22 citations · 43 across the 16 of their papers we have counts for
20 papers
Registration is a Representation Problem: Solving Whole-Body PET/CT Registration
Neel Dey, Adrian V. Dalca
Raw image intensities are a poor surrogate for similarity across images in correspondence problems. Further, while auxiliary segmentation labels can provide semantic identification…
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…
FleXray: Universal Clinical X-ray Segmentation
Victor Ion Butoi, Vivek Gopalakrishnan, John V. Guttag +2
X-ray is medicine's most widely used imaging modality, yet remains among its least quantitative. Unlike volumetric modalities like CT or MRI, X-ray collapses 3D anatomy into a 2D p…
JParc: Joint cortical surface parcellation with registration
Jian Li, Karthik Gopinath, Brian L. Edlow +2
Cortical surface parcellation is a fundamental task in both basic neuroscience research and clinical applications, enabling more accurate mapping of brain regions. Model-based and…
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…
Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains
Marianne Rakic, Siyu Gai, Etienne Chollet +2
A single biomedical image can be meaningfully segmented in multiple ways, depending on the desired application. For instance, a brain MRI can be segmented according to tissue types…
Deep infant brain segmentation from multi-contrast MRI
Malte Hoffmann, Lilla Zöllei, Adrian V. Dalca
Segmentation of magnetic resonance images (MRI) facilitates analysis of human brain development by delineating anatomical structures. However, in infants and young children, accura…
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…
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-…
MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance
Hallee E. Wong, Jose Javier Gonzalez Ortiz, John Guttag +1
Medical researchers and clinicians often need to perform novel segmentation tasks on a set of related images. Existing methods for segmenting a new dataset are either interactive,…
Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis
Neel Dey, Benjamin Billot, Hallee E. Wong +5
Current volumetric biomedical foundation models struggle to generalize as public 3D datasets are small and do not cover the broad diversity of medical procedures, conditions, anato…
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…
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…
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.…
ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image
Hallee E. Wong, Marianne Rakic, John Guttag +1
Biomedical image segmentation is a crucial part of both scientific research and clinical care. With enough labelled data, deep learning models can be trained to accurately automate…
JOSA: Joint surface-based registration and atlas construction of brain geometry and function
Jian Li, Greta Tuckute, Evelina Fedorenko +3
Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are m…
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…
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…