Search Results for author: Jerry L. Prince

Found 31 papers, 7 papers with code

Automated Ventricle Parcellation and Evan's Ratio Computation in Pre- and Post-Surgical Ventriculomegaly

no code implementations3 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.


FastCod: Fast Brain Connectivity in Diffusion Imaging

no code implementations18 Feb 2023 Zhangxing Bian, Muhan Shao, Jiachen Zhuo, Rao P. Gullapalli, Aaron Carass, Jerry L. Prince

Connectivity information derived from diffusion-weighted magnetic resonance images~(DW-MRIs) plays an important role in studying human subcortical gray matter structures.

Synthesizing audio from tongue motion during speech using tagged MRI via transformer

no code implementations14 Feb 2023 Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Maureen Stone, Georges El Fakhri, Jonghye Woo

However, elucidating the relationship between these two sources of information is challenging, due in part to the disparity in data structure between spatiotemporal motion fields (i. e., 4D motion fields) and one-dimensional audio waveforms.

A latent space for unsupervised MR image quality control via artifact assessment

no code implementations1 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.

Contrastive Learning

DRIMET: Deep Registration for 3D Incompressible Motion Estimation in Tagged-MRI with Application to the Tongue

no code implementations18 Jan 2023 Zhangxing Bian, Fangxu Xing, Jinglun Yu, Muhan Shao, Yihao Liu, Aaron Carass, Jiachen Zhuo, Jonghye Woo, Jerry L. Prince

However, this technique faces several challenges such as tag fading, large motion, long computation times, and difficulties in obtaining diffeomorphic incompressible flow fields.

Motion Estimation TAG

Segmenting thalamic nuclei from manifold projections of multi-contrast MRI

no code implementations15 Jan 2023 Chang Yan, Muhan Shao, Zhangxing Bian, Anqi Feng, Yuan Xue, Jiachen Zhuo, Rao P. Gullapalli, Aaron Carass, Jerry L. Prince

After registration of these contrasts and isolation of the thalamus, we use the uniform manifold approximation and projection (UMAP) method for dimensionality reduction to produce a low-dimensional representation of the data within the thalamus.

Dimensionality Reduction

Deep filter bank regression for super-resolution of anisotropic MR brain images

no code implementations6 Sep 2022 Samuel W. Remedios, Shuo Han, Yuan Xue, Aaron Carass, Trac D. Tran, Dzung L. Pham, Jerry L. Prince

In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane signals are typically of lower resolution than the in-plane signals.

regression Super-Resolution

Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator

no code implementations5 Jun 2022 Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Jiachen Zhuo, Maureen Stone, Georges El Fakhri, Jonghye Woo

Understanding the underlying relationship between tongue and oropharyngeal muscle deformation seen in tagged-MRI and intelligible speech plays an important role in advancing speech motor control theories and treatment of speech related-disorders.


Disentangling A Single MR Modality

no code implementations10 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.

Anatomy Disentanglement +3

Coordinate Translator for Learning Deformable Medical Image Registration

1 code implementation5 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.

Deformable Medical Image Registration Image Registration +1

Structure-aware Unsupervised Tagged-to-Cine MRI Synthesis with Self Disentanglement

no code implementations25 Feb 2022 Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Maureen Stone, Georges El Fakhri, Jonghye Woo

Specifically, we propose a novel input-output image patches self-training scheme to achieve a disentanglement of underlying anatomical structures and imaging modalities.

Disentanglement Style Transfer

Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis

no code implementations23 Jun 2021 Xiaofeng Liu, Fangxu Xing, Maureen Stone, Jiachen Zhuo, Reese Timothy, Jerry L. Prince, Georges El Fakhri, Jonghye Woo

Self-training based unsupervised domain adaptation (UDA) has shown great potential to address the problem of domain shift, when applying a trained deep learning model in a source domain to unlabeled target domains.

Image Generation Pseudo Label +2

MR Slice Profile Estimation by Learning to Match Internal Patch Distributions

1 code implementation31 Mar 2021 Shuo Han, Samuel Remedios, Aaron Carass, Michael Schär, Jerry L. Prince

To super-resolve the through-plane direction of a multi-slice 2D magnetic resonance (MR) image, its slice selection profile can be used as the degeneration model from high resolution (HR) to low resolution (LR) to create paired data when training a supervised algorithm.


