Prof. Qi Liu Profile

Prof. Qi Liu

at Vanderbilt Univ Medical Ctr

SPIE Involvement:

Author

Publications (6)

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

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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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 | 15 February 2021 Presentation + Paper

A cross-platform informatics system for the Gut Cell Atlas: integrating clinical, anatomical and histological data

Shunxing Bao, Sophie Chiron, Yucheng Tang, Cody Heiser, Austin Southard-Smith, Ho Hin Lee, Marisol Ramirez, Yuankai Huo, Mary Washington, Elizabeth Scoville, Joseph Roland, Qi Liu, Ken Lau, Keith Wilson, Lori Coburn, Bennett Landman

Proceedings Volume 11601, 1160106 (2021) https://doi.org/10.1117/12.2581074

KEYWORDS: Information science, Tissues, System integration, Profiling, Electronic health records, Data storage, Biopsy, Analytical research

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Showing 5 of 6 publications

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