713 citations · 2.5k across the 20 of their papers we have counts for
23 papers
Multi-Head Graph Convolutional Network for Structural Connectome Classification
Anees Kazi, Jocelyn Mora, Bruce Fischl +2
We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks…
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…
SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration
Sean I. Young, Yaël Balbastre, Adrian V. Dalca +3
In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to instead use self-supervision, with excellent resul…
Learning the Effect of Registration Hyperparameters with HyperMorph
Andrew Hoopes, Malte Hoffmann, Douglas N. Greve +3
We introduce HyperMorph, a framework that facilitates efficient hyperparameter tuning in learning-based deformable image registration. Classical registration algorithms perform an…
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…
HyperMorph: Amortized Hyperparameter Learning for Image Registration
Andrew Hoopes, Malte Hoffmann, Bruce Fischl +2
We present HyperMorph, a learning-based strategy for deformable image registration that removes the need to tune important registration hyperparameters during training. Classical r…
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…
3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology
Henry Tregidgo, Adria Casamitjana, Caitlin Latimer +9
Neuroimaging to neuropathology correlation (NTNC) promises to enable the transfer of microscopic signatures of pathology to in vivo imaging with MRI, ultimately enhancing clinical…
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…
Fast Infant MRI Skullstripping with Multiview 2D Convolutional Neural Networks
Amod Jog, P. Ellen Grant, Joseph L. Jacobson +4
Skullstripping is defined as the task of segmenting brain tissue from a full head magnetic resonance image~(MRI). It is a critical component in neuroimage processing pipelines. Dow…
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
Adrian V. Dalca, Evan Yu, Polina Golland +3
Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these…
PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation
Amod Jog, Andrew Hoopes, Douglas N. Greve +2
With the advent of convolutional neural networks~(CNN), supervised learning methods are increasingly being used for whole brain segmentation. However, a large, manually annotated t…
Pulse Sequence Resilient Fast Brain Segmentation
Amod Jog, Bruce Fischl
Accurate automatic segmentation of brain anatomy from -weighted~(-w) magnetic resonance images~(MRI) has been a computationally intensive bottleneck in neuroimaging pipel…
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
Juan Eugenio Iglesias, Ricardo Insausti, Garikoitz Lerma-Usabiaga +7
The human thalamus is a brain structure that comprises numerous, highly specific nuclei. Since these nuclei are known to have different functions and to be connected to different a…
Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections
Juan Eugenio Iglesias, Marc Modat, Loic Peter +6
Nonlinear registration of 2D histological sections with corresponding slices of MRI data is a critical step of 3D histology reconstruction. This task is difficult due to the large…