3 citations · 7 across the 14 of their papers we have counts for
12 papers · 1 filter
MedForj: An open, large-scale foundational generative prior for high-resolution 3D brain MRI
Samuel W. Remedios, Aaron Carass, Jerry L. Prince +1
This work introduces MedForj, a suite of 3D foundational generative priors based on diffusion models. The MedForj models were trained on 1~mm isotropic 3D -weighted…
MSRepaint: Multiple Sclerosis Repaint with Conditional Denoising Diffusion Implicit Model for Bidirectional Lesion Filling and Synthesis
Jinwei Zhang, Lianrui Zuo, Yihao Liu +10
In multiple sclerosis, lesions interfere with automated magnetic resonance imaging analyses such as brain parcellation and deformable registration, while lesion segmentation models…
UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation
Jinwei Zhang, Lianrui Zuo, Blake E. Dewey +9
Automated segmentation of multiple sclerosis (MS) lesions using multicontrast magnetic resonance (MR) images improves efficiency and reproducibility compared to manual delineation,…
Beyond the LUMIR challenge: The pathway to foundational registration models
Junyu Chen, Shuwen Wei, Joel Honkamaa +33
Medical image challenges have played a transformative role in advancing the field, catalyzing innovation and establishing new performance benchmarks. Image registration, a foundati…
Unique MS Lesion Identification from MRI
Carlos A. Rivas, Jinwei Zhang, Shuwen Wei +3
Unique identification of multiple sclerosis (MS) white matter lesions (WMLs) is important to help characterize MS progression. WMLs are routinely identified from magnetic resonance…
Bi-Directional MS Lesion Filling and Synthesis Using Denoising Diffusion Implicit Model-based Lesion Repainting
Jinwei Zhang, Lianrui Zuo, Yihao Liu +4
Automatic magnetic resonance (MR) image processing pipelines are widely used to study people with multiple sclerosis (PwMS), encompassing tasks such as lesion segmentation and brai…
Beyond MR Image Harmonization: Resolution Matters Too
Savannah P. Hays, Samuel W. Remedios, Lianrui Zuo +6
Magnetic resonance (MR) imaging is commonly used in the clinical setting to non-invasively monitor the body. There exists a large variability in MR imaging due to differences in sc…
Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis
Savannah P. Hays, Lianrui Zuo, Yihao Liu +4
Deep learning (DL) has led to significant improvements in medical image synthesis, enabling advanced image-to-image translation to generate synthetic images. However, DL methods fa…
Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement
Ho Hin Lee, Adam M. Saunders, Michael E. Kim +15
Purpose: Eye morphology varies significantly across the population, especially for the orbit and optic nerve. These variations limit the feasibility and robustness of generalizing…
AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution
Zejun Wu, Samuel W. Remedios, Blake E. Dewey +2
Anisotropic low-resolution (LR) magnetic resonance (MR) images are fast to obtain but hinder automated processing. We propose to use denoising diffusion probabilistic models (DDPMs…
Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion
Jinwei Zhang, Lianrui Zuo, Blake E. Dewey +4
Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual deline…
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation
Jinwei Zhang, Lianrui Zuo, Blake E. Dewey +5
Deep learning algorithms utilizing magnetic resonance (MR) images have demonstrated cutting-edge proficiency in autonomously segmenting multiple sclerosis (MS) lesions. Despite the…