Vessel segmentation for X-separation

2025-02-03

AI Summary

Vascular artifacts in χ-separation quantitative susceptibility mapping (QSM) confound accurate quantification of iron and myelin. Method: We propose a three-stage vascular segmentation framework: (1) novel generation of high-specificity seed points by fusing R₂* and the product of paramagnetic and diamagnetic susceptibility (χₚₐᵣₐ·|χdᵢₐ|); (2) region-growing guided by geometric vascular priors; and (3) morphological refinement to exclude non-vascular structures. Contribution/Results: The method significantly improves vascular mask specificity and robustness, achieving an average 12.6% Dice score improvement over conventional approaches across multicenter data. When integrated into χ-sepnet-R₂* quantification and group-level ROI analysis, it effectively eliminates vascular bias, yielding more accurate iron/myelin distribution measurements and revealing statistically significant, pathology-relevant regional differences.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

$chi$-separation is an advanced quantitative susceptibility mapping (QSM) method that is designed to generate paramagnetic ($chi_{para}$) and diamagnetic ($|chi_{dia}|$) susceptibility maps, reflecting the distribution of iron and myelin in the brain. However, vessels have shown artifacts, interfering with the accurate quantification of iron and myelin in applications. To address this challenge, a new vessel segmentation method for $chi$-separation is developed. The method comprises three steps: 1) Seed generation from $ extit{R}_2^*$ and the product of $chi_{para}$ and $|chi_{dia}|$ maps; 2) Region growing, guided by vessel geometry, creating a vessel mask; 3) Refinement of the vessel mask by excluding non-vessel structures. The performance of the method was compared to conventional vessel segmentation methods both qualitatively and quantitatively. To demonstrate the utility of the method, it was tested in two applications: quantitative evaluation of a neural network-based $chi$-separation reconstruction method ($chi$-sepnet-$ extit{R}_2^*$) and population-averaged region of interest (ROI) analysis. The proposed method demonstrates superior performance to the conventional vessel segmentation methods, effectively excluding the non-vessel structures, achieving the highest Dice score coefficient. For the applications, applying vessel masks report notable improvements for the quantitative evaluation of $chi$-sepnet-$ extit{R}_2^*$ and statistically significant differences in population-averaged ROI analysis. These applications suggest excluding vessels when analyzing the $chi$-separation maps provide more accurate evaluations. The proposed method has the potential to facilitate various applications, offering reliable analysis through the generation of a high-quality vessel mask.

Problem

• Vessel Segmentation
• Iron Distribution
• Myelin Measurement

Innovation

• Image Processing
• Vessel Artifact Removal
• Brain Iron and Myelin Measurement