Conditional Atlas

Conditional atlases are computational models that generate anatomical atlases tailored to specific subpopulations defined by factors like age, disease, or developmental stage, enabling more precise analysis of anatomical variations. Recent research focuses on developing novel deep learning architectures, including implicit neural representations and diffusion models, to generate these atlases efficiently and accurately, often bypassing traditional registration methods. This work improves the precision of medical image analysis, particularly in fields like fetal brain development and neuropsychiatric disorder diagnosis, by providing more accurate and condition-specific anatomical references for segmentation, registration, and other downstream tasks. The resulting atlases facilitate more nuanced studies of anatomical differences across populations and improve the accuracy of clinical diagnoses.