Brain Grey Matter Parcellation Using Tractogram Cluster Merging
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Solution Overview
Problem
Current methods for parceling grey matter of a human brain lack accuracy and efficiency, often resulting in overly granular and biologically irrelevant subdivisions of grey matter regions.
Innovation Solution
A computer-implemented method using a predetermined clustering algorithm to identify tractogram streamline clusters, followed by an iterative merging process based on a metric quantifying overlap, to determine a biologically accurate parcellation of grey matter regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If current parceling methods are used to divide grey matter, then the grey matter is subdivided into many small regions, but the subdivisions become overly granular and biologically irrelevant
Solution Approach 1:
The patent applies merging by combining overlapping grey matter regions identified from different tractogram streamline clusters. The iterative merging process merges pairs of regions based on a metric quantifying their overlap, consolidating overly granular subdivisions into coherent, biologically relevant parcels while maintaining accurate anatomical representation.
2Reliability
If traditional clustering methods are used for tractogram streamlines, then the processing is simpler, but the resulting parcellation lacks biological accuracy
Solution Approach 1:
The patent implements feedback through an iterative merging process that continuously evaluates region overlaps using a quantifying metric. The process repeatedly merges overlapping regions and re-evaluates until convergence, ensuring that the final parcellation achieves biological accuracy by systematically eliminating spurious subdivisions while maintaining anatomical validity.
3Measurement precision
If more detailed tractogram analysis is performed, then the parcellation accuracy improves, but the computational time and resources increase
Solution Approach 1:
The patent applies segmentation by dividing the complex parcellation task into distinct stages: initial region identification from tractogram streamlines, iterative merging of overlapping regions, and final parcellation determination. This segmented approach enables systematic processing of detailed tractogram data while managing computational complexity through structured, incremental refinement.
Data Source
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AI summary
The disclosure notably relates to a computer-implemented method for parceling grey matter of a human brain of a human patient comprising obtaining a tractogram including tractogram streamlines, each having a first extremity located in a first portion of grey matter of a second extremity located in a second portion of grey matter. The parceling method also comprises using a predetermined clustering algorithm to obtain tractogram streamline clusters, and, for each cluster of at least a part of the clusters, identifying a respective first region of grey matter including for each streamline of the cluster its first extremity, and a respective second region of grey matter including for each streamline of the cluster its second extremity. The parceling method also comprises determining a parcellation based on the identified regions, including an iterative merging process which includes merging pairs of regions based on a metric quantifying an overlap.