T2AgeNet: A Text-Guided Framework with Tissue Features for Brain Age Estimation

Brain age estimation from structural MRI is an effective approach for detecting abnormal neurodevel opment and neurodegeneration. However, most existing methods produce global biomarkers that lack tissue-level specificity and fail to leverage medical prior knowledge. To address these limitations, we propose T2AgeNet, a dual-path image-text framework for tissue-level brain age estimation that integrates anatomical features with clinical semantics. The framework first segments brain MRI to generate tissue-specific masks, forming the basis for localized age prediction. To further incorporate medical prior knowledge, the model first aligns visual features with personalized clinical descriptions to guide semantic understanding of tissue-level variation. In parallel, it transforms handcrafted aging-related features into textual representations through an auxiliary branch using a large language model, enabling enriched interpretation and representation. We evaluate T2AgeNet on five datasets spanning fetal development, preterm infants, Alzheimer's disease, and autism spectrum disorder. Results demonstrate accurate age estimation across diverse populations. On the OASIS-3 and ABIDE-I datasets, the model further identifies tissue specific structural abnormalities consistent with known neurological patterns.