524 citations · 617 across the 12 of their papers we have counts for
15 papers
Uncertainty-Aware Cortical Surface Reconstruction and Spherical Registration for Heterogeneous Brain MRI
Jesper Duemose Nielsen, Karthik Gopinath, Andrew Hoopes +4
Surface-based cortical analysis is useful in many applications as it is a natural way to model the highly folded structure of the cerebral cortex, allowing quantities such as curva…
Unified Brain Surface and Volume Registration
S. Mazdak Abulnaga, Andrew Hoopes, Malte Hoffmann +6
Accurate registration of brain MRI scans is fundamental for cross-subject analysis in neuroscientific studies. This involves aligning both the cortical surface of the brain and the…
AtlasMorph: Learning conditional deformable templates for brain MRI
Marianne Rakic, Andrew Hoopes, S. Mazdak Abulnaga +3
Deformable templates, or atlases, are images that represent a prototypical anatomy for a population, and are often enhanced with probabilistic anatomical label maps. They are commo…
End-to-end Cortical Surface Reconstruction from Clinical Magnetic Resonance Images
Jesper Duemose Nielsen, Karthik Gopinath, Andrew Hoopes +7
Surface-based cortical analysis is valuable for a variety of neuroimaging tasks, such as spatial normalization, parcellation, and gray matter (GM) thickness estimation. However, mo…
MultiMorph: On-demand Atlas Construction
S. Mazdak Abulnaga, Andrew Hoopes, Neel Dey +5
We present MultiMorph, a fast and efficient method for constructing anatomical atlases on the fly. Atlases capture the canonical structure of a collection of images and are essenti…
VoxelPrompt: A Vision Agent for End-to-End Medical Image Analysis
Andrew Hoopes, Neel Dey, Victor Ion Butoi +2
We present VoxelPrompt, an end-to-end image analysis agent that tackles free-form radiological tasks. Given any number of volumetric medical images and a natural language prompt, V…
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…
An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation
Stefano Cerri, Douglas N. Greve, Andrew Hoopes +4
In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that…
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…
Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels
Sungmin Hong, Anna K. Bonkhoff, Andrew Hoopes +6
Despite the superior performance of Deep Learning (DL) on numerous segmentation tasks, the DL-based approaches are notoriously overconfident about their prediction with highly pola…
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Alessa Hering, Lasse Hansen, Tony C. W. Mok +50
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medi…
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…
A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
Stefano Cerri, Andrew Hoopes, Douglas N. Greve +2
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis. The method builds upon an existing cross…
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…