Ms. Can Cui Profile

Can Cui

at Vanderbilt Univ

SPIE Involvement:

Author

Publications (14)

SPIE Journal Paper | 5 November 2024 Open Access

Data-driven nucleus subclassification on colon hematoxylin and eosin using style-transferred digital pathology

Lucas Remedios, Shunxing Bao, Samuel Remedios, Ho Hin Lee, Leon Cai, Thomas Li, Ruining Deng, Nancy Newlin, Adam Saunders, Can Cui, Jia Li, Qi Liu, Ken S. Lau, Joseph Roland, Mary Washington, Lori Coburn, Keith Wilson, Yuankai Huo, Bennett Landman

JMI, Vol. 11, Issue 06, 067501, (November 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.6.067501

KEYWORDS: Education and training, Tissues, Colon, Data modeling, Diseases and disorders, Virtual colonoscopy, Image segmentation, Performance modeling, Pathology, Cross validation

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PurposeCells are building blocks for human physiology; consequently, understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions in both health and disease. Hematoxylin and eosin (H&E) is the standard stain used in histological analysis of tissues in both clinical and research settings. Although H&E is ubiquitous and reveals tissue microanatomy, the classification and mapping of cell subtypes often require the use of specialized stains. The recent CoNIC Challenge focused on artificial intelligence classification of six types of cells on colon H&E but was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). We propose to use inter-modality learning to label previously un-labelable cell types on H&E.ApproachWe took advantage of the cell classification information inherent in multiplexed immunofluorescence (MxIF) histology to create cell-level annotations for 14 subclasses. Then, we performed style transfer on the MxIF to synthesize realistic virtual H&E. We assessed the efficacy of a supervised learning scheme using the virtual H&E and 14 subclass labels. We evaluated our model on virtual H&E and real H&E.ResultsOn virtual H&E, we were able to classify helper T cells and epithelial progenitors with positive predictive values of 0.34±0.15 (prevalence 0.03±0.01) and 0.47±0.1 (prevalence 0.07±0.02), respectively, when using ground truth centroid information. On real H&E, we needed to compute bounded metrics instead of direct metrics because our fine-grained virtual H&E predicted classes had to be matched to the closest available parent classes in the coarser labels from the real H&E dataset. For the real H&E, we could classify bounded metrics for the helper T cells and epithelial progenitors with upper bound positive predictive values of 0.43±0.03 (parent class prevalence 0.21) and 0.94±0.02 (parent class prevalence 0.49) when using ground truth centroid information.ConclusionsThis is the first work to provide cell type classification for helper T and epithelial progenitor nuclei on H&E.

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Proceedings Article | 3 April 2024 Presentation + Paper

Cell spatial analysis in Crohn's disease: unveiling local cell arrangement pattern with graph-based signatures

Shunxing Bao, Sichen Zhu, Vasantha Kolachala, Lucas Remedios, Yeonjoo Hwang, Yutong Sun, Ruining Deng, Can Cui, Rendong Zhang, Yike Li, Jia Li, Joseph Roland, Qi Liu, Ken Lau, Subra Kugathasan, Peng Qiu, Keith Wilson, Lori Coburn, Bennett Landman, Yuankai Huo

Proceedings Volume 12933, 1293314 (2024) https://doi.org/10.1117/12.3006675

KEYWORDS: Connective tissue, Plasma, Visualization, Diseases and disorders, Biopsy, Spatial analysis, Medicine, Biological samples, Tissues, Image segmentation

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Proceedings Article | 3 April 2024 Presentation + Paper

Spatial pathomics toolkit for quantitative analysis of podocyte nuclei with histology and spatial transcriptomics data in renal pathology

Jiayuan Chen, Yu Wang, Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao, Yilin Liu, Jianyong Zhong, Agnes Fogo, Haichun Yang, Shilin Zhao, Yuankai Huo

Proceedings Volume 12933, 1293310 (2024) https://doi.org/10.1117/12.3006318

KEYWORDS: Feature extraction, Statistical analysis, Image segmentation, Pathology, Biological samples, Kidney, Cell phenotyping, Tumors, Injuries, Visualization

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Proceedings Article | 3 April 2024 Presentation + Paper

Nucleus subtype classification using inter-modality learning

Lucas Remedios, Shunxing Bao, Samuel Remedios, Ho Hin Lee, Leon Cai, Thomas Li, Ruining Deng, Can Cui, Jia Li, Qi Liu, Ken Lau, Joseph Roland, Mary Washington, Lori Coburn, Keith Wilson, Yuankai Huo, Bennett Landman

