Search Results for author: Aaron Carass

Found 24 papers, 8 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.

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

On Finite Difference Jacobian Computation in Deformable Image Registration

1 code implementation12 Dec 2022 Yihao Liu, Junyu Chen, Shuwen Wei, Aaron Carass, Jerry Prince

We show that for a 2D transformation, four unique finite difference approximations of $|J|$'s must be positive to ensure the entire domain is invertible and free of folding at the pixel level.

Image Registration

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

Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions

no code implementations5 Aug 2022 Germain Morilhat, Naomi Kifle, Sandra FinesilverSmith, Bram Ruijsink, Vittoria Vergani, Habtamu Tegegne Desita, Zerubabel Tegegne Desita, Esther Puyol-Anton, Aaron Carass, Andrew P. King

This work showcases, for the first time, the potential of DL in automating the process of effusion assessment from ultrasound in LMIC settings where there is often a lack of experienced radiologists to perform such tasks.

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

Data-driven Uncertainty Quantification in Computational Human Head Models

no code implementations29 Oct 2021 Kshitiz Upadhyay, Dimitris G. Giovanis, Ahmed Alshareef, Andrew K. Knutsen, Curtis L. Johnson, Aaron Carass, Philip V. Bayly, Michael D. Shields, K. T. Ramesh

This framework is demonstrated on a 2D subject-specific head model, where the goal is to quantify uncertainty in the simulated strain fields (i. e., output), given variability in the material properties of different brain substructures (i. e., input).

Density Estimation Dimensionality Reduction

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

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

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.


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.

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

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.


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