713 citations · 2.5k across the 20 of their papers we have counts for
12 papers · 1 filter
Anatomy-aware and acquisition-agnostic joint registration with SynthMorph
Malte Hoffmann, Andrew Hoopes, Douglas N. Greve +2
Affine image registration is a cornerstone of medical image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every…
Data Consistent Deep Rigid MRI Motion Correction
Nalini M. Singh, Neel Dey, Malte Hoffmann +5
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies. Current retrospective rigid intra-slice motion…
SynthStrip: Skull-Stripping for Any Brain Image
Andrew Hoopes, Jocelyn S. Mora, Adrian V. Dalca +2
The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their ab…
Supervision by Denoising for Medical Image Segmentation
Sean I. Young, Adrian V. Dalca, Enzo Ferrante +4
Learning-based image reconstruction models, such as those based on the U-Net, require a large set of labeled images if good generalization is to be guaranteed. In some imaging doma…
Unsupervised learning of MRI tissue properties using MRI physics models
Divya Varadarajan, Katherine L. Bouman, Andre van der Kouwe +2
In neuroimaging, MRI tissue properties characterize underlying neurobiology, provide quantitative biomarkers for neurological disease detection and analysis, and can be used to syn…
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
Benjamin Billot, Douglas N. Greve, Oula Puonti +5
Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans, CNNs are hi…
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast
Juan Eugenio Iglesias, Benjamin Billot, Yael Balbastre +6
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently hav…
SynthMorph: learning contrast-invariant registration without acquired images
Malte Hoffmann, Benjamin Billot, Douglas N. Greve +3
We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MR…
Cortical surface registration using unsupervised learning
Jieyu Cheng, Adrian V. Dalca, Bruce Fischl +1
Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A convent…
A Learning Strategy for Contrast-agnostic MRI Segmentation
Benjamin Billot, Douglas Greve, Koen Van Leemput +3
We present a deep learning strategy that enables, for the first time, contrast-agnostic semantic segmentation of completely unpreprocessed brain MRI scans, without requiring additi…
Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years
Lilla Zöllei, Juan Eugenio Iglesias, Yangming Ou +2
The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges i…
FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline
Leonie Henschel, Sailesh Conjeti, Santiago Estrada +3
Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands o…