🤖 AI Summary
This study systematically investigates the impact of preprocessing—particularly color normalization—on cross-modal registration accuracy between H&E-stained digital pathology images and nonlinear multimodal images. Four color normalization methods (CycleGAN, Macenko, Reinhard, and Vahadane) are evaluated, each combined with enhancement strategies including intensity inversion, contrast adjustment, intensity normalization, and denoising; rigid and non-rigid multi-resolution registration is performed using VALIS. Validation on 20 real tissue samples demonstrates that CycleGAN-based preprocessing yields the lowest median and average relative target registration error (MMrTRE/AMrTRE), with point-based evaluation further confirming its superior performance. To our knowledge, this is the first work to establish CycleGAN’s distinct advantage in histopathological cross-modal registration, empirically validating color transformation as a critical preprocessing step for enhancing registration robustness. The findings provide a reproducible, optimized pipeline for spatial biomarker localization and multimodal tissue reconstruction.
📝 Abstract
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.