Search Results for author: Blake E. Dewey

Found 15 papers, 5 papers with code

Synthetic multi-inversion time magnetic resonance images for visualization of subcortical structures

no code implementations4 Jun 2025 Savannah P. Hays, Lianrui Zuo, Anqi Feng, Yihao Liu, Blake E. Dewey, Jiachen Zhuo, Ellen M. Mowry, Scott D. Newsome Jerry L. Prince, Aaron Carass

It accurately synthesized multi-TI images from standard clinical inputs, achieving image quality comparable to that from explicitly acquired multi-TI data. The synthetic images, especially for TI values between 400-800 ms, enhanced visualization of subcortical structures and improved segmentation of thalamic nuclei.

ECLARE: Efficient cross-planar learning for anisotropic resolution enhancement

1 code implementation14 Mar 2025 Samuel W. Remedios, Shuwen Wei, Shuo Han, Jinwei Zhang, Aaron Carass, Kurt G. Schilling, Dzung L. Pham, Jerry L. Prince, Blake E. Dewey

Super-resolution (SR) methods aim to address this problem, but previous methods do not address all of the following: slice profile shape estimation, slice gap, domain shift, and non-integer or arbitrary upsampling factors.

Super-Resolution

RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation using Multimodal MRI inputs

1 code implementation10 Sep 2024 Anqi Feng, Zhangxing Bian, Blake E. Dewey, Alexa Gail Colinco, Jiachen Zhuo, Jerry L. Prince

In this work, we introduce RATNUS, which uses synthetic T1-weighted images with many inversion times along with diffusion-derived features to enhance the visibility of nuclei within the thalamus.

Segmentation

Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion

no code implementations3 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.

Lesion Segmentation Segmentation

Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation

no code implementations31 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.

Domain Generalization Lesion Segmentation

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

1 code implementation1 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

Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis

no code implementations4 Mar 2021 Dzung L. Pham, Yi-Yu Chou, Blake E. Dewey, Daniel S. Reich, John A. Butman, Snehashis Roy

Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images.

Classification General Classification

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

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 +1

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.

Super-Resolution

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