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Abstract #0942

Vessel segmentation toolbox for susceptibility imaging: Region-growing algorithm and deep learning

Taechang Kim1, Hangyeol Park1, Sooyeon Ji2, and Jongho Lee1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, Korea, Republic of

Synopsis

Keywords: Software Tools, Software Tools, Chi-separation, Quantitative Susceptibility Mapping, Blood Vessels

Motivation: χ-separation generates paramagnetic and diamagnetic susceptibility maps, potentially reflecting iron and myelin distribution. However, large vessels introduce erroneous values, hindering accurate quantification of these components.

Goal(s): Our goal is to develop a user-friendly, GUI-based vessel segmentation toolbox that generates a high-quality vessel mask to exclude vessels in χ-separation maps.

Approach: Two vessel segmentation methods are developed: a region-growing algorithm-based method with handy hyperparameter tuning options and a deep learning-based method with no hyperparameter tuning.

Results: The toolbox produces a high-quality vessel mask for enhanced χ-separation analysis.

Impact: The vessel segmentation toolbox generates a high-quality vessel mask through a user-friendly GUI, supporting the reliability of analysis of χ-separation results by effectively excluding vessel artifacts for improved quantification of iron and myelin.

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