Accurate and automatic segmentation of lifespan brain MRI into regions of interest (ROIs) is crucial for studying brain development, aging, and early diagnosis of neurological diseases. Existing segmentation methods are often tailored to specific age groups, such as infants or adults, resulting in inconsistent performance when processing brain data from different age groups. To overcome this limitation, we introduce BrainSMM, a novel metadata-driven model that incorporates text-based prompts to guide representation learning in a segmentation backbone. These prompts, extracted via a pretrained image-text alignment model, encode valuable prior knowledge (e.g., age, scanner, gender) and are infused into the vision model to condition the features according to domain-specific contexts. We evaluate BrainSMM on a large-scale lifespan brain MRI dataset with 5,565 T1w MR images spanning multiple ages. Our approach achieves an average DSC of 94.59% for tissue segmentation (i.e., gray matter, white matter, and cerebrospinal fluid) and 86.34% for anatomical region segmentation (e.g., hippocampus, putamen, etc.) with corresponding average ASD of 0.20 mm and 0.75 mm, respectively. Notably, BrainSMM shows strong consistency in segmentation accuracy across all age groups and demonstrates improved anatomical detail preservation compared to baseline methods. Additionally, our metadata prompt technique is easily transferable and compatible with multiple backbone architectures, highlighting its adaptability. Overall, BrainSMM offers a robust, generalizable solution for lifespan brain MRI segmentation and lays the groundwork for enhanced clinical and developmental neuroimaging applications.
Teng et al. (Wed,) studied this question.