Anatomical Landmark Detection in Medical Imaging
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Solution Overview
Problem
Current medical imaging technologies, such as CT imaging, face challenges in accurately detecting anatomical landmarks necessary for proper patient positioning, leading to potential radiation exposure and reduced image quality due to suboptimal scan angles and extents.
Innovation Solution
A computer-implemented method using machine-learning algorithms to process medical images and generate likelihood indicators for anatomical landmarks, allowing for precise prediction of landmark presence and position, thereby improving image quality and reducing radiation exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional atlas registration technique is used to detect anatomical landmarks, then the system complexity is lower, but the measurement precision and reliability of landmark detection deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/image processing methods (atlas registration) with a machine learning-based detection system. The machine learning model automatically identifies anatomical landmarks by learning from training data, substituting the manual or algorithmic registration process with an intelligent system that achieves higher precision in landmark detection.
2Productivity
If manual landmark identification is used, then the equipment complexity is reduced, but the productivity and measurement precision deteriorate
Solution Approach 1:
The system enables automatic landmark detection and scan parameter determination without requiring manual intervention. The machine learning model processes the medical image autonomously to identify landmarks and calculate optimal scan parameters, making the system self-sufficient in the landmark identification task and significantly improving workflow efficiency.
3Object-affected harmful factors
If scan parameters are not optimized based on accurate landmark detection, then the device operation is simpler, but radiation exposure to sensitive organs increases
Solution Approach 1:
The system uses detected anatomical landmarks as feedback to automatically determine and adjust scan parameters. The machine learning model identifies landmarks such as the eye lenses and brain, then uses this information to calculate optimal scan angles and extents that minimize radiation exposure to sensitive structures while maintaining diagnostic quality.
Data Source
AI summary
A mechanism for identifying a position of one or more anatomical landmarks in a medical image. The medical image is processed with a machine-learning algorithm to generate, for each pixel/voxel of the medical image, an indicator that indicates whether or not the pixel represents part of an anatomical landmark. The indicators are then processed in turn to predict a presence and/or position of the one or more anatomical landmarks.