Self-Supervised Learning for Perivascular Spaces segmentation with enhanced contrast knowledge

Due to the absence of isotropic T2w modality in clinical datasets, it is challenging to enhance the PVS contrast using multiple neuroimage modalities. To overcome this issue, in this work we introduced using self-supervised pre-trained model in the enhanced PVS contrast image space to improve the downstream model segmentation performance when solely using T1w as the training data. The experiment results showed that the proposed method increased segmentation accuracy compared to the model trained from scratch using T1w modality and resulted in faster training and less required training data volume.