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Abstract #1719

Automated segmentation of midbrain structures using quantitative susceptibility mapping images

Benjamn Garzn 1 , Grgoria Kalpouzos 1 , and Rouslan Sitnikov 2

1 Aging Research Center, Karolinska Institute and Stockholm University, Stockholm, Sweden, 2 MRI Research Centre, Karolinska University Hospital, Stockholm, Sweden

We present a fully automated algorithm for segmentation of the red nucleus, substantia nigra and subthalamic nucleus from a pair of T1w and quantitative susceptibility (QSM) images, aimed at providing accurate, objective and reproducible segmentations. The algorithm produces spatial probabilistic maps via a multi-atlas label fusion scheme by combining global (T1w) and local (QSM) non-linear registrations. These probabilistic maps are employed as priors in a model representing QSM intensities as a Gaussian mixture. Manual segmentations were obtained for 16 subjects and used to train and validate the model. Cross-validated Dice scores ranged between 0.66 (subthalamic nucleus) and 0.85 (red nucleus).

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