no code implementations • 19 Feb 2024 • Savannah P. Hays, Lianrui Zuo, Yihao Liu, Anqi Feng, Jiachen Zhuo, Jerry L. Prince, Aaron Carass
Subsequently, this estimated deformation is applied to align the paired WMn counterpart of the moving CSFn image, yielding a synthetic WMn image for the fixed CSFn image.
no code implementations • 3 Dec 2023 • Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Dzung L. Pham, Aaron Carass, Jerry L. Prince
Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation.
no code implementations • 31 Oct 2023 • Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Savannah P. Hays, Dzung L. Pham, Jerry L. Prince, Aaron Carass
Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation.
no code implementations • 13 Apr 2023 • Peiyu Duan, Yuan Xue, Shuo Han, Lianrui Zuo, Aaron Carass, Caitlyn Bernhard, Savannah Hays, Peter A. Calabresi, Susan M. Resnick, James S. Duncan, Jerry L. Prince
The meninges, located between the skull and brain, are composed of three membrane layers: the pia, the arachnoid, and the dura.
no code implementations • 4 Apr 2023 • Savannah P. Hays, Lianrui Zuo, Yuli Wang, Mark G. Luciano, Aaron Carass, Jerry L. Prince
Development of MR harmonization has enabled different contrast MRIs to be synthesized while preserving the underlying anatomy.
no code implementations • 3 Mar 2023 • Yuli Wang, Anqi Feng, Yuan Xue, Lianrui Zuo, Yihao Liu, Ari M. Blitz, Mark G. Luciano, Aaron Carass, Jerry L. Prince
Normal pressure hydrocephalus~(NPH) is a brain disorder associated with enlarged ventricles and multiple cognitive and motor symptoms.
no code implementations • 1 Feb 2023 • Lianrui Zuo, Yuan Xue, Blake E. Dewey, Yihao Liu, Jerry L. Prince, Aaron Carass
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.
no code implementations • 12 Dec 2022 • Lianrui Zuo, Yihao Liu, Yuan Xue, Blake E. Dewey, Samuel W. Remedios, Savannah P. Hays, Murat Bilgel, Ellen M. Mowry, Scott D. Newsome, Peter A. Calabresi, Susan M. Resnick, Jerry L. Prince, Aaron Carass
Furthermore, HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts.
no code implementations • 10 May 2022 • Lianrui Zuo, Yihao Liu, Yuan Xue, Shuo Han, Murat Bilgel, Susan M. Resnick, Jerry L. Prince, Aaron Carass
Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks.
1 code implementation • 5 Mar 2022 • Yihao Liu, Lianrui Zuo, Shuo Han, Yuan Xue, Jerry L. Prince, Aaron Carass
The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images.
no code implementations • 24 Mar 2021 • Lianrui Zuo, Blake E. Dewey, Aaron Carass, Yihao Liu, Yufan He, Peter A. Calabresi, Jerry L. Prince
Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis.
1 code implementation • 7 Jul 2020 • Yufan He, Aaron Carass, Lianrui Zuo, Blake E. Dewey, Jerry L. Prince
However, training a model for each target domain is time consuming and computationally expensive, even infeasible when target domain data are scarce or source data are unavailable due to data privacy.