1 citations · 2 across the 6 of their papers we have counts for
6 papers
Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets
Michael E. Kim, Chenyu Gao, Karthik Ramadass +16
Proper quality control (QC) is time consuming when working with large-scale medical imaging datasets, yet necessary, as poor-quality data can lead to erroneous conclusions or poorl…
RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation using Multimodal MRI inputs
Anqi Feng, Zhangxing Bian, Blake E. Dewey +3
Accurate segmentation of thalamic nuclei is important for better understanding brain function and improving disease treatment. Traditional segmentation methods often rely on a sing…
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…
Scalable, reproducible, and cost-effective processing of large-scale medical imaging datasets
Michael E. Kim, Karthik Ramadass, Chenyu Gao +11
Curating, processing, and combining large-scale medical imaging datasets from national studies is a non-trivial task due to the intense computation and data throughput required, va…
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…
A latent space for unsupervised MR image quality control via artifact assessment
Lianrui Zuo, Yuan Xue, Blake E. Dewey +3
Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images. Existin…