Search Results for author:
Found 6 papers, 2 papers with code
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
Characterizing normal perinatal development of the human brain structural connectivity
The new computational method and results are useful for assessing normal and abnormal development of the structural connectome early in life.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI
Our study demonstrates the potential of data-driven methods for improving the accuracy and robustness of fODF estimation.
A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging
Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles.
A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI
Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation.
