Shoulder MRI Tear Measurement Using Landmark Regression
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Solution Overview
Problem
Current methods for measuring rotator cuff tears in MRI images, such as manual interpretation by radiologists and existing deep learning approaches, are time-consuming and lack robustness.
Innovation Solution
A regression-based approach using a machine learning model with a DSNT layer to directly calculate the distance between anatomical landmarks, such as the endpoints of a rotator cuff tear, by generating probability maps and determining precise locations through a differentiable spatial to numerical transform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation of MRI imaging is used to determine tear length, then measurement accuracy can be maintained, but the process becomes time-consuming and tedious
Solution Approach 1:
The patent replaces the manual mechanical measurement process with an automated machine learning system. The system uses a trained model to automatically identify anatomical landmarks and calculate tear length from MRI images, eliminating the need for manual radiologist measurement while maintaining accuracy and significantly reducing time requirements.
Solution Approach 2:
The measurement system performs self-service by automatically processing MRI images without requiring manual intervention. The machine learning model independently identifies landmarks, computes distances, and generates measurements, making the system autonomous and eliminating time-consuming manual operations.
2Extent of automation
If segmentation-based deep learning approach is used for automatic measurement, then automation is achieved, but computational expense increases and robustness decreases
Solution Approach 1:
The patent extracts only the essential information needed for measurement by directly predicting anatomical landmark locations rather than performing full segmentation of the entire tendon structure. This extraction approach maintains automation while reducing computational complexity and improving robustness by focusing only on critical measurement points.
Solution Approach 2:
Instead of following the conventional segmentation-based approach (segment entire tendon → extract measurements), the patent inverts the process by directly regressing landmark coordinates from the image. This inverted approach bypasses the computationally expensive segmentation step while achieving the same measurement goal with improved efficiency and reliability.
3Extent of automation
If segmentation-based approach is used, then automatic measurement is achieved, but device complexity and computational cost increase
Solution Approach 1:
The patent replaces the complex multi-step segmentation machinery with a simpler direct regression model. Instead of using elaborate segmentation networks that divide and classify pixel regions, the system uses a streamlined approach that directly predicts landmark coordinates, significantly reducing computational complexity while maintaining automation.
Data Source
AI summary
Systems and methods for calculating a distance between a first point and a second point of an anatomical landmark are provided. An input medical image of an anatomical landmark of a patient is received. One or more probability maps predicting a first point and a second point of the anatomical landmark in the input medical image are generated using a machine learning based model. Locations of the first point and the second point in the input medical image are determined based on the one or more probability maps. A distance between the first point and the second point is calculated based on the locations. The locations of the first point and the second point in the input medical image and/or the calculated distance between the first point and the second point are output.