Tractogram Streamline Segmentation Using Clustering
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Solution Overview
Problem
Current methods for segmenting human patient tractograms into white matter streamline bundles face issues such as erroneous cutting, noise in acquisition, and dimensionality problems, leading to inaccurate anatomical analysis and high computational costs.
Innovation Solution
A computer-implemented method that attributes tractogram streamlines to white matter atlas bundles using a clustering algorithm, selecting sets based on proximity criteria and centroid computations to ensure anatomical accuracy and robustness against noise, while reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all tractogram streamlines are analyzed to ensure anatomical accuracy, then measurement precision is improved, but computational cost increases significantly
Solution Approach 1:
The patent segments the tractogram streamlines into multiple groups based on their spatial proximity and anatomical characteristics. By dividing the large set of streamlines into manageable segments, the system can process them in batches rather than all at once, reducing computational load while maintaining comprehensive anatomical analysis coverage.
Solution Approach 2:
The patent creates a virtual copy of the tractogram streamlines organized into bundles, allowing the system to perform repeated analyses on the bundled structure without reprocessing the entire original dataset each time. This enables efficient iterative refinement of anatomical accuracy metrics.
2Measurement precision
If tractogram streamlines are processed to maintain anatomical accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the complex task of tractogram analysis into discrete, manageable segments by creating streamline bundles. Each bundle can be processed independently using standardized algorithms, simplifying the overall system architecture compared to handling all streamlines as a single complex dataset.
Solution Approach 2:
The patent transforms the tractogram data by changing parameters such as organizing streamlines into bundles based on spatial proximity and anatomical characteristics. This parameter transformation simplifies the data structure and enables the use of simpler, more efficient analysis algorithms while maintaining anatomical accuracy.
3Productivity
If clustering algorithms are applied to segment streamlines into bundles, then productivity is improved, but measurement precision may be compromised
Solution Approach 1:
The patent performs preliminary organization of streamlines into bundles based on spatial proximity and anatomical characteristics before conducting detailed anatomical analysis. This preliminary action groups similar streamlines together, enabling more efficient batch processing while ensuring that anatomical accuracy is maintained within each bundle through subsequent verification steps.
Solution Approach 2:
The patent incorporates feedback mechanisms that allow the system to review and adjust the streamline bundle assignments. By comparing the clustered bundles against anatomical expectations and allowing for iterative refinement, the system ensures that productivity gains from clustering do not compromise anatomical accuracy.
Data Source
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AI summary
The disclosure notably relates to a computer-implemented method for segmenting of a human patient tractogram into one or more white matter streamline bundles, comprising obtaining a tractogram of a human patient, the tractogram including tractogram streamlines, and a white matter atlas including one or more bundles each including respective atlas streamlines. The method also comprises, for at least one bundle of the atlas and its respective atlas streamlines, attributing, to the at least one bundle, respective tractogram streamlines, the respective tractogram streamlines including one or more first sets each of at least one tractogram streamline, each first set corresponds to a respective set of at least one atlas streamline of the at least one bundle, and the respective tractogram streamlines further including one or more second sets each of at least one tractogram streamline, each second set corresponds to a respective sectional portion of a respective set of at least one atlas streamline of the at least one bundle.