Systematic Evaluation of Preprocessing Techniques for Accurate Image Registration in Digital Pathology

TL;DR

This study systematically evaluates preprocessing techniques, especially color transformations like CycleGAN, to improve the accuracy of image registration in digital pathology, demonstrating that proper preprocessing significantly enhances alignment across modalities.

Contribution

The paper introduces a comprehensive evaluation of preprocessing methods, highlighting the effectiveness of CycleGAN in improving multimodal image registration accuracy in digital pathology.

Findings

CycleGAN preprocessing yields lowest registration errors

Color transformation improves multimodal image alignment

Preprocessing enhances reliability of digital pathology analysis

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.