Proceedings Volume 12933, 129330F (2024) https://doi.org/10.1117/12.3006237

KEYWORDS: Machine learning, Colon, Pathology, Multiplexing, Human physiology, Clinical research, Biological imaging, Artificial intelligence

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SPIE Journal Paper | 5 January 2024 Open Access

Robust fiber orientation distribution function estimation using deep constrained spherical deconvolution for diffusion-weighted magnetic resonance imaging

Tianyuan Yao, Francois Rheault, Leon Y. Cai, Vishwesh Nath, Zuhayr Asad, Nancy Newlin, Can Cui, Ruining Deng, Karthik Ramadass, Andrea Shafer, Susan Resnick, Kurt Schilling, Bennett A. Landman, Yuankai Huo

JMI, Vol. 11, Issue 01, 014005, (January 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.1.014005

KEYWORDS: Data modeling, Diffusion, Voxels, Education and training, Reproducibility, Modeling, 3D modeling, Brain, Silver, Performance modeling

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PurposeDiffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF). This function is the essential first step for the downstream tractography and connectivity analyses. With recent advantages in data sharing, large-scale multisite DW-MRI datasets are being made available for multisite studies. However, measurement variabilities (e.g., inter- and intrasite variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods [e.g., constrained spherical deconvolution (CSD)] and learning-based methods (e.g., deep learning) do not explicitly consider such variabilities in fODF modeling, which consequently leads to inferior performance on multisite and/or longitudinal diffusion studies.ApproachIn this paper, we propose a data-driven deep CSD method to explicitly constrain the scan–rescan variabilities for a more reproducible and robust estimation of brain microstructure from repeated DW-MRI scans. Specifically, the proposed method introduces a three-dimensional volumetric scanner-invariant regularization scheme during the fODF estimation. We study the Human Connectome Project (HCP) young adults test–retest group as well as the MASiVar dataset (with inter- and intrasite scan/rescan data). The Baltimore Longitudinal Study of Aging dataset is employed for external validation.ResultsFrom the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation. By introducing the contrastive loss with scan/rescan data, the proposed method achieved a higher consistency while maintaining higher angular correlation coefficients with the CSD modeling. The proposed method is assessing the downstream connectivity analysis and shows increased performance in distinguishing subjects with different biomarkers.ConclusionWe propose a deep CSD method to explicitly reduce the scan–rescan variabilities, so as to model a more reproducible and robust brain microstructure from repeated DW-MRI scans. The plug-and-play design of the proposed approach is potentially applicable to a wider range of data harmonization problems in neuroimaging.

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Proceedings Article | 7 April 2023 Presentation

Influence of cell-type ratio on spatially resolved single-cell transcriptomes using the Tangram algorithm: based on implementation on single-cell and MxIF data

Can Cui, Shunxing Bao, Jia Li, Ruining Deng, Lucas Remedios, Zuhayr Asad, Sophie Chiron, Ken Lau, Yaohong Wang, Lori Coburn, Keith Wilson, Joseph T. Roland, Bennett Landman, Qi Liu, Yuankai Huo

Proceedings Volume 12471, 124710A (2023) https://doi.org/10.1117/12.2654135

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Proceedings Article | 7 April 2023 Presentation + Paper

Topological-preserving membrane skeleton segmentation in multiplex immunofluorescence imaging

Shunxing Bao, Can Cui, Jia Li, Yucheng Tang, Ho Hin Lee, Ruining Deng, Lucas Remedios, Xin Yu, Qi Yang, Sophie Chiron, Nathan Heath Patterson, Ken Lau, Qi Liu, Joseph Roland, Lori Coburn, Keith Wilson, Bennett Landman, Yuankai Huo

Proceedings Volume 12471, 124710B (2023) https://doi.org/10.1117/12.2654087

KEYWORDS: Image segmentation, Deep learning, Education and training, Tissues, Biological imaging, RGB color model, Medicine, Image enhancement, Diseases and disorders, Cross validation

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Proceedings Article | 7 April 2023 Presentation + Paper

Predicting Crohn’s disease severity in the colon using mixed cell nucleus density from pseudo labels

