Image Registration: The Navigation System for Digital Pathology Diagnostics and Biomedical Research

Image registration is the automatic alignment of similar images such as consecutive tissue sections in pathology diagnostics and biomedical research. In a digital workflow, image registration allows pathologists to seamlessly switch between various stains, minimizing repetitive navigation tasks and thereby streamlining the diagnostic procedure. In research, it integrates direct and third-party information from differently stained slides, combining otherwise separate information in pharmacological studies and biomarker discovery. For this integration to work in practice, image registration must be robust against variations found in routine pathology images, such as differences in stain intensity and artifacts, to ensure widespread applicability. Diverse approaches are possible for registration: feature-based, intensity-based, deep learning, and combinations thereof. As computational pathology continues to advance, leveraging image registration technologies significantly improves diagnostic efficiency and accuracy, paving the way for enhanced patient outcomes and groundbreaking research discoveries.

Authors

Publication Details

Journal
Trillium Pathology
Published
2025-06-02
DOI
https://doi.org/10.47184/tp.2025.01.02
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Image Registration: The Navigation System for Digital Pathology Diagnostics and Biomedical Research

Daniel Budelmann, Johannes Lotz
Trillium Pathology
AI in cancer detection
article

Image Registration: The Navigation System for Digital Pathology Diagnostics and Biomedical Research

Daniel Budelmann, Johannes Lotz
article en

Abstract

Image registration is the automatic alignment of similar images such as consecutive tissue sections in pathology diagnostics and biomedical research. In a digital workflow, image registration allows pathologists to seamlessly switch between various stains, minimizing repetitive navigation tasks and thereby streamlining the diagnostic procedure. In research, it integrates direct and third-party information from differently stained slides, combining otherwise separate information in pharmacological studies and biomarker discovery. For this integration to work in practice, image registration must be robust against variations found in routine pathology images, such as differences in stain intensity and artifacts, to ensure widespread applicability. Diverse approaches are possible for registration: feature-based, intensity-based, deep learning, and combinations thereof. As computational pathology continues to advance, leveraging image registration technologies significantly improves diagnostic efficiency and accuracy, paving the way for enhanced patient outcomes and groundbreaking research discoveries.

Trillium PathologyVol. 4(1)
Openalex Percentile: Top 94%
AI in cancer detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Image Registration: The Navigation System for Digital Pathology Diagnostics and Biomedical Research — Daniel Budelmann, Johannes Lotz · Trillium Pathology (2025) | TGRS Research Map | TGRS