Whole-Slide Image Alignment Using Similarity Graphs
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Solution Overview
Problem
Existing methods for aligning immunohistochemistry (IHC) slide images face challenges due to varying pixel intensities, tissue shape changes, sample distortion, and unknown slice order, often focusing on small regions or limited slides.
Innovation Solution
A computer-implemented method for slide image registration that includes detecting features, creating a distance matrix, forming a graph with similar images, and aligning graph components to form a composite image, using techniques like KAZE features, hierarchical clustering, and optical flow for precise alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional alignment methods are used focusing on small regions of interest, then alignment precision for local areas is improved, but the ability to handle whole slide images and provide comprehensive spatial analysis is worsened
Solution Approach 1:
The patent divides the alignment process into multiple stages by segmenting the slide images into different regions of interest (ROIs) and processing them separately before integrating the results. This allows high-precision alignment to be achieved in local areas while ultimately providing comprehensive coverage of the entire slide through hierarchical clustering and multi-scale processing.
2Device complexity
If conventional methods focus on single stains or limited number of slides, then processing complexity is reduced, but the ability to handle multi-stain and multi-slide comprehensive analysis is worsened
Solution Approach 1:
The patent develops a universal alignment framework that can handle multiple stains, multiple slides, and various tissue types through a single integrated system. The method uses stain normalization techniques and invariant feature detection that work across different staining protocols and slide preparations, eliminating the need for separate processing pipelines for each stain or slide set.
3Productivity
If automated alignment is implemented, then productivity is improved, but the ability to handle unknown slice order and complex distortions is worsened
Solution Approach 1:
The patent implements iterative alignment with feedback mechanisms where the system continuously refines the alignment based on detected features and tissue structure. The hierarchical clustering algorithm provides feedback about the relative positions of different slides, allowing the system to automatically correct for unknown slice order and adjust for distortions through multiple refinement cycles.
Solution Approach 2:
The patent performs preliminary processing steps including stain normalization, feature detection, and initial alignment before the main alignment process. This preliminary action prepares the data in advance, making the subsequent automated alignment more reliable and accurate even when slice order is unknown or distortions are present.
Data Source
AI summary
Systems and methods for slide image alignment are described herein. An example method includes receiving a plurality of slide images, detecting a plurality of features contained in the slide images, and comparing a plurality of pairs of the slide images. The comparison uses the detected features. The method also includes creating a distance matrix that reflects a respective difference between each of the pairs of the slide images, creating a graph by connecting each of the slide images to its most similar slide image, and detecting a plurality of graph components. Each of the graph components includes one or more of the slide images. The method further includes aligning the slide images within each of the graph components, and aligning the graph components to form a composite image.