Hypergraph Fusion for Multi-Modal Brain Atlas Construction
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Solution Overview
Problem
Current medical imaging technologies face challenges in accurately fusing features from different modalities of data due to heterogeneity, leading to inaccurate brain atlas construction and incorrect disease diagnosis.
Innovation Solution
An image-driven brain atlas construction method that extracts non-Euclidean and Euclidean spatial features from multi-modal data and performs hypergraph fusion to acquire complementary fused features, thereby improving the accuracy of brain atlas representation and disease diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If different modalities of data are directly fused by weighted summation, then the fusion process is simple and fast, but the heterogeneity between modalities prevents accurate extraction of fusion features
Solution Approach 1:
The patent introduces a hypergraph as an intermediary structure to bridge different modalities of data. The hypergraph fusion network uses hyperedges to connect nodes from different modalities (Euclidean and non-Euclidean spaces), enabling accurate feature fusion while managing heterogeneity. This intermediary structure allows the system to capture complex relationships without direct weighted summation of heterogeneous data.
Solution Approach 2:
The patent transforms the fusion problem from direct feature space to hypergraph space, adding a new dimensional layer. By mapping features from different modalities into a hypergraph structure with vertices and hyperedges, the system operates in an elevated dimensional space where heterogeneity can be properly handled through hyperedge connections rather than direct feature concatenation or weighted summation.
2Reliability
If heterogeneity between different modalities of data is not addressed, then the processing is straightforward, but accurate brain atlas construction becomes impossible
Solution Approach 1:
The patent segments the feature extraction process into distinct pathways for different modalities. Euclidean spatial features are extracted separately from non-Euclidean spatial features, each through dedicated convolution operations. This segmentation allows each modality to be processed according to its specific characteristics before being integrated in the hypergraph structure, improving reliability while managing complexity through modular processing.
Solution Approach 2:
The patent changes the parameters and representation of features from different modalities to make them compatible for fusion. By transforming raw features into hypergraph representations with specific vertex and hyperedge attributes, the system adjusts feature parameters to a unified framework that can handle heterogeneity, enabling accurate brain atlas construction through standardized hyperedge connections.
3Measurement precision
If weighted summation is used for modal fusion, then computational efficiency is maintained, but the heterogeneity of data prevents accurate disease diagnosis
Solution Approach 1:
The hypergraph serves as an intermediary that enables accurate disease diagnosis by properly fusing heterogeneous modalities. Instead of directly summing weighted features from different modalities, the hypergraph structure with its hyperedges mediates the fusion process, capturing complex relationships and interactions between modalities that are essential for accurate disease classification and diagnosis.
Solution Approach 2:
The patent creates a composite fusion structure by combining Euclidean and non-Euclidean spatial features within a unified hypergraph framework. This composite approach integrates features from multiple modalities (sMRI, DTI, fMRI) into a single hypergraph representation, where different feature types coexist and interact through hyperedge connections, enabling accurate disease diagnosis through the combined power of heterogeneous data.
Data Source
AI summary
The present application is applicable to the field of medical imaging technologies, and provides an image-driven brain atlas construction method and apparatus, a device and a storage medium. The method includes: acquiring multi-modal data of a brain to be predicted, where the multi-modal data is acquired according to image data collected when the brain is under at least three different modalities; inputting the multi-modal data into a preset fusion network for processing to output and acquire feature parameters of the brain; where the processing of the multi-modal data by the fusion network includes: extracting a non-Euclidean spacial feature and an Euclidean spacial feature of the multi-modal data, and performing hypergraph fusion on the non-Euclidean spacial feature and the Euclidean spacial feature to acquire the feature parameters, where the feature parameters are used to characterize a brain connection matrix and/or a disease category of the brain.