Multimodal Graph Construction for Tissue Image Alignment
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Solution Overview
Problem
Current methods for processing multimodal images of tissue for medical evaluation face challenges such as alignment issues between differently-stained pathology slides, which hinder the capture of spatial and contextual information, and are computationally intensive, especially with high-resolution whole-slide images.
Innovation Solution
A computer-implemented method generates a multimodal graph by detecting biological entities, creating entity graphs, selecting anchor elements, and constructing anchor graphs across images, allowing for the interconnection of anchor nodes to encode correspondence between different image modalities without the need for image registration, thereby providing a context-aware representation of multimodal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image registration techniques are employed to transform images into alignment, then spatial correspondence between multimodal images is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent segments the complex image registration task into independent entity detection and graph construction steps. Biological entities are detected separately in each modality, then connected through entity graphs that preserve spatial relationships without requiring full image alignment. This segmentation eliminates the need for computationally intensive global registration while maintaining local spatial correspondence.
Solution Approach 2:
The patent introduces entity graphs as an intermediary representation between multimodal images. Instead of directly aligning images, the system detects biological entities in each modality, constructs entity graphs that encode spatial relationships, and then integrates these graphs. This intermediary approach preserves spatial correspondence information without requiring the images themselves to be registered.
2Measurement precision
If fine-grained registration of tissue is performed to align multimodal images, then alignment precision is improved, but biological entities such as cells are distorted and vital information is changed in irreversible ways
Solution Approach 1:
The patent segments the tissue into discrete biological entities (cells, nuclei, etc.) that are detected independently in each modality. By working with these segmented entities rather than continuous image data, the system avoids the distortion problems of fine-grained registration. Each entity's properties are preserved from its source image, and spatial relationships are reconstructed through graph connections.
Solution Approach 2:
The patent creates copies of biological entities in the form of entity nodes within entity graphs. These graph-based representations capture the essential features and spatial relationships of biological entities without requiring the original images to be transformed or distorted. The entity graphs serve as faithful copies that preserve vital biological information while enabling multimodal integration.
3Measurement precision
If image registration is performed on whole-slide images with multi-gigapixel resolution, then alignment between images is achieved, but computational resources and processing time become excessively high
Solution Approach 1:
The patent extracts only the essential information from whole-slide images - specifically, biological entities and their spatial relationships - rather than processing the entire high-resolution images. By detecting entities and constructing compact entity graphs, the system reduces the data volume from multi-gigapixel images to manageable graph structures, dramatically improving processing efficiency while maintaining alignment capability.
Solution Approach 2:
The patent segments the whole-slide image analysis into independent entity detection tasks followed by graph construction. This segmentation allows parallel processing of entity detection across different image regions and modalities, avoiding the need for sequential registration of entire gigapixel images. The entity graphs provide a compressed representation that enables efficient multimodal integration.
4Device complexity
If independent analysis of different-modality images is performed with results combined later, then processing complexity for each image is reduced, but spatial and contextual information across multimodal images cannot be captured
Solution Approach 1:
The patent merges independent entity graphs from different modalities into a unified multimodal entity graph. Each modality is processed independently to construct its entity graph, preserving simplicity in individual processing. The graphs are then merged by connecting corresponding entity nodes, which restores spatial and contextual relationships across modalities. This merging approach captures cross-modal spatial information without increasing individual processing complexity.
Solution Approach 2:
The patent uses entity graphs as intermediary structures that bridge independent modality analyses. Each modality produces an entity graph that encodes spatial relationships within that modality. These graphs serve as intermediaries that can be integrated to recover cross-modal spatial context, solving the problem of information loss while keeping individual processing steps simple and independent.
Data Source
AI summary
Methods and systems are provided for processing different-modality digital images of tissue. The method includes, for each image, detecting biological entities in the image and generating an entity graph comprising entity nodes, representing respective biological entities, interconnected by edges representing interactions between entities represented by the entity nodes. The method also includes selecting, from each image, anchor elements comprising elements corresponding to anchor elements of at least one other image, and generating an anchor graph in which anchor nodes, representing respective anchor elements, are interconnected with entity nodes of the entity graph for the image by edges indicating relations between entity nodes and anchor nodes. The method further includes generating a multimodal graph by interconnecting anchor nodes of the anchor graphs for different images via correspondence edges indicating correspondence between anchor nodes, and processing the multimodal graph to output multimodal data, derived from the plurality of images, for medical evaluation.