Deformable Model Brain Structure Segmentation
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Solution Overview
Problem
Current methods face challenges in accurately identifying and quantifying structural atrophy in sub-cortical brain structures following Traumatic Brain Injury (TBI) due to methodological limitations, hindering the understanding of neuropathology in 3D imaging.
Innovation Solution
A system and method for automatic segmentation of brain structures using a deformable model adapted to volumetric images, such as MRI or ultrasound, which selects and deforms a surface mesh model to accurately delineate structures like the corpus callosum, hippocampus, cerebellum, thalamus, and caudate, allowing for user input and analysis of deformation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual segmentation methods are used to identify brain structures, then flexibility and adaptability are maintained, but time consumption and productivity are excessive
Solution Approach 1:
The patent applies preliminary action by pre-defining anatomical models of brain structures (corpus callosum, hippocampus, cerebellum, thalamus, caudate) with known geometric characteristics and spatial relationships. These pre-defined models are prepared in advance and then automatically adapted to individual patient images, eliminating the need for manual segmentation while maintaining anatomical accuracy.
Solution Approach 2:
The patent uses copying by creating digital replicas of anatomical structures through deformable models that are adapted to match patient-specific imaging data. These virtual models are copied from standardized anatomical templates and then deformed to fit the unique geometry of each patient's brain structures, enabling automated quantification without manual intervention.
2Productivity
If automated segmentation is implemented to improve productivity, then time consumption is reduced, but measurement precision and reliability may deteriorate
Solution Approach 1:
The patent implements feedback through an iterative optimization process where the deformable models are continuously adjusted based on image data. The system compares the model predictions with actual imaging data, calculates energy minimization criteria, and refines the model adaptation until convergence is achieved, ensuring high measurement precision while maintaining automated efficiency.
Solution Approach 2:
The patent applies dynamics by using deformable models that can dynamically adapt their shape and geometry to match patient-specific anatomy. The models are not rigid templates but flexible representations that can deform within anatomically plausible boundaries, allowing automated segmentation to achieve high precision by adapting to individual variations in brain structure geometry.
3Measurement precision
If detailed anatomical modeling is performed to improve measurement precision, then segmentation accuracy is enhanced, but device complexity and computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the complex task of brain structure analysis into separate deformable models for each major structure (corpus callosum, hippocampus, cerebellum, thalamus, caudate). Each model is independently adapted to the imaging data, which simplifies the computational complexity compared to attempting to segment all structures simultaneously, while still achieving high measurement precision for each individual structure.
Data Source
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AI summary
A system and method for automatic segmentation, performed by selecting a deformable model of an anatomical structure of interest imaged in a volumetric image, the deformable model formed of a plurality of polygons including vertices and edges, displaying the deformable model on a display, detecting a feature point of the anatomical structure of interest corresponding to each of the plurality of polygons and adapting the deformable model by moving each of the vertices toward the corresponding feature points until the deformable model morphs to a boundary of the anatomical structure of interest, forming a segmentation of the anatomical structure of interest.