Deformable Model Adaptation via Selective Image-Driven Elements
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Solution Overview
Problem
Deformable models used for image segmentation often exhibit problematic areas where the distance between the model's surface and the accurate reference segmentation is significantly higher than the mean distance, indicating inefficiencies in adaptation to image data.
Innovation Solution
An adaptation system that selectively chooses image-driven model elements, optimizing the model energy by balancing internal and external energies, allowing exclusion of poorly adaptable elements and iterative refinement to improve fit with image data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all model elements are made image-driven to improve adaptation to image data, then the model can fit image features better, but problematic areas with large distances from reference segmentation increase
Solution Approach 1:
The patent applies local quality by differentiating between image-driven and non-image-driven model elements based on their local adaptability. Model elements are classified into different types (fully image-driven, partially image-driven, non-image-driven) according to their local image quality and adaptability characteristics. This allows each region of the model to have appropriate driving forces - regions with good image quality use image-driven adaptation while regions with poor image quality rely more on model constraints, thereby resolving the contradiction between local adaptability and overall precision.
2Productivity
If image-driven forces are applied to all model elements to improve segmentation accuracy, then adaptation speed increases, but reliability decreases due to problematic areas
Solution Approach 1:
The patent implements dynamics by making the driving force of model elements dynamic rather than static. The degree of image-driven adaptation for each model element is adjusted dynamically based on local image quality metrics and adaptation progress. Model elements can transition between different driving force types during the adaptation process, allowing the system to maintain high productivity in well-defined regions while ensuring reliability in ambiguous regions through adaptive reconfiguration of driving forces.
3Manufacturing precision
If selective adaptation of model elements is implemented to reduce problematic areas, then segmentation precision improves, but system complexity increases
Solution Approach 1:
The patent applies parameter changes by introducing a driving force type parameter for each model element that can take different values (fully image-driven, partially image-driven, non-image-driven). This parameter is determined based on local image quality metrics and adaptation state, allowing the system to manage complexity through parameter-based classification rather than complex structural modifications. The parameter changes enable automatic adaptation strategies that improve precision without requiring manual configuration of each model element.
Data Source
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AI summary
The invention relates to an adaptation system (200) for adapting a deformable model comprising a plurality of model elements to an object of interest in an image data set, said adaptation system (200)comprising a selector (220) for selecting at least one image- driven model element from the plurality of model elements and an adapter (230) for adapting the deformable model on the basis of optimizing a model energy of the deformable model, said model energy comprising an internal energy of the plurality of model elements and an external energy of the at least one image-driven model element, thereby adapting the deformable model. By enabling the adaptation system (200) to selectively choose the image- driven model elements, the adaptation system of the current invention allows excluding a poorly adaptable model element from interacting with the image data set and thus from being pulled and/or pushed by the image data set into a wrong location.