Dr. Lianrui Zuo Profile

Dr. Lianrui Zuo

at Johns Hopkins Univ

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Author

Publications (13)

Proceedings Article | 2 April 2024 Poster + Paper

Deep learning-based segmentation of hydrocephalus brain ventricle from ultrasound

Yuli Wang, Yihao Liu, Shuwen Wei, Yuan Xue, Lianrui Zuo, Samuel Remedios, Zhangxing Bian, Michael Meggyesy, Jheesoo Ahn, Ryan Lee, Mark Luciano, Jerry Prince, Aaron Carass

Proceedings Volume 12926, 1292639 (2024) https://doi.org/10.1117/12.3007668

KEYWORDS: Ultrasonography, Image segmentation, Brain, Neuroimaging, Deep learning, Video, Magnetic resonance imaging, Education and training, Computed tomography, Convolution

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Proceedings Article | 2 April 2024 Presentation + Paper

Revisiting registration-based image synthesis: a focus on unsupervised MR image synthesis

Savannah Hays, Lianrui Zuo, Yihao Liu, Anqi Feng, Jiachen Zhuo, Jerry Prince, Aaron Carass

Proceedings Volume 12926, 1292612 (2024) https://doi.org/10.1117/12.3006425

KEYWORDS: Education and training, Image segmentation, Magnetic resonance imaging, Deformation, Anatomy, Image registration, Image visualization, Image fusion, Visualization, Image processing

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Proceedings Article | 2 April 2024 Poster + Paper

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

Jinwei Zhang, Lianrui Zuo, Blake Dewey, Samuel Remedios, Savannah Hays, Dzung Pham, Jerry Prince, Aaron Carass

Proceedings Volume 12930, 129302I (2024) https://doi.org/10.1117/12.3011291

KEYWORDS: Education and training, Multiple sclerosis, Magnetic resonance imaging, Image segmentation, Solids, Fourier transforms, Scanners, White matter, Regenerative medicine, Neuroscience

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Proceedings Article | 10 April 2023 Presentation + Paper

Investigating the effect of cerebral atrophy on brain deformation using subject-specific models

Allen Hong, Aaron Carass, Lianrui Zuo, Jerry Prince, Ahmed Alshareef

Proceedings Volume 12468, 1246815 (2023) https://doi.org/10.1117/12.2654461

KEYWORDS: Brain, 3D modeling, Deformation, Traumatic brain injury, Biomechanics, Magnetic resonance imaging

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Proceedings Article | 3 April 2023 Poster + Paper

Unsupervised quality assurance for brain MR image rigid registration using latent shape representation

Yuan Xue, Lianrui Zuo, Samuel Remedios, Blake Dewey, Peiyu Duan, Yihao Liu, Rendong Zhang, Scott Newsome, Ellen Mowry, Aaron Carass, Jerry Prince

Proceedings Volume 12464, 124641G (2023) https://doi.org/10.1117/12.2647484

KEYWORDS: Image registration, Magnetic resonance imaging, 3D modeling, Brain, Education and training, Binary data, Rigid registration, Neuroimaging, Image quality, Multiple sclerosis

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Proceedings Article | 3 April 2023 Poster + Paper

Joint synthesis of WMn MPRAGE and parameter maps using deep learning and an imaging equation

Pouria Tohidi, Shuo Han, Lianrui Zuo, Jiachen Zhuo, Steven Roys, Aaron Carass, Rao Gullapalli, Jerry Prince

Proceedings Volume 12464, 124642F (2023) https://doi.org/10.1117/12.2653988

KEYWORDS: Image segmentation, Magnetic resonance imaging, Voxels, Deep learning, Thalamus, White matter, Data modeling, Visualization, Realistic image synthesis

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Proceedings Article | 3 April 2023 Presentation + Paper

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

Lianrui Zuo, Yuan Xue, Blake Dewey, Yihao Liu, Jerry Prince, Aaron Carass

Proceedings Volume 12464, 1246418 (2023) https://doi.org/10.1117/12.2652917

KEYWORDS: Image quality, Magnetic resonance imaging, Education and training, Image processing, Feature extraction

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Proceedings Article | 4 April 2022 Presentation + Paper

Cranial meninges reconstruction based on convolutional networks and deformable models: applications to longitudinal study of normal aging

Peiyu Duan, Shuo Han, Lianrui Zuo, Yang An, Yihao Liu, Ahmed Alshareef, Junghoon Lee, Aaron Carass, Susan Resnick, Jerry Prince

