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20232026
most citedBoosting Skull-Stripping Performance for Pediatric Brain Images

22 citations · 43 across the 16 of their papers we have counts for

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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.IV2025★ 3 cited

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-…

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.IV2024★ 22 cited

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…

eess.IV2024

Tyche: Stochastic In-Context Learning for Medical Image Segmentation

Marianne Rakic, Hallee E. Wong, Jose Javier Gonzalez Ortiz +3

Existing learning-based solutions to medical image segmentation have two important shortcomings. First, for most new segmentation task, a new model has to be trained or fine-tuned.…

eess.IV2023★ 7 cited

AngioMoCo: Learning-based Motion Correction in Cerebral Digital Subtraction Angiography

Ruisheng Su, Matthijs van der Sluijs, Sandra Cornelissen +6

Cerebral X-ray digital subtraction angiography (DSA) is the standard imaging technique for visualizing blood flow and guiding endovascular treatments. The quality of DSA is often n…

eess.IV2023

Joint cortical registration of geometry and function using semi-supervised learning

Jian Li, Greta Tuckute, Evelina Fedorenko +3

Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces. Existing iterative and learnin…