Automated Synchronized Navigation for Pathology Stain Images
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Solution Overview
Problem
Existing systems for aligning and navigating pathology stain images require manual adjustments, which are tedious and impractical due to differences in image appearance, local deformations, and varied tissue sample placements, especially for large images.
Innovation Solution
An automated system that downscaling, estimates rotation, aligns, and transforms image coordinates to generate alignment data, allowing synchronized navigation across different resolutions and orientations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alignment methods are used, then navigation accuracy can be maintained for small images, but the process becomes tedious and impractical for large images
Solution Approach 1:
The patent divides the large pathology images into multiple lower-resolution segments or tiles. The alignment process operates on these segmented, smaller images rather than the full large images, significantly reducing computation time while maintaining navigation accuracy through the segmented coordinate transformation.
Solution Approach 2:
The system performs preliminary alignment on downsampled versions of the images before final navigation. By pre-aligning the images at lower resolution and storing the transformation parameters, the system avoids time-consuming real-time alignment operations during navigation, thus reducing overall processing time while maintaining precision.
2Measurement precision
If images are examined at high resolution locally, then detail visibility is improved, but the appearance between corresponding regions diverges rapidly making it difficult to find matching points
Solution Approach 1:
The patent introduces an intermediate representation dimension by creating downsampled versions of the images. These lower-resolution images serve as a bridge dimension where structural correspondences are more visible and easier to detect, while the high-resolution original images maintain detail visibility. The system transforms coordinates between these dimensional representations.
Solution Approach 2:
The downsampled images act as an intermediary representation between the high-resolution original images and the alignment process. This intermediate dimension allows the system to detect matching points and establish correspondences more easily, while still enabling navigation at the original high resolution through coordinate transformation.
3Productivity
If automated alignment is implemented, then navigation efficiency is improved, but the system must handle varied tissue sample placements and local deformations
Solution Approach 1:
The patent employs affine transformation parameters to model and compensate for tissue deformations and variations in sample placement. By changing the transformation parameters (translation, rotation, scaling, shearing) to adapt to different tissue configurations, the system achieves automated alignment while maintaining the ability to handle varied and deformed samples.
Solution Approach 2:
The alignment system uses dynamic transformation models that can adapt to different tissue deformation patterns. The affine transformation parameters are estimated based on detected feature correspondences and can be adjusted to accommodate varied tissue placements and local deformations, enabling the system to maintain navigation efficiency across diverse samples.
4Loss of time
If downscaling is applied to images, then alignment computation time is reduced, but image resolution is decreased
Solution Approach 1:
The system performs alignment computations on downsampled images as a preliminary step, completing the time-consuming alignment process before final navigation. The transformation parameters obtained from the low-resolution alignment are then applied to the high-resolution images, achieving fast computation while preserving original image quality for navigation.
Solution Approach 2:
The patent creates a lower-resolution dimension for alignment computations while maintaining the original high-resolution dimension for navigation. By operating in this reduced-dimensional space during alignment and then transforming back to the original dimension, the system achieves both fast computation and high-resolution output.
Data Source
AI summary
A method for synchronizing navigation in pathology stain images includes (a) downscaling the pathology stain images, (b) estimating rotation of the downscaled images, (c) aligning the downscaled images to generate aligned coordinates, and (d) transforming the aligned coordinates to original image coordinates in the pathology stain images to thereby generate alignment data. Also provided is a system for synchronized navigation in pathology stain images having original resolutions comprising a downscaler, a rotation estimator, an alignment module, and a coordinate transformer. The system may also include an image display system to display corresponding areas of the pathology stain images.