Cross-Modality Cell-to-Cell Registration for Accurate Tissue Phenotyping
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Solution Overview
Problem
Conventional image registration techniques struggle to align and track individual tissue cells across different visualization modalities, leading to challenges in accurately identifying and classifying tissue cells, especially when conventional techniques require the same imaging modality and are unsuitable for aligning cells captured using various visualization methods.
Innovation Solution
A cross-modality cell-to-cell registration process is employed to identify and phenotype tissue cells using one visualization modality as ground truth data for training machine-learning models, allowing classification and phenotype across different modalities by leveraging spatial features observable by human experts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image registration techniques are used to align tissue cells, then alignment can be performed within the same imaging modality, but the technique fails to align cells across different visualization modalities
Solution Approach 1:
The patent introduces a cross-modality registration technique that acts as an intermediary between different visualization modalities (e.g., H&E staining and immunofluorescence). This technique uses a two-step process: first aligning images within the same modality using conventional methods, then establishing correspondence between different modalities through a learned transformation model. This intermediary approach enables reliable cross-modality alignment without requiring direct alignment between disparate modalities.
Solution Approach 2:
The registration process is segmented into distinct stages: (1) intra-modality alignment using conventional image registration, (2) inter-modality correspondence establishment through a learned transformation, and (3) cell-level registration. This segmentation allows each stage to be optimized independently, improving overall reliability while enabling cross-modality capability.
2Measurement precision
If conventional image registration techniques are used, then processing can be performed on 2D images, but individual tissue cell identification and classification becomes inaccurate
Solution Approach 1:
The patent transitions from conventional 2D image registration to a multi-dimensional approach that operates at the cell level. By representing cells as graphical objects with properties (position, morphology, staining characteristics) and establishing correspondences between cells across modalities, the system achieves precise cell identification. This dimensional shift from pixel-level to object-level registration significantly improves measurement precision while managing complexity through structured processing.
3Loss of information
If multiple visualization modalities are used to visualize tissue cells, then comprehensive information can be obtained, but the appearance differences make cell recognition challenging
Solution Approach 1:
The system employs feedback mechanisms where the learned transformation model continuously refines cell correspondence between modalities. By iteratively adjusting the transformation parameters based on observed discrepancies and validation data, the system improves its ability to recognize cells across different visual appearances. This feedback loop compensates for the information loss that occurs when viewing cells through different modalities.
Data Source
AI summary
A method implemented by one or more computer devices includes receiving a plurality of images of a set of tissue cells, the plurality of images comprising a first image including a first visualization modality and a second image including a second visualization modality. The method includes identifying a first tissue cell of the set of tissue cells in the first image and the first tissue cell in the second image, and performing a cell-to-cell registration process based on the first tissue cell identified in the first image and the first tissue cell identified in the second image. The cell-to-cell registration process includes matching of the first tissue cell identified in the first image to the first tissue cell identified in the second image. The method includes classifying the first tissue cell into a phenotype based on the cell-to-cell registration process, the phenotype partially indicative of a disease pathology.