Abstract #3410

Bi-parametric Joint Label Fusion: An Improved Segmentation Tool for Deep Gray Matter in QSM

Fahad Salman^1,2, Kevin Thomas¹, Ademola Adegbemigun¹, Niels P. Bergsland¹, Michael G. Dwyer^1,3, Robert Zivadinov^1,3, and Ferdinand Schweser^1,3

¹Buffalo Neuroimaging Analysis Center, Department of Neurology at the Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States, ²Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY, United States, ³Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, United States

Synopsis

Keywords: Segmentation, Segmentation, Deep gray matter, multiple sclerosis, susceptibility, T1 contrast, FSL FIRST, SynthSeg, FreeSurfer, joint label fusion, multi-contrast, automated segmentation

Motivation: Publicly available T1-weighted-based segmentation tools may introduce systematic errors, especially in conditions like multiple sclerosis (MS), where atrophy is common.

Goal(s): We aimed to improve segmentation accuracy by developing a multi-atlas, bi-parametric DGM tool (bi-parametric joint label fusion [B-JLF], leveraging QSM and T1-weighted images.

Approach: We generated nine group-specific atlases across age and disease spectra, and modified the original ANTs JLF for multi-contrast based segmentation. We compared our tool against T1-based methods using overlap and susceptibility measures against manual segmentations.

Results: B-JLF achieved superior voxel overlap (75%) and highest correlation with ground truth susceptibility (R²=0.88), outperforming T1-based methods.

Impact: B-JLF provides reliable DGM segmentation, enhancing quantitative accuracy in neurodegenerative studies. The tool and atlases are publicly accessible, supporting broader neuroimaging applications.

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