Several disease states, including cancer and autoimmunity, are characterized by the infiltration of large populations of immune cells into organ tissue. The degree and composition of these invading cells have been correlated with patient outcomes, suggesting that the intercellular interactions occurring in inflamed tissue play a role in pathology. Immunofluorescence staining paired with confocal microscopy produce detailed visualizations of these interactions. Applying computer vision and machine learning methods to the resulting images allows for robust quantification of immune infiltrates. We are developing an analytical pipeline to assess the immune environments of two distinct disease states: lupus nephritis and triple-negative breast cancer (TNBC). Biopsies of inflamed kidney tissue (lupus) and tumors (TNBC) were stained and imaged for panels of 20 markers using a strip-reprobe technique. This set of markers interrogates populations of T-cells, B-cells, and antigen presenting cells. To detect T cells, we first trained a U-Net to segment CD3+CD4+ T-cells in images of lupus biopsies and achieved an object-level precision of 0.855 and recall of 0.607 on an independent test set. We then evaluated the generalizability of this network to CD3+CD8+ T cells in lupus nephritis and CD3+CD4+ T cells in TNBC, