Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology
Year: 2024
Type: preprint
Abstract: Understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions. H&E is widely available, however, cell subtyping often requires expert knowledge and the use of specialized stains. To reduce the annotation burden, AI has been proposed for the classification of cells on H&E. For example, the recent Colon Nucleus Identification and Classification (CoNIC) Challenge focused on labeling 6 cell types on H&E of the colon. However, the CoNIC Challenge was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). We use inter-modality learning to label previously un-labelable cell types on H&E. We take advantage of multiplexed immunofluorescence (MxIF) histology to label 14 cell subclasses. We performed style transfer on the same MxIF tissues to synthesize realistic virtual H&E which we paired with the MxIF-derived cell subclassification labels. We evaluated the efficacy of using a supervised learning scheme where the input was realistic-quality virtual H&E and the labels were MxIF-derived cell subclasses. We assessed our model on private virtual H&E and public real H&E. On virtual H&E, we were able to classify helper T cells and epithelial progenitors with positive predictive values of $0.34 \pm 0.15$ (prevalence $0.03 \pm 0.01$) and $0.47 \pm 0.1$ (prevalence $0.07 \pm 0.02$) respectively, when using ground truth centroid information. On real H&E we could classify helper T cells and epithelial progenitors with upper bound positive predictive values of $0.43 \pm 0.03$ (parent class prevalence 0.21) and $0.94 \pm 0.02$ (parent class prevalence 0.49) when using ground truth centroid information. This is the first work to provide cell type classification for helper T and epithelial progenitor nuclei on H&E.
Source: arXiv (Cornell University)
Authors: Remedios, Lucas W., Bao, Shunxing, Remedios, Samuel W., Lee, Ho Hin, Cai, Leon Y., Li, Thomas, Deng, Ruining, Newlin, Nancy R., Saunders, Adam M., Cui, Can, Li, Jia, Liu, Qi, Lau, Ken S., Roland, Joseph T., Washington, Mary K, Coburn, Lori A., Wilson, Keith T., Huo, Yuankai, Landman, Bennett A.
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Veterans Affairs, Vanderbilt University, Leona M. and Harry B. Helmsley Charitable Trust, Nvidia, Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Georgia Clinical and Translational Science Alliance, National Institutes of Health, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, National Center for Research Resources, National Center for Advancing Translational Sciences
Awards: G1016015727, G1173929046, G1182793083, G1390942713, G1473319671, G1531600799, CAREER: Modeling Personalized Brain Development with Big Data, G1942042315, G1951620746, G2043477120, G2109392540, G2209161966, G2308744219, G2537422520, G2591101265, G2759730755, Tuft cell heterogeneity in function, lineage, and structure in ileal inflammatory disease, G3002259123, NSF Convergence Accelerator Track D: Scalable, TRaceable Ai for Imaging Translation: Innovation to Implementation for Accelerated Impact (STRAIT I3), G326042038, G3619590958, G3724712001, Medical Scientist Training Program, G3958555850, G4107354744, G4195101910, G4290769314, G4425699080, Graduate Research Fellowship Program (GRFP), G560889077, G5614773401, G563474107, G5679716143, G5868276417, The Vanderbilt Institute for Clinical and Translational Research (VICTR) UL1, G6246474086, G6263508347, G64116031, Spermidine as a New Therapy for Colitis and Chemopreventive for Colitis-associated Carcinogenesis, G6671297155, G6704072150, G6988210397, G7307553525, G7358180766, G7412434281, G7653134492, The Vanderbilt Institute for Clinical and Translational Research (VICTR), G8159288582, G8287001021, G9000001553, G9000033427, G9000041293, G9000043901, G9000117310, G9000128600, G9000141600, G9000278956, G9000300101, Molecular and Cellular Basis for Digestive Diseases, G949362299, G959610197
OpenAlex ID: W4400488398