A Brain Age Estimation Network based on QSM using the Segment Transformer

Quantitative Susceptibility Mapping (QSM) reflects various biological procedures, e.g., iron accumulation in deep gray matter, which is tightly associated with brain development and highly related to various neurodegenerative diseases. Nowadays, deep-learning-based methods have achieved great success in medical image segmentation due to their ability to extract multi-scale features. We create a novel network to segment key brain areas on QSM images to improve brain age prediction. Our network further integrates multi-level features by utilizing a variant of Vision Transformer, termed Segment Transformer. Results show that our method can improve brain age estimation compared to previous studies based on T1w MRI.