12 citations · 18 across the 6 of their papers we have counts for
6 papers
cSVR: Convolutional Slice-to-Volume Reconstruction
Margherita Firenze, Sean I. Young, Clinton J. Wang +5
Unpredictable fetal motion during MRI scans can result in oblique 2D slices that are difficult to interpret and often leads to prolonged scan times. Slice-to-volume reconstruction…
Reference-Free 3D Reconstruction of Brain Dissection Slabs via Learned Atlas Coordinates
Lin Tian, Jonathan Williams-Ramirez, Dina Zemlyanker +14
Correlation of neuropathology with MRI has the potential to transfer microscopic signatures of pathology to in vivo scans. There is increasing interest in building these correlatio…
Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series
S. Mazdak Abulnaga, Neel Dey, Sean I. Young +6
Blood oxygen level dependent (BOLD) MRI time series with maternal hyperoxia can assess placental oxygenation and function. Measuring precise BOLD changes in the placenta requires a…
Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
Pablo Laso, Stefano Cerri, Annabel Sorby-Adams +21
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated se…
Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI
Sean I. Young, Yaël Balbastre, Bruce Fischl +2
In magnetic resonance imaging (MRI), slice-to-volume reconstruction (SVR) refers to computational reconstruction of an unknown 3D magnetic resonance volume from stacks of 2D slices…
A Framework for Interpretability in Machine Learning for Medical Imaging
Alan Q. Wang, Batuhan K. Karaman, Heejong Kim +4
Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretabilit…