Automatic LV Border Detection in Echocardiography
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Solution Overview
Problem
Current echocardiographic methods for evaluating left ventricular (LV) function are operator-dependent, time-consuming, and lack automation, particularly in detecting LV borders and assessing segmental wall motion, which are crucial for accurate diagnosis of cardiac diseases.
Innovation Solution
A method for automatic LV inner border detection using a multi-level image map, piece-wise histogram equalization, and polynomial interpolation, which reduces noise and tracks the border across cardiac cycles to calculate ejection fraction and diastolic/systolic function parameters, enabling quantitative evaluation without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracing of LV border is performed by experts, then measurement precision is improved, but productivity deteriorates due to time-consuming operation
Solution Approach 1:
The system performs automatic LV border detection and functional evaluation without requiring manual expert tracing. The algorithm independently processes echocardiographic images to detect borders, calculate volumes, and determine ejection fraction, replacing the need for operator-dependent manual measurements while maintaining clinical diagnostic quality
Solution Approach 2:
The patent replaces manual mechanical tracing operations with automated image processing algorithms. The system uses digital image analysis, edge detection, and contour tracking methods to automatically identify LV borders and compute functional parameters, substituting human expert operations with computational processing
2Productivity
If automatic LV border detection is implemented, then productivity is improved, but measurement precision deteriorates due to noise and false edges
Solution Approach 1:
The patent divides the echocardiographic image into distinct regions using multi-level segmentation techniques. The image is partitioned into different intensity levels to separate the LV cavity from surrounding tissues, allowing the algorithm to identify true borders while excluding false edges caused by speckle noise and artifacts
Solution Approach 2:
The patent introduces intermediate processing steps including noise filtering algorithms and border validation mechanisms. These intermediary processes clean the image data before border detection and verify detected borders against anatomical constraints, preventing false edges from compromising measurement accuracy
3Measurement precision
If quantitative segmental wall motion evaluation is performed, then measurement precision is improved, but device complexity deteriorates
Solution Approach 1:
The patent divides the LV into multiple segmental regions and independently tracks border motion in each segment throughout the cardiac cycle. This segmentation approach enables quantitative assessment of regional wall motion abnormalities while using the same automated detection framework, avoiding the need for complex additional hardware or procedures
Data Source
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AI summary
A method for automatic left ventricular (LV) inner border detection, the method comprising: performing image mapping on an echocardiogram, to produce a multi-level image map; converting the image map into a binary image, by attributing pixels of one or more darker levels of the image map to the LV cavity and pixels of one or more lighter levels of the image map to the myocardium; applying a radial filter to contours of the myocardium in the binary image, to extract an approximate inner border of the LV; and performing shape modeling on the approximate inner border, to determine the LV inner border.