Vessel segmentation for X-separation
This addresses vessel artifacts in brain imaging for researchers using χ-separation, though it is incremental as it builds on existing segmentation approaches.
The paper tackles artifacts from vessels in χ-separation QSM that interfere with iron and myelin quantification by developing a new vessel segmentation method, which outperforms conventional methods with the highest Dice score and improves quantitative evaluations in applications.
$χ$-separation is an advanced quantitative susceptibility mapping (QSM) method that is designed to generate paramagnetic ($χ_{para}$) and diamagnetic ($|χ_{dia}|$) 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 $χ$-separation is developed. The method comprises three steps: 1) Seed generation from $\textit{R}_2^*$ and the product of $χ_{para}$ and $|χ_{dia}|$ 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 $χ$-separation reconstruction method ($χ$-sepnet-$\textit{R}_2^*$) 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 $χ$-sepnet-$\textit{R}_2^*$ and statistically significant differences in population-averaged ROI analysis. These applications suggest excluding vessels when analyzing the $χ$-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.