Camilo A. Calixto

This paper develops a multi-task deep learning method to segment and parcellate fetal brain structures from diffusion MRI, achieving high accuracy in tissue segmentation, tract segmentation, and parcellation.

This study quantifies crossing tracts and bottleneck regions in fetal brain white matter using diffusion MRI, finding high prevalence of both features compared to adult brains.

This study constructs a spatiotemporal atlas of fetal white matter tracts using dMRI, revealing developmental patterns of FA and MD across gestational weeks 22-37.

This study uses deep learning to estimate fiber orientation distribution functions (FODs) from limited diffusion MRI data in newborns and fetuses, achieving results comparable to standard methods with fewer measurements.