Proceedings Volume 12032, 1203215 (2022) https://doi.org/10.1117/12.2613146

KEYWORDS: Brain, Magnetic resonance imaging, Statistical analysis, Spherical lenses, Reconstruction algorithms, Magnetism, Image segmentation, Data modeling, Convolutional neural networks, Statistical modeling

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Proceedings Article | 4 April 2022 Presentation + Paper

Evaluating the impact of MR image contrast on whole brain segmentation

Savannah Hays, Lianrui Zuo, Aaron Carass, Jerry Prince

Proceedings Volume 12032, 120320I (2022) https://doi.org/10.1117/12.2611503

KEYWORDS: Magnetic resonance imaging, Image segmentation, Brain, Neuroimaging, Image processing algorithms and systems, Algorithm development, Data modeling, Visualization, Manufacturing, Magnetism

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Proceedings Article | 4 April 2022 Presentation + Paper

Evaluating the impact of MR image harmonization on thalamus deep network segmentation

Muhan Shao, Lianrui Zuo, Aaron Carass, Jiachen Zhuo, Rao Gullapalli, Jerry Prince

Proceedings Volume 12032, 120320H (2022) https://doi.org/10.1117/12.2613159

KEYWORDS: Image segmentation, Thalamus, Magnetic resonance imaging, Brain, Neuroimaging, Image processing algorithms and systems, Visualization, Diffusion tensor imaging

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Medical image segmentation is one of the core tasks of medical image analysis. Automatic segmentation of brain magnetic resonance images (MRIs) can be used to visualize and track changes of the brain’s anatomical structures that may occur due to normal aging or disease. Machine learning techniques are widely used in automatic structure segmentation. However, the contrast variation between the training and testing data makes it difficult for segmentation algorithms to generate consistent results. To address this problem, an image–to– image translation technique called MR image harmonization can be used to match the contrast between different data sets. It is important for the harmonization to transform image intensity while maintaining the underlying anatomy. In this paper, we present a 3D U-Net algorithm to segment the thalamus from multiple MR image modalities and investigate the impact of harmonization on the segmentation algorithm. Manual delineations of thalamic nuclei on two data sets are available. However, we aim to analyze the thalamus in another large data set where ground truth labels are lacking. We trained two segmentation networks, one with unharmonized images and the other with harmonized images, on one data set with manual labels, and compared their performances on the other data set with manual labels. These two data groups were diagnosed with two brain disorders and were acquired with similar imaging protocols. The harmonization target is the large data set without manual labels, which also has a different imaging protocol. The networks trained on unharmonized and harmonized data showed no significant difference when evaluating on the other data set; demonstrating that image harmonization can maintain the anatomy and does not affect the segmentation task. The two networks were evaluated on the harmonization target data set and the network trained on harmonized data showed significant improvement over the network trained on unharmonized data. Therefore, the network trained on harmonized data provides the potential to process large amounts of data from other sites, even in the absence of site-specific training data.

Proceedings Article | 10 March 2020 Paper

Variational intensity cross channel encoder for unsupervised vessel segmentation on OCT angiography

Yihao Liu, Lianrui Zuo, Aaron Carass, Yufan He, Angeliki Filippatou, Sharon Solomon, Shiv Saidha, Peter Calabresi, Jerry Prince

Proceedings Volume 11313, 113130Y (2020) https://doi.org/10.1117/12.2549967

KEYWORDS: Image segmentation, Angiography, Optical coherence tomography, Network architectures, Capillaries, Eye

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Proceedings Article | 15 March 2019 Presentation + Paper

Automatic quality control using hierarchical shape analysis for cerebellum parcellation

Lianrui Zuo, Shuo Han, Aaron Carass, Sarah Ying, Chiadikaobi Onyike, Jerry Prince

Proceedings Volume 10949, 109490J (2019) https://doi.org/10.1117/12.2512805

KEYWORDS: Cerebellum, Image segmentation, Shape analysis, Automatic control, Error analysis, Medicine, Visualization, Computer engineering, Biomedical engineering, Behavioral neuroscience

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Proceedings Article | 12 March 2018 Presentation + Paper

Automatic outlier detection using hidden Markov model for cerebellar lobule segmentation

Lianrui Zuo, Aaron Carass, Shuo Han, Jerry Prince

Proceedings Volume 10578, 105780D (2018) https://doi.org/10.1117/12.2295709

KEYWORDS: Image segmentation, Cerebellum, Image classification, Error analysis, Failure analysis, Visualization, Medical imaging, Feature extraction, Probability theory, Image processing algorithms and systems

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Showing 5 of 13 publications

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