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

• URL: http://arxiv.org/abs/2511.04171v1
• Date: Thu, 06 Nov 2025 08:22:44 GMT
• Title: Systematic Evaluation of Preprocessing Techniques for Accurate Image Registration in Digital Pathology
• Authors: Fatemehzahra Darzi, Rodrigo Escobar Diaz Guerrero, Thomas Bocklitz,
• Abstract summary: We investigated how various color transformation techniques affect image registration between hematoxylin and eosin stained images and non-linear multimodal images.<n>CycleGAN color transformation achieved the lowest registration errors, while the other methods showed higher errors.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Robust Rigid and Non-Rigid Medical Image Registration Using Learnable Edge Kernels [2.8802868527181995]
  We propose a method that integrates learnable edge kernels with learning-based rigid and non-rigid registration techniques.<n>Our approach begins with a predefined edge detection kernel, which is then perturbed with random noise.<n>This adaptive edge detection enhances the registration process by capturing diverse structural features critical in medical imaging.
  arXiv  Detail & Related papers  (2025-12-01T15:13:33Z)
• Transformation trees -- documentation of multimodal image registration [0.0]
  Multimodal image registration plays a key role in creating digital patient models.<n>We propose the use of transformation trees as a method for structured recording and management of transformations.
  arXiv  Detail & Related papers  (2025-01-31T13:49:16Z)
• MsMorph: An Unsupervised pyramid learning network for brain image registration [4.000367245594772]
  MsMorph is an image registration framework aimed at mimicking the manual process of registering image pairs. It decodes semantic information at different scales and continuously compen-sates for the predicted deformation field. The proposed method simulates the manual approach to registration, focusing on different regions of the image pairs and their neighborhoods.
  arXiv  Detail & Related papers  (2024-10-23T19:20:57Z)
• Progressive Retinal Image Registration via Global and Local Deformable Transformations [49.032894312826244]
  We propose a hybrid registration framework called HybridRetina. We use a keypoint detector and a deformation network called GAMorph to estimate the global transformation and local deformable transformation. Experiments on two widely-used datasets, FIRE and FLoRI21, show that our proposed HybridRetina significantly outperforms some state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-09-02T08:43:50Z)
• AGMDT: Virtual Staining of Renal Histology Images with Adjacency-Guided Multi-Domain Transfer [9.8359439975283]
  We propose a novel virtual staining framework AGMDT to translate images into other domains by avoiding pixel-level alignment. Based on it, the proposed framework AGMDT discovers patch-level aligned pairs across the serial slices of multi-domains through glomerulus detection and bipartite graph matching. Experimental results show that the proposed AGMDT achieves a good balance between the precise pixel-level alignment and unpaired domain transfer.
  arXiv  Detail & Related papers  (2023-09-12T17:37:56Z)
• Breaking Modality Disparity: Harmonized Representation for Infrared and Visible Image Registration [66.33746403815283]
  We propose a scene-adaptive infrared and visible image registration. We employ homography to simulate the deformation between different planes. We propose the first ground truth available misaligned infrared and visible image dataset.
  arXiv  Detail & Related papers  (2023-04-12T06:49:56Z)
• Stain-invariant self supervised learning for histopathology image analysis [74.98663573628743]
  We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin stained images of breast cancer. Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets.
  arXiv  Detail & Related papers  (2022-11-14T18:16:36Z)
• CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation [56.622832383316215]
  We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to non-contrast CT scans. Our approach is based on cycle-consistent generative adversarial convolutional transformers, for short, CyTran. Our empirical results show that CyTran outperforms all competing methods.
  arXiv  Detail & Related papers  (2021-10-12T23:25:03Z)
• A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection [93.38607559281601]
  We devise a hierarchical generative model that captures the multi-scale patch distribution of each training image. The anomaly score is obtained by aggregating the patch-based votes of the correct transformation across scales and image regions.
  arXiv  Detail & Related papers  (2021-04-29T17:49:48Z)
• CoMIR: Contrastive Multimodal Image Representation for Registration [4.543268895439618]
  We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations) CoMIRs enable the registration of multimodal images where existing registration methods often fail due to a lack of sufficiently similar image structures.
  arXiv  Detail & Related papers  (2020-06-11T10:51:33Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.