Scapular Landmark Analysis for Accurate Chest Rotation Imaging
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Solution Overview
Problem
Existing methods for determining patient chest rotation in medical images, such as chest posteroanterior radiographs, are inadequate, particularly in cases of scoliosis, as they rely on landmarks like clavicle tips and spinous processes, which can be unreliable indicators of overall chest rotation.
Innovation Solution
A computer-implemented method using scapular spatial data to determine chest rotation by confirming scapular symmetry and calculating geometric or empirical rotations based on scapulae landmarks, employing convolutional neural networks for image segmentation and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If landmark-based methods (clavicle tips and spinous processes) are used to determine chest rotation, then the measurement process is simple, but the measurement precision deteriorates in cases of scoliosis
Solution Approach 1:
The patent extracts the scapulae from the complex anatomical structures and uses them as the primary reference for rotation measurement. By focusing specifically on the scapulae's position and orientation relative to the rib cage, the method isolates a more reliable indicator of chest rotation that is not affected by spinal deformities like scoliosis.
Solution Approach 2:
The patent changes the measurement parameter from traditional landmarks (clavicle tips and spinous processes) to scapular parameters (position and orientation of scapulae relative to rib cage). This parameter change enables accurate rotation measurement even in pathological conditions where traditional landmarks become unreliable.
2Device complexity
If traditional landmark methods are used, then device complexity is low, but reliability of rotation detection deteriorates in severe scoliosis cases
Solution Approach 1:
The patent introduces scapular spatial data as an intermediary between the image data and the rotation measurement. The scapulae serve as a mediator that provides a stable reference frame for determining chest rotation, bridging the gap between complex anatomical variations and reliable measurement.
Solution Approach 2:
The patent replaces the mechanical landmark-based measurement system with an image processing system that uses convolutional neural networks to extract scapular spatial data. This substitution enables automated, reliable rotation detection that is not dependent on manual landmark identification.
3Measurement precision
If scapular spatial data processing is implemented to confirm scapular symmetry, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent performs preliminary processing of scapular spatial data to confirm scapular symmetry before final rotation determination. This preliminary action involves checking whether the scapulae are symmetrically positioned, which validates the measurement process and ensures reliable results before proceeding to calculate the rotation angle.
Solution Approach 2:
The patent implements a feedback mechanism where scapular symmetry is checked and used to validate the rotation measurement process. The symmetry confirmation provides feedback on whether the measurement conditions are appropriate, allowing the system to adjust or reject measurements that do not meet quality criteria.
Data Source
AI summary
There is provided a computer-implemented method for determining rotation of a patient's chest in a medical image. The method comprises: receiving the medical image of the patient; processing the medical image to determine scapular spatial data relating to the patient's scapula; and determining rotation of the patient's chest with respect to at least one reference axis using the scapular spatial data.