Lucas Remedios, Shunxing Bao, Cailey Kerley, Leon Cai, François Rheault, Ruining Deng, Can Cui, Sophie Chiron, Ken Lau, Joseph Roland, Mary Washington, Lori Coburn, Keith Wilson, Yuankai Huo, Bennett Landman

Proceedings Volume 12471, 1247116 (2023) https://doi.org/10.1117/12.2653918

KEYWORDS: Diseases and disorders, Cross validation, Tissues, Image segmentation, Biopsy, Histograms, Colon, Binary data

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Proceedings Article | 3 April 2023 Presentation + Paper

Deep constrained spherical deconvolution for robust harmonization

Tianyuan Yao, Francois Rheault, Leon Cai, Vishwesh Nath, Zuhayr Asad, Nancy Newlin, Can Cui, Ruining Deng, Karthik Ramadass, Kurt Schilling, Bennett Landman, Yuankai Huo

Proceedings Volume 12464, 124640W (2023) https://doi.org/10.1117/12.2654398

KEYWORDS: Diffusion, Voxels, Deep learning, Education and training, Data modeling, Spherical harmonics, Tolerancing, White matter, Spherical lenses, Reconstruction algorithms

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Proceedings Article | 4 April 2022 Poster + Paper

Eosinophilic esophagitis multi-label feature recognition on whole slide imaging using transfer learning

Yuxuan Shi, Quan Liu, Jiachen Xu, Zuhayr Asad, Can Cui, Hernán Correa, Yash Choksi, Girish Hiremath, Yuankai Huo

Proceedings Volume 12039, 1203919 (2022) https://doi.org/10.1117/12.2611521

KEYWORDS: Tissues, Distributed interactive simulations, Data modeling, Biopsy, Image segmentation, Performance modeling, Binary data, Visualization, Image processing, Image classification

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Proceedings Article | 4 April 2022 Presentation + Paper

Multi-modal learning with missing data for cancer diagnosis using histopathological and genomic data

Can Cui, Zuhayr Asad, William Dean, Isabelle Smith, Christopher Madden, Shunxing Bao, Bennett Landman, Joseph Roland, Lori Coburn, Keith Wilson, Jeffrey Zwerner, Shilin Zhao, Lee Wheless, Yuankai Huo

Proceedings Volume 12033, 120331D (2022) https://doi.org/10.1117/12.2612318

KEYWORDS: Data modeling, Feature extraction, Integrated modeling, Genomics, Data fusion, Cancer, Image fusion, Performance modeling, Tumor growth modeling, Image classification

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Proceedings Article | 4 April 2022 Poster + Paper

Multi-modality classification between myxofibrosarcoma and myxoma using radiomics and machine learning models

Can Cui, Samuel Johnson, Cullen Moran, Katherine Hajdu, Joanna Shechtel, John Block, Brian Bingham, David Smith, Leo Luo, Hakmook Kang, Jennifer Halpern, Herbert Schwartz, Ginger Holt, Joshua Lawrenz, Benoit Dawant

Proceedings Volume 12033, 1203330 (2022) https://doi.org/10.1117/12.2612311

KEYWORDS: Tumors, Image segmentation, Machine learning, Feature selection, Feature extraction, Magnetic resonance imaging, Data modeling, Tissues, Biopsy, Statistical modeling

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Proceedings Article | 4 April 2022 Poster + Paper

Integrate memory efficiency methods for self-supervised learning on pathological image analysis

Quan Liu, Bryan Millis, Zuhayr Asad, Can Cui, William Dean, Isabelle Smith, Christopher Madden, Joseph Roland, Jeffrey Zwerner, Shilin Zhao, Lee Wheless, Yuankai Huo

Proceedings Volume 12032, 120322M (2022) https://doi.org/10.1117/12.2607976

KEYWORDS: Data modeling, Performance modeling, Parallel computing, Image analysis, Instrument modeling, Process modeling, Pathology, Neural networks, Data processing, Skin cancer

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Proceedings Article | 15 February 2021 Presentation + Paper

Brain vessel segmentation in contrast-enhanced T1-weighted MR Images for deep brain stimulation of the anterior thalamus using a deep convolutional neural network

Can Cui, Han Liu, Dario Englot, Benoit Dawant

Proceedings Volume 11598, 115980K (2021) https://doi.org/10.1117/12.2581896

KEYWORDS: Image segmentation, Thalamus, Neuroimaging, Magnetic resonance imaging, Brain stimulation, Brain, Convolutional neural networks, Epilepsy, Electrodes

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Showing 5 of 14 publications

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