Line-Based Image Registration for Digital Pathology
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Solution Overview
Problem
Current Digital Pathology systems face challenges in efficiently aligning and annotating digitized images of adjacent tissue sections, particularly due to issues like wear-and-tear, Area of Interest mismatches, rotation, and horizontal/vertical flips, which affect imaging performance and scalability.
Innovation Solution
The proposed solution involves a computerized image registration process that models tissue sample boundary regions with line segments, matches these line segments between images to achieve global alignment, and uses a finer sub-image registration process based on normalized correlation in the gradient magnitude domain to refine local alignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image registration methods are used, then alignment speed may be acceptable, but alignment accuracy deteriorates under conditions of wear-and-tear, rotation, flips, and Area of Interest mismatches
Solution Approach 1:
The patent segments the image registration process into two distinct stages: coarse registration that handles global transformations (rotation, flips, translation) and fine registration that handles local deformations. This segmentation allows each stage to optimize for its specific purpose, with coarse registration providing robustness to major misalignments and fine registration delivering precision for local details, thereby resolving the contradiction between accuracy and robustness.
Solution Approach 2:
The patent applies preliminary action by performing coarse registration first to establish a rough alignment that corrects for wear-and-tear, rotation, flips, and Area of Interest mismatches before proceeding to fine registration. This preliminary global alignment creates a better starting point for the subsequent local refinement, enabling the fine registration to focus only on minor deformations and achieve higher accuracy.
2Productivity
If a single-pass registration process is used, then processing time is reduced, but the ability to handle complex mismatches deteriorates
Solution Approach 1:
The patent segments the registration process into coarse and fine passes, each targeting specific types of mismatches. The coarse pass handles global transformations (rotation, flips, translation) while the fine pass handles local deformations. This segmentation enables the system to efficiently handle multiple mismatch types without requiring a single overly complex algorithm, maintaining processing speed while improving adaptability.
Solution Approach 2:
The patent implements a dynamic two-pass registration system where the first pass establishes global alignment and the second pass refines local details. This dynamic approach allows the system to adapt to different types of mismatches by adjusting the focus of each pass, thereby improving versatility while maintaining computational efficiency through the hierarchical structure.
3Device complexity
If coarse registration alone is used, then processing complexity is reduced, but local alignment precision deteriorates
Solution Approach 1:
The patent segments the registration process into coarse and fine passes, where the coarse pass handles global transformations with simpler algorithms and the fine pass handles local deformations with more sophisticated methods. This segmentation allows the system to maintain reasonable overall complexity while achieving high local precision in the fine registration stage, resolving the contradiction between simplicity and precision.
4Measurement precision
If fine registration is applied to entire images, then local precision is improved, but processing time increases significantly
Solution Approach 1:
The patent applies preliminary coarse registration to establish global alignment before performing fine registration. This preliminary action confines the fine registration computations to smaller local regions around corresponding features, dramatically reducing the computational domain and processing time while maintaining high local precision where it is most needed.
Solution Approach 2:
The patent implements local quality by applying fine registration only to specific local regions rather than entire images. The coarse registration provides a global framework, and the fine registration focuses computational resources on local areas requiring precision, thereby achieving high local alignment precision without the prohibitive processing time that would result from applying fine registration to entire images.
Data Source
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. The basis for alignment may be a line-based registration process, wherein sets of lines are computed on the boundary regions computed for the two images, where the boundary regions are obtained using information from two domains—soft-weighted foreground images and gradient magnitude images. The binary mask image, based on whose boundary the line features are computed, may be generated by combining two binary masks—a first binary mask is obtained on thresholding a soft-weighted (continuous valued) foreground image, which is computed based on the stain content in an image, while a second binary mask is obtained after thresholding a gradient magnitude domain image, where the gradient is computed from the grayscale image obtained from the color image.