7 citations · 26 across the 22 of their papers we have counts for
6 papers · 1 filter
CATNUS: Coordinate-Aware Thalamic Nuclei Segmentation Using T1-Weighted MRI
Anqi Feng, Zhangxing Bian, Samuel W. Remedios +6
Accurate segmentation of thalamic nuclei from magnetic resonance images is important due to the distinct roles of these nuclei in overall brain function and to their differential i…
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…
Segmenting Thalamic Nuclei: T1 Maps Provide a Reliable and Efficient Solution
Anqi Feng, Zhangxing Bian, Samuel W. Remedios +5
Accurate thalamic nuclei segmentation is crucial for understanding neurological diseases, brain functions, and guiding clinical interventions. However, the optimal inputs for segme…
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,…
Synthetic multi-inversion time magnetic resonance images for visualization of subcortical structures
Savannah P. Hays, Lianrui Zuo, Anqi Feng +7
Purpose: Visualization of subcortical gray matter is essential in neuroscience and clinical practice, particularly for disease understanding and surgical planning.While multi-inver…
ECLARE: Efficient cross-planar learning for anisotropic resolution enhancement
Samuel W. Remedios, Shuwen Wei, Shuo Han +6
In clinical imaging, magnetic resonance (MR) image volumes are often acquired as stacks of 2D slices with decreased scan times, improved signal-to-noise ratio, and image contrasts…