3D Cerebral Hemorrhage Volume Calculation via CT Value Shift Correction
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Solution Overview
Problem
Current methods for calculating 3D volume of cerebral hemorrhage from X-ray CT images are either labor-intensive and skill-dependent (semi-automatic) or inaccurate (fully automatic), and both ignore partial volume effects and CT value shifts, leading to inconsistent results across different cerebral sections.
Innovation Solution
A method and apparatus that set ROIs for normal cerebral parenchymal and hemorrhage lesions, adjust CT values based on mean shifts, and calculate 3D volume using a specific equation, eliminating bone and foreign parts to achieve accurate and user-independent results across all cerebral sections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semi-automatic method with specialist involvement is used, then segmentation accuracy is improved, but labor and time consumption increase
Solution Approach 1:
The system performs preliminary automated segmentation to generate an initial result, which is then refined by the specialist only where needed. This preliminary action reduces the overall time consumption while maintaining high accuracy through selective manual intervention.
Solution Approach 2:
The system incorporates feedback mechanisms where specialist corrections are used to improve and refine the automated segmentation algorithm. This feedback loop enables the system to learn from manual corrections and improve its automated performance over time, reducing both time and labor requirements.
2Productivity
If fully automatic method is used, then productivity is improved, but segmentation accuracy deteriorates
Solution Approach 1:
The fully automatic method is divided into distinct functional modules: initial segmentation, CT value shift correction, partial volume effect compensation, and quality assessment. This modular segmentation allows each component to be optimized independently, maintaining high productivity while improving overall accuracy through systematic processing.
Solution Approach 2:
The system dynamically adjusts segmentation parameters based on image characteristics and CT value distributions. By changing parameters adaptively rather than using fixed thresholds, the system maintains high automation efficiency while achieving accurate segmentation across varying image conditions.
3Device complexity
If CT value shift among images is ignored, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary detection of CT value shifts across images before proceeding with volume calculation. This preliminary action identifies the need for correction without adding significant complexity to the overall process, ensuring measurement precision is maintained.
Solution Approach 2:
The system replaces complex manual calibration procedures with automated CT value shift detection and correction algorithms. This substitution maintains measurement precision while simplifying the operational process and reducing the need for complex device adjustments.
4Device complexity
If partial volume effect is ignored, then calculation complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system incorporates partial volume effect compensation by adjusting calculation parameters based on local image characteristics. This approach accounts for partial volume effects through parameter modification rather than complex additional processing, maintaining measurement accuracy while controlling calculation complexity.
Data Source
AI summary
The present invention provides a method and apparatus for appropriately calculating 3D volume of cerebral hemorrhage lesion. The present invention sets a ROI for normal cerebral parenchymal region and a ROI for cerebral hemorrhage lesion respectively for a plurality of continuous slice images obtained by imaging the head having cerebral hemorrhage onset with an X-ray CT device, determines a mean value of the CT values of pixels within the ROI of normal cerebral parenchymal region for each of a plurality of images, then determines an amount of shift of mean values of images based on the mean value of one image, adjusts the CT value of pixels in the ROI of cerebral hemorrhage lesion for each image by using the amount of shift for each image, specifies the maximum value of the adjusted CT value through a plurality of images, and then calculates the 3D volume of the cerebral hemorrhage lesion.