Cardiac Valve Contour Extraction from Volume Data
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Solution Overview
Problem
Current methods for extracting the contour of cardiac valves from three-dimensional information are inefficient in capturing the entire contour with high precision, particularly in identifying boundary lines between anatomical parts essential for diagnosis.
Innovation Solution
An image processing device that detects feature points in volume data, extracts contours of cardiac valve parts based on anatomical definitions, optimizes and combines these contours to create a precise measurement object contour, and acquires necessary diagnostic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the whole contour of the cardiac valve is extracted as one shape from volume data, then the extraction process is simplified, but the precision and accuracy of the contour extraction deteriorates
Solution Approach 1:
The cardiac valve is divided into multiple anatomical parts (anterior leaflet, posterior leaflet, septal leaflet, lateral leaflet) based on anatomical definitions. Each part is extracted separately with its own contour, allowing for precise identification of boundary lines between parts while maintaining systematic processing. This segmentation resolves the contradiction by enabling both structured extraction and high precision.
2Measurement precision
If manual extraction of cardiac valve contour is performed, then the extraction can be customized, but the time required and operational complexity increases significantly
Solution Approach 1:
Anatomical definitions and part classification criteria are predetermined and stored in the system. The automatic extraction process applies these pre-established rules to identify and extract contours of different valve parts, eliminating the need for manual customization while maintaining accuracy. This preliminary preparation enables fast automatic extraction without sacrificing precision.
3Device complexity
If boundary lines between anatomical parts are not extracted, then the processing is simpler, but the diagnostic information completeness deteriorates
Solution Approach 1:
The system applies different processing qualities to different regions of the cardiac valve. Boundary lines between anatomical parts are extracted with high precision where diagnostically important, while other regions are processed according to their specific requirements. This localized approach ensures complete diagnostic information is captured without uniformly increasing overall processing complexity.
Data Source
AI summary
Volume data is used for extracting a contour of a measurement object and measurement information describing anatomical structure useful for diagnosis is acquired from the contour. When volume data of a subject is inputted (S301), an image processing device detects feature points in the volume data (S302); detects contours of a plurality of parts in the volume data based on the detected feature points and anatomical definitions (S303); and optimizes boundary lines defining contours of parts contacting each other, out of the detected plural parts, so as to combine together the optimized contours of the plural parts for creating a contour of the measurement object (S304). Measurements are taken on diagnostic items useful for diagnosis based on the created contour (S305); and the acquired measurement information is outputted as measurement results (S306) which are displayed at a display.