Consistent Segmentation of Longitudinal Brain MR Images with Spatio-Temporal Constrained Networks

Accurate and consistent segmentation of longitudinal brain magnetic resonance (MR) images is of great importance in studying brain morphological and functional changes over time. However, current available brain segmentation methods, especially deep learning methods, are mostly trained with cross-sectional brain images that might generate inconsistent results in longitudinal studies. To overcome this limitation, we present a novel coarse-to-fine spatio-temporal constrained deep learning model for consistent longitudinal segmentation based on limited labeled cross-sectional data with semi-supervised learning. Specifically, both segmentation smoothness and temporal consistency are imposed in the loss function. Moreover, brain structural changes over time are summarized as age constraint, to make the model better reflect the trends of longitudinal aging changes. We validate our proposed method on 53 sets of longitudinal T1-weighted brain MR images from ADNI, with an average of 4.5 time-points per subject. Both quantitative and qualitative comparisons with comparison methods demonstrate the superior performance of our proposed method.