8 citations · 19 across the 14 of their papers we have counts for
6 papers · 1 filter
Fréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets
Nicholas Konz, Richard Osuala, Preeti Verma +16
Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, t…
SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images
Zafer Yildiz, Yuwen Chen, Maciej A. Mazurowski
Creating annotations for 3D medical data is time-consuming and often requires highly specialized expertise. Various tools have been implemented to aid this process. Segment Anythin…
Rethinking Perceptual Metrics for Medical Image Translation
Nicholas Konz, Yuwen Chen, Hanxue Gu +2
Modern medical image translation methods use generative models for tasks such as the conversion of CT images to MRI. Evaluating these methods typically relies on some chosen downst…
ContourDiff: Unpaired Medical Image Translation with Structural Consistency
Yuwen Chen, Nicholas Konz, Hanxue Gu +5
Accurately translating medical images between different modalities, such as Computed Tomography (CT) to Magnetic Resonance Imaging (MRI), has numerous downstream clinical and machi…
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models
Nicholas Konz, Yuwen Chen, Haoyu Dong +1
Diffusion models have enabled remarkably high-quality medical image generation, yet it is challenging to enforce anatomical constraints in generated images. To this end, we propose…
SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI
Hanxue Gu, Roy Colglazier, Haoyu Dong +20
Magnetic Resonance Imaging (MRI) is pivotal in radiology, offering non-invasive and high-quality insights into the human body. Precise segmentation of MRIs into different organs an…