3D Cerebrovascular Chunk Classification for Accurate Condition Evaluation
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Solution Overview
Problem
Existing methods struggle to accurately classify and identify cerebrovascular structures due to complexity and inter-individual differences, limiting effective diagnosis and treatment of cerebrovascular diseases.
Innovation Solution
A method and device that analyze cerebrovascular images by extracting vascular unit structures based on geometric features, using a learning model to classify chunks and evaluate conditions, employing techniques such as DNNs and ensemble models for precise chunk classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image-based methods are used to analyze cerebrovascular structures, then the analysis process is simple, but the classification accuracy is low due to complexity and inter-individual differences
Solution Approach 1:
The patent segments the cerebrovascular image into multiple vascular unit structures (chunks) based on geometric features and 3D modeling. Each chunk represents a discrete vascular segment that can be independently analyzed. This segmentation transforms the complex overall vascular structure into manageable units, enabling more accurate classification while maintaining analytical tractability
Solution Approach 2:
The patent introduces geometric features and 3D model-based vascular unit structures as intermediary representations between the raw image data and the final classification. These intermediaries capture essential structural characteristics while filtering out irrelevant variations, thereby improving classification accuracy without requiring direct analysis of the complex original images
2Measurement precision
If detailed geometric feature extraction is performed on vascular unit structures, then classification accuracy improves, but processing time increases
Solution Approach 1:
By dividing the vascular structure into pre-defined chunk units with specific geometric characteristics, the patent enables parallel processing of multiple independent segments. This segmentation allows the system to extract geometric features from each chunk independently and simultaneously, reducing overall processing time while maintaining detailed feature analysis for accurate classification
Solution Approach 2:
The patent performs preliminary 3D modeling and vascular unit structure extraction before detailed geometric feature analysis. By pre-processing the image to identify and segment vascular chunks in advance, the system prepares structured data that facilitates faster subsequent feature extraction and classification, reducing the time required for detailed geometric analysis
Data Source
AI summary
A method of analyzing a cerebrovascular image based on cerebrovascular chunk features is disclosed, the method including receiving, by an analysis device, a cerebrovascular image of a subject; extracting, by the analysis device, a plurality of vascular unit structures from the cerebrovascular image based on geometric features of a 3D model; extracting, by the analysis device, feature values for each of the plurality of vascular unit structures; inputting, by the analysis device, the feature values of each of the plurality of vascular unit structures into a learning model trained in advance, classifying chunks to which each of the plurality of vascular unit structures belongs, and generating chunk features for the cerebrovascular image; and evaluating, by the analysis device, a condition of the subject based on the chunk features.