Statistical Shape Modeling for Pre-Morbid Anatomical Characterization
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Solution Overview
Problem
Existing surgical joint repair procedures face challenges in accurately determining pre-morbid characteristics of patient anatomy due to damage or disease progression, which affects proper prosthetic selection and positioning, and are hindered by issues like under- and over-segmentation in imaging data.
Innovation Solution
The use of statistical shape modeling (SSM) to determine a pre-morbid shape of anatomical objects by aligning segmented shapes to an initial shape model through iterative processes like ICP and elastic registration, despite under- and over-segmentation, to generate a pre-morbid approximation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If imaging data is used to determine current anatomy, then anatomical information is obtained, but damage or disease progression causes loss of portions of the anatomical objects that would be desirable for generating pre-morbid characterization
Solution Approach 1:
The system performs preliminary action by using statistical shape models to predict what the anatomy looked like before damage occurred. Instead of waiting for complete anatomical data that no longer exists, the system proactively reconstructs the pre-morbid state by combining available fragmented imaging data with population-based shape statistics, allowing surgical planning to proceed as if the original anatomy were intact.
Solution Approach 2:
The statistical shape model acts as an intermediary between the damaged current anatomy and the desired pre-morbid characterization. The SSM serves as a mediator that bridges the gap by using population statistics to fill in missing information, transforming incomplete current anatomical data into a comprehensive pre-morbid representation that guides prosthetic selection and surgical planning.
2Manufacturing precision
If over-segmentation or under-segmentation occurs in imaging, then imaging data is lost, but accurate pre-morbid characterization requires complete anatomical information
Solution Approach 1:
The system employs feedback mechanisms through iterative optimization where the statistical shape model continuously refines the pre-morbid characterization by comparing predicted anatomy against the available segmented imaging data. The model adjusts its reconstruction to maximize consistency with observed data while maintaining anatomical plausibility, effectively compensating for segmentation errors through repeated refinement cycles.
Solution Approach 2:
The system changes parameters by utilizing statistical variations from population-based shape models to adjust and complete anatomical structures that are incompletely segmented. By leveraging statistical distributions of anatomical parameters from healthy populations, the system can reconstruct missing or missegmented portions by sampling from appropriate statistical parameters, effectively correcting segmentation deficiencies.
3Measurement precision
If traditional imaging methods are used, then current damaged anatomy is visualized, but pre-morbid characteristics are not accurately determined for prosthetic selection
Solution Approach 1:
The system creates a copy of the pre-morbid anatomy through statistical shape modeling rather than relying on direct imaging of the damaged structure. By generating a statistical representation of what the anatomy likely looked like before injury based on population data and available fragments, the system produces a usable anatomical model for surgical planning without requiring complex attempts to reconstruct or guess the original state from damaged images alone.
Data Source
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AI summary
Techniques are described for determining a pre-morbid shape of an anatomical object. Processing circuitry may determine an aligned shape based on image data with under- or over-segmentation. The processing circuitry may utilize the aligned shape and a shape model such as a mean shape model of the anatomical object to register the aligned shape to the shape model and generate information indicative of the pre-morbid shape of the anatomical object.