A Structural Causal Model for MR Images of Multiple Sclerosis

1 code implementation4 Mar 2021 Jacob C. Reinhold, Aaron Carass, Jerry L. Prince

Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?"

Counterfactual Inference Disease Prediction

A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

no code implementations2 Aug 2020 S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers

In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.

A Deep Joint Sparse Non-negative Matrix Factorization Framework for Identifying the Common and Subject-specific Functional Units of Tongue Motion During Speech

no code implementations9 Jul 2020 Jonghye Woo, Fangxu Xing, Jerry L. Prince, Maureen Stone, Arnold Gomez, Timothy G. Reese, Van J. Wedeen, Georges El Fakhri

Experiments carried out with in vivo tongue motion data show that the proposed method can determine the common and subject-specific functional units with increased interpretability and decreased size variability.

Self domain adapted network

1 code implementation7 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.

Self-Supervised Learning Unsupervised Domain Adaptation

Finding novelty with uncertainty

2 code implementations11 Feb 2020 Jacob C. Reinhold, Yufan He, Shizhong Han, Yunqiang Chen, Dashan Gao, Junghoon Lee, Jerry L. Prince, Aaron Carass

Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because the appearance of pathology across diseases varies widely.

Validating uncertainty in medical image translation

1 code implementation11 Feb 2020 Jacob C. Reinhold, Yufan He, Shizhong Han, Yunqiang Chen, Dashan Gao, Junghoon Lee, Jerry L. Prince, Aaron Carass

Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians.


Evaluating the Impact of Intensity Normalization on MR Image Synthesis

1 code implementation11 Dec 2018 Jacob C. Reinhold, Blake E. Dewey, Aaron Carass, Jerry L. Prince

Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image.

Image Generation Imputation

A Sparse Non-negative Matrix Factorization Framework for Identifying Functional Units of Tongue Behavior from MRI

no code implementations15 Apr 2018 Jonghye Woo, Jerry L. Prince, Maureen Stone, Fangxu Xing, Arnold Gomez, Jordan R. Green, Christopher J. Hartnick, Thomas J. Brady, Timothy G. Reese, Van J. Wedeen, Georges El Fakhri

We then use three-dimensional synthetic and \textit{in vivo} tongue motion data using protrusion and simple speech tasks to identify subject-specific and data-driven functional units of the tongue in localized regions.

Topology guaranteed segmentation of the human retina from OCT using convolutional neural networks

no code implementations14 Mar 2018 Yufan He, Aaron Carass, Bruno M. Jedynak, Sharon D. Solomon, Shiv Saidha, Peter A. Calabresi, Jerry L. Prince

Optical coherence tomography (OCT) is a noninvasive imaging modality which can be used to obtain depth images of the retina.

Self Super-Resolution for Magnetic Resonance Images using Deep Networks

no code implementations26 Feb 2018 Can Zhao, Aaron Carass, Blake E. Dewey, Jerry L. Prince

This paper presents a self super-resolution~(SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image.


Fiber Orientation Estimation Guided by a Deep Network

no code implementations19 May 2017 Chuyang Ye, Jerry L. Prince

In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN).

Speech Map: A Statistical Multimodal Atlas of 4D Tongue Motion During Speech from Tagged and Cine MR Images

no code implementations24 Jan 2017 Jonghye Woo, Fangxu Xing, Maureen Stone, Jordan Green, Timothy G. Reese, Thomas J. Brady, Van J. Wedeen, Jerry L. Prince, Georges El Fakhri

Quantitative measurement of functional and anatomical traits of 4D tongue motion in the course of speech or other lingual behaviors remains a major challenge in scientific research and clinical applications.

Motion Estimation

Estimation of Fiber Orientations Using Neighborhood Information

no code implementations16 Jan 2016 Chuyang Ye, Jiachen Zhuo, Rao P. Gullapalli, Jerry L. Prince

Data from diffusion magnetic resonance imaging (dMRI) can be used to reconstruct fiber tracts, for example, in muscle and white matter.

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