22 citations · 26 across the 9 of their papers we have counts for
5 papers · 1 filter
: learning to disentangle technical distortions from true biological change
Jingru Fu, Kathleen E. Larson, Douglas N. Greve +2
Longitudinal MRI enables sensitive measurement of structural brain change for studying aging and neurodegenerative disease. Deformable image registration is a key tool for estimati…
Learning accurate rigid registration for longitudinal brain MRI from synthetic data
Jingru Fu, Adrian V. Dalca, Bruce Fischl +2
Rigid registration aims to determine the translations and rotations necessary to align features in a pair of images. While recent machine learning methods have become state-of-the-…
Neurovascular Segmentation in sOCT with Deep Learning and Synthetic Training Data
Etienne Chollet, Yaël Balbastre, Chiara Mauri +3
Microvascular anatomy is known to be involved in various neurological disorders. However, understanding these disorders is hindered by the lack of imaging modalities capable of cap…
A label-free and data-free training strategy for vasculature segmentation in serial sectioning OCT data
Etienne Chollet, Yael Balbastre, Caroline Magnain +2
Serial sectioning Optical Coherence Tomography (sOCT) is a high-throughput, label free microscopic imaging technique that is becoming increasingly popular to study post-mortem neur…
Boosting Skull-Stripping Performance for Pediatric Brain Images
William Kelley, Nathan Ngo, Adrian V. Dalca +3
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the…