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Whole slide image registration via multi-stained feature matching
Md Ziaul Hoque1, Anja Keskinarkaus1, Pia Nyberg2
1Physiological Signal Analysis Group, Center for Machine Vision and Signal Analysis, University of Oulu, Finland; Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland.
Computers in Biology and Medicine
|March 7, 2022
Summary
This study introduces a new algorithm for whole slide image registration, addressing challenges like staining variations and large file sizes in digital pathology. The method enhances matching accuracy for improved computer-aided diagnosis in biobanking.
Area of Science:
- Digital Pathology
- Medical Image Analysis
- Computational Biology
Background:
- Medical image registration and fusion are crucial for disease monitoring and treatment planning.
- Digital pathology often faces challenges with staining variations, artifacts, and large image sizes.
- Accurate overlay of histological slides from different modalities is essential for specific area analysis.
Purpose of the Study:
- To develop an algorithm for whole slide image registration that overcomes variations from different scanner manufacturers.
- To improve the accuracy of image registration in digital pathology for enhanced diagnostic capabilities.
Main Methods:
- Proposed a whole slide image registration algorithm utilizing adaptive smoothing for stained images.
- Employed a modified scale-invariant feature transform (SIFT) for common information extraction.
- Utilized joint distance for correct keypoint matching, eliminating position transformation errors.
Main Results:
- The algorithm successfully registered whole slide images, overcoming variations due to staining and scanner differences.
- Demonstrated superior registration performance and increased correct correspondences compared to state-of-the-art methods.
- Validated using lung cancer (adenocarcinoma) surgical resection samples.
Conclusions:
- The developed registration algorithm effectively handles common challenges in digital pathology.
- The method shows potential for improving matching accuracy, benefiting computer-aided diagnosis in biobank applications.
- This work contributes to more reliable analysis of histological data for clinical and research purposes.
