Nucleus subtype classification using inter-modality learning

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

doi:10.1117/12.3006237

3 April 2024 Nucleus subtype classification using inter-modality learning

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

Author Affiliations +

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 129330F (2024) https://doi.org/10.1117/12.3006237
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available both for clinical studies and research. The Colon Nucleus Identification and Classification (CoNIC) Challenge has recently innovated on robust artificial intelligence labeling of six cell types on H&E stains of the colon. However, this is a very small fraction of the number of potential cell classification types. Specifically, the CoNIC Challenge is unable to classify epithelial subtypes (progenitor, endocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), or connective subtypes (fibroblasts, stromal). In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E. We leveraged multiplexed immunofluorescence (MxIF) histology imaging to identify 14 subclasses of cell types. We performed style transfer to synthesize virtual H&E from MxIF and transferred the higher density labels from MxIF to these virtual H&E images. We then evaluated the efficacy of learning in this approach. We identified helper T and progenitor nuclei 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 on virtual H&E. This approach represents a promising step towards automating annotation in digital pathology.

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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios, Ho Hin Lee, Leon Cai, Thomas Li, Ruining Deng, Can Cui, Jia Li, Qi Liu, Ken S. Lau, Joseph T. Roland, Mary K. Washington, Lori A. Coburn, Keith T. Wilson, Yuankai Huo, and Bennett A. Landman "Nucleus subtype classification using inter-modality learning", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 129330F (3 April 2024); https://doi.org/10.1117/12.3006237

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