Kuberan Pushparajah

The paper introduces a deep learning pipeline for automated fetal brain segmentation and volumetry, showing reduced need for manual refinement and agreement with manual segmentations in clinical cases.

This paper proposes an automated pipeline for reconstructing fetal thorax MRI from motion-corrupted stacks across 21-36 weeks GA, using 3D CNNs and landmark-guided pose estimation.

The paper creates 3D MRI atlases of normal and abnormal fetal aortic arch anatomy and evaluates deep learning for automated multi-label segmentation.

This paper develops a 4D flow MRI framework for fetal heart circulation visualization and quantification using non-coplanar velocity-encoding stacks.