Muscle Force Potential Estimation From Segmented Patient Images
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Solution Overview
Problem
Existing technologies lack effective methods for estimating the characteristics and properties of soft-tissue structures, such as muscle force potential, passive tension, and muscle quality, which are crucial for pre-operative planning and post-operative rehabilitation.
Innovation Solution
A method implemented on an electronic computing device for estimating these properties using image segmentation and machine learning models, such as CNNs and SSMs, to analyze patient images and derive characteristics like muscle volume, pennation angle, and passive tension, enabling precise surgical planning and rehabilitation guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image segmentation and machine learning models are used to estimate soft-tissue properties, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies image segmentation to divide the patient image into distinct soft-tissue structures, allowing separate analysis and estimation of properties for each segmented region. This enables precise measurement of specific soft-tissue characteristics while managing computational complexity through focused processing.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that bridge the gap between raw image data and soft-tissue property estimation. These models process segmented images and output biomechanical properties, improving measurement precision while encapsulating complexity within the model layer.
2Loss of information
If detailed soft-tissue characteristic estimation is performed, then information availability is improved, but processing time increases
Solution Approach 1:
The patent performs image segmentation and preliminary feature extraction before final property estimation. By preparing and segmenting the images in advance, the system reduces processing time during the actual estimation phase while maintaining complete information through thorough segmentation.
Solution Approach 2:
The patent extracts key features and segmented soft-tissue structures from the patient images, separating essential information from redundant data. This extraction process maintains information completeness by capturing all necessary soft-tissue characteristics while reducing the computational burden for final property estimation.
Data Source
AI summary
A method implemented on an electronic computing device for estimating a force potential of a muscle or muscle group is described. The method comprising: receiving an image of a patient, the image depicting at least a portion of a muscle or muscle group; segmenting the image of the patient to produce a segmented patient image, the segmented patient image comprising at least one segmented muscle or segmented muscle group; and estimating a muscle force potential for the segmented muscle or segmented muscle group, wherein the estimated muscle force potential is based at least partially on the segmented patient image.