Fig. 1: The proposed pipeline for hierarchical quantitative analysis of cerebrovasculature. | Nature Communications

Fig. 1: The proposed pipeline for hierarchical quantitative analysis of cerebrovasculature.

From: Self-supervised learning for accurately modelling hierarchical evolutionary patterns of cerebrovasculature

Fig. 1

To start with, subjects were admitted into different hospitals to create records. Basic demographics were measured and recorded, followed by guiding the subjects to different MR scanners within each hospital for image acquisition. Typically, paired T1 and MRA volumes were acquired. Each T1 image was used to generate a deformation field for individualized biprojection between the subject and MNI space. Each subject’s cerebrovascular segmentation was obtained by applying the deep-learning segmentation model to the corresponding TOF-MRA volume. For feature quantification, the atlas defined in MNI space was transformed to subject space using the previously created deformation field. Normative models were then built using group-wise statistical vascular and cortical features.

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