrast adjustment, intensity normalization, and denoising. All images were registered using the VALIS registration method, which first applies rigid registration and then performs non-rigid registration in two steps on both low and high-resolution images. Registration performance was evaluated using the relative Target Registration Error (rTRE). We reported the median of median rTRE values (MMrTRE) and the average of median rTRE values (AMrTRE) for each method. In addition, we performed a custom point-based evaluation using ten manually selected key points. Registration was done separately for two scenarios, using either the original or inverted multimodal images. In both scenarios, CycleGAN color transformation achieved the lowest registration errors, while the other methods showed higher errors. These findings show that applying color transformation before registration improves alignment between images from different modalities and supports more reliable analysis in digital pathology.
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