Whole Slide Image Registration Using Tissue Structure Matching
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Solution Overview
Problem
Digital Pathology faces impediments in widespread adoption due to challenges in imaging performance, scalability, and management, particularly in aligning and transferring annotations across digitized images of adjacent tissue sections stained with different biomarkers or obtained using different imaging modes.
Innovation Solution
A computer program product for aligning digital images of adjacent tissue sections using image registration processes based on matching tissue structure, allowing for the transfer of annotations between images, which involves computing soft weighted foreground images, extracting binary tissue edge-maps, and computing global transformation parameters to map images onto a common grid, followed by a fine registration process to refine annotation locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image registration processes are used to align digital images of adjacent tissue sections, then alignment accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The image registration process is divided into two distinct stages: coarse registration that establishes initial alignment using global transformation parameters, and fine registration that refines alignment locally around annotation regions. This segmentation allows the system to achieve high precision without processing the entire image at full resolution, thereby reducing overall processing time while maintaining alignment accuracy.
Solution Approach 2:
The system applies fine registration selectively only to regions containing annotations rather than processing the entire image. By focusing computational resources on partial regions where precision is most needed, the system achieves high alignment accuracy for annotated areas while significantly reducing total processing time compared to full-image fine registration.
2Loss of information
If multiple tissue sections stained with different biomarkers are analyzed, then diagnostic information is improved, but annotation transfer complexity increases
Solution Approach 1:
The annotation transfer system is designed to handle multiple stain types and imaging modes through a universal framework that uses tissue structure matching rather than stain-specific alignment. The coarse registration uses global transformation parameters that work across different staining protocols, while fine registration adapts locally to preserve annotation accuracy, enabling the system to process diverse pathological images without requiring separate processing pipelines for each stain type.
3Measurement precision
If manual annotation is performed on each tissue section separately, then annotation accuracy is maintained, but pathologist workload increases
Solution Approach 1:
The system performs preliminary coarse registration and generates initial annotation transfers before the pathologist reviews the images. This preliminary alignment provides a head start, allowing the pathologist to focus only on verifying and refining annotations rather than creating them from scratch on each section. The pre-computed transformation parameters serve as a foundation that maintains accuracy while significantly reducing the manual effort required.
Solution Approach 2:
The system implements an iterative feedback mechanism where pathologist corrections to transferred annotations are used to refine the registration parameters for subsequent sections. This feedback loop continuously improves alignment accuracy across the series while reducing the burden on the pathologist, as the system learns from previous corrections and requires minimal intervention for later sections.
Data Source
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AI summary
The disclosure relates to devices, systems and methods for image registration and annotation. The devices include computer software products for aligning whole slide digital images on a common grid and transferring annotations from one aligned image to another aligned image on the basis of matching tissue structure. The systems include computer-implemented systems such as work stations and networked computers for accomplishing the tissue-structure based image registration and cross-image annotation. The methods include processes for aligning digital images corresponding to adjacent tissue sections on a common grid based on tissue structure, and transferring annotations from one of the adjacent tissue images to another of the adjacent tissue images.