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20192026
most citedSynthStrip: Skull-Stripping for Any Brain Image

524 citations · 617 across the 12 of their papers we have counts for

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8 papers · 1 filter

eess.IV2026

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…

eess.IV2025

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…

eess.IV2024★ 4 cited

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…

eess.IV2023★ 31 cited

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…

eess.IV2022★ 524 cited

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…

eess.IV2021★ 4 cited

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…

eess.IV2021★ 1 cited

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…

eess.IV2020★ 9 cited

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…