3D Organ Modeling via Block Segmentation and Automated Merging
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Solution Overview
Problem
Existing methods for three-dimensionally modeling biological organs through image segmentation require significant manual work and post-processing, leading to inefficiencies and inaccuracies, especially in complex organ structures.
Innovation Solution
The proposed method involves receiving medical image data, setting a region of interest, forming blocks corresponding to these regions, applying segmentation algorithms to each block, and merging the resulting image data to generate a three-dimensional model of the entire organ, thereby minimizing manual intervention and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual work and post-processing are used in traditional image segmentation methods, then modeling accuracy can be maintained through expert adjustment, but the productivity and processing speed are significantly reduced
Solution Approach 1:
The patent divides the organ modeling process into multiple blocks, where each block is processed independently through automated segmentation algorithms. This allows parallel processing of different regions while maintaining consistent segmentation quality, thereby improving both productivity and accuracy without requiring manual intervention for each region.
Solution Approach 2:
The system employs automated segmentation algorithms that perform self-adjustment and optimization without manual intervention. The algorithms automatically identify organ boundaries, handle variations in imaging conditions, and refine segmentation results through iterative processing, replacing traditional manual expert adjustment while maintaining or improving accuracy.
2Reliability
If traditional image segmentation methods are used, then comprehensive organ modeling can be achieved, but the complexity of manual work and post-processing increases significantly
Solution Approach 1:
By dividing the organ into multiple manageable blocks and applying automated segmentation to each, the system reduces the complexity of manual operations. Each block can be processed independently with consistent algorithms, eliminating the need for complex manual coordination across different regions while ensuring comprehensive coverage.
Solution Approach 2:
The patent employs a universal automated segmentation framework that can handle various organ types and imaging modalities through the same core algorithms. This multi-functional approach replaces multiple specialized manual techniques with a single unified system, reducing operational complexity while maintaining comprehensive modeling capability.
3Productivity
If automated algorithms are applied to each block, then productivity and processing speed are improved, but the consistency and accuracy across different blocks may be compromised
Solution Approach 1:
The system applies localized optimization to each block while maintaining global consistency. Automated segmentation algorithms are configured with block-specific parameters that account for local anatomical variations and imaging conditions, ensuring high accuracy in each region. Simultaneously, global constraints and registration procedures ensure consistent integration across all blocks, achieving both speed and consistency.
Solution Approach 2:
The system implements feedback mechanisms where segmentation results from each block are evaluated and used to adjust processing parameters for subsequent blocks. This iterative refinement ensures that consistency issues are detected and corrected during processing, maintaining high modeling accuracy across all blocks while preserving the productivity benefits of automation.
4Productivity
If multiple blocks are processed separately, then processing efficiency is improved through parallel computation, but the merging and integration of blocks requires additional post-processing
Solution Approach 1:
The system performs preliminary registration and coordinate system alignment during the initial setup phase, before actual segmentation processing begins. This preliminary action ensures that blocks are pre-configured for seamless integration, eliminating the need for time-consuming post-processing alignment operations and maximizing the efficiency gains from parallel processing.
Solution Approach 2:
The patent employs automated merging procedures that integrate segmented blocks into a unified organ model through algorithmic registration and surface matching. This automated combining process replaces manual post-processing operations, reducing the time required for integration while ensuring accurate geometric and topological consistency across block boundaries.
Data Source
AI summary
The present disclosure relates to a method for three-dimensionally modeling an organ through image segmentation. The three-dimensional modeling of an organ includes the operations of: receiving one or more pieces of medical image data for a specific bodily organ of a target object; setting a region of interest with respect to the bodily organ based on the one or more pieces of medical image data; forming one or more blocks corresponding to the region of interest, wherein the blocks include a portion of the bodily organ corresponding to the regions of interest; setting a segment algorithm for each of the blocks; generating first image data respectively performing 3D modeling of portions contained in the blocks based on algorithms set to the blocks; and merging the first image data, and generating a three-dimensional section image data with respect to the entire bodily organ.