5 citations · 10 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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,…
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…
GIST: Generating Image-Specific Text for Fine-grained Object Classification
Kathleen M. Lewis, Emily Mu, Adrian V. Dalca +1
Recent vision-language models outperform vision-only models on many image classification tasks. However, because of the absence of paired text/image descriptions, it remains diffic…