Digital Slide Alignment Using Sectionable Fiducial Markers
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Solution Overview
Problem
Conventional methods for aligning digital slide images of tissue samples are cumbersome and prone to failure due to variations in morphology across different levels, making it difficult to visually locate corresponding points or locations, and manual manipulation of transparent slides is tedious and inefficient.
Innovation Solution
Embedding sectionable fiducial markers within tissue samples to create consistent reference points across levels, using machine learning models to identify and align digital slide images based on these markers, enabling automatic orientation, scaling, and alignment of images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual manipulation of transparent slides is used for alignment, then the pathologist can physically adjust slides to view corresponding sections, but the process becomes tedious and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical manipulation of physical slides with an automated computational system. Machine learning models automatically detect fiducial markers in digital slide images and compute alignment transformations, eliminating the need for pathologists to manually manipulate transparent slides. This substitution of mechanical operation with automated image processing resolves the contradiction by dramatically reducing both the effort required and the time needed for alignment.
Solution Approach 2:
The alignment system performs self-alignment by automatically detecting fiducial markers and computing the necessary transformations without human intervention. The machine learning model independently identifies markers across multiple slide images and determines the optimal alignment parameters, enabling the system to service itself rather than requiring manual operation. This self-service capability eliminates the tedious manual manipulation while maintaining accurate alignment.
2Reliability
If conventional alignment methods rely on visual matching of tissue morphology, then alignment can be attempted without additional markers, but the process is prone to failure due to morphological variations across different levels
Solution Approach 1:
The patent introduces fiducial markers as intermediary objects embedded within the tissue sample block. These markers serve as reliable reference points that persist across different tissue levels, providing a stable basis for alignment that is independent of variable tissue morphology. The machine learning model detects these intermediary markers to establish correspondences between slides, significantly improving alignment reliability compared to direct morphological matching while adding only moderate complexity through marker embedding and detection.
Solution Approach 2:
The invention changes the basis of alignment from relying on variable tissue morphological parameters to using stable fiducial marker parameters. By detecting the position, orientation, and shape of embedded markers rather than attempting to match complex tissue structures, the system transforms the alignment problem into a more reliable parameter-based task. This parameter change from morphology-dependent to marker-dependent alignment resolves the reliability issue while managing system complexity.
3Loss of information
If multiple sections or levels of tissue samples are analyzed, then comprehensive diagnostic review is achieved, but it becomes difficult to visually locate corresponding points across different levels
Solution Approach 1:
The fiducial markers serve a universal function across all tissue levels and slide images. The same marker type is embedded in the original tissue block and appears consistently across multiple sectioned levels, providing a common reference framework that works universally for aligning any number of slides. This universality enables the system to maintain correspondence information across all levels while making it easy to locate corresponding points through automatic marker-based alignment rather than manual visual searching.
Data Source
AI summary
The disclosed method of processing tissue samples may include (1) placing a tissue sample within a pre-formed structure that includes a sectionable fiducial marker therein, (2) processing the tissue sample and the pre-formed structure, which includes the sectionable fiducial marker therein, together to form a sectionable tissue sample block, and (3) sectioning the sectionable tissue sample block into a variety of sections, each of which comprises a section of the tissue sample and a section of the sectionable fiducial marker. Various other methods, systems, apparatuses, and computer-readable media are also disclosed.