Personalized White Matter Bundle Segmentation for Early Childhood.

White matter segmentation methods from diffusion magnetic resonance imaging range from streamline clustering-based approaches to bundle mask delineation, but none have proposed a pediatric-specific approach. White matter tract segmentation tools optimized for adults have shown inconsistent performance in pediatric populations. We explore optimization of manually corrected masks integrated into a direct-set, deep learning framework for generalizable pediatric white matter tract segmentation. We hypothesize that a deep learning model with a similar approach to TractSeg will improve similarity between an algorithm-generated mask and an expert-labeled ground truth. Given a cohort of 56 manually labelled white matter bundles, we take inspiration from TractSeg's 2D UNet architecture, and we modify (a) inputs to match bundle definitions as determined by pediatric experts; (b) evaluation to use k-fold cross validation instead of a train/validation/test split; (c) the loss function to masked Dice loss. We evaluate Dice score, volume overlap, and volume overreach of 16 major regions of interest compared to the expert-labeled dataset. To test whether our approach offers statistically significant improvements over TractSeg, we compare Dice voxels, volume overlap, and adjacency voxels with a Wilcoxon signed-rank test followed by false discovery rate (FDR) correction. We find statistical significance (