Anatomical Landmark Detection in Deformed Radiography
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Solution Overview
Problem
Current medical imaging techniques, particularly digital radiography, face challenges in accurately detecting anatomical landmarks due to artifacts such as motion and exposure issues, which are exacerbated by severe skeletal deformations, leading to inaccurate diagnoses and the need for manual, time-consuming, and subjective expert analysis.
Innovation Solution
A processor-implemented method using trained neural networks to filter artifacts and generate heat maps for identifying anatomical landmarks, with a classifier determining probability scores and fine-tuning or realigning landmarks based on pre-defined thresholds, leveraging domain knowledge and 3D-2D deformable models to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital radiography images are obtained from abnormal subjects with severe skeletal deformations, then diagnostic information can be acquired, but artifact presence and deformation make landmark detection extremely difficult
Solution Approach 1:
The patent segments the landmark detection task into multiple specialized neural networks: a first neural network for artifact removal, a second neural network for heat map generation, and a trained classifier for probability scoring. This segmentation allows each component to specialize in one aspect of the challenging detection task, improving overall precision despite the difficult imaging conditions
Solution Approach 2:
The patent applies preliminary action by first filtering artifacts from the DR images using a trained neural network before proceeding to landmark detection. This pre-processing step removes motion artifacts and exposure issues that would otherwise interfere with accurate landmark identification in deformed anatomical structures
2Measurement precision
If traditional manual landmark identification by experts is performed, then detection can be done, but the process is exhaustive, time-consuming and subjective leading to observer errors
Solution Approach 1:
The patent implements self-service by using trained neural networks and classifiers that automatically perform landmark detection without requiring manual expert intervention. The system self-corrects for artifacts and deformations through the trained models, eliminating observer subjectivity and reducing time consumption while maintaining or improving detection accuracy
Solution Approach 2:
The patent replaces the mechanical process of manual expert analysis with an automated computational system using neural networks and classifiers. This substitution eliminates the subjective and time-consuming nature of manual landmark identification while providing consistent, objective results
3Reliability
If imaging process is repeated to avoid motion artifacts, then image quality may be improved, but patient is exposed to higher radiation risk
Solution Approach 1:
The patent converts the harmful effect of motion artifacts into a benefit by training neural networks to specifically recognize and remove these artifacts from single images. Instead of requiring repeated imaging to avoid motion, the system learns to identify and eliminate motion-induced distortions, improving image reliability while minimizing radiation exposure
4Adaptability or versatility
If standard atlas models are used for landmark detection, then detection can be performed under normal cases, but these models cannot be adapted directly for abnormal cases with deformations
Solution Approach 1:
The patent applies dynamics by using a deformable model that can adapt its structure to match the specific anatomical variations present in each patient's image. Rather than using a fixed standard atlas, the model dynamically adjusts to accommodate skeletal deformations and abnormalities, maintaining detection accuracy across diverse pathological conditions
Data Source
AI summary
Conventionally, systems and methods have been provided for manual annotation of anatomical landmarks in digital radiography (DR) images. Embodiments of the present disclosure provides system and method for anatomical landmark detection and identification from DR images containing severe skeletal deformations. More specifically, motion artefacts and exposure are filtered from an input DR image to obtain a pre-processed DR image and probable/candidate anatomical landmarks comprised therein are identified. These probable candidate anatomical landmarks are assigned a score. A subset of the candidate anatomical landmarks (CALs) is selected as accurate anatomical landmarks based on comparison of the score with a pre-defined threshold performed by a trained classifier. Position of remaining CALs may be fine-tuned for classification thereof as accurate anatomical landmarks or missing anatomical landmarks. The CALs may be further fed to the system for checking misalignment of any of the CALs and correcting the misaligned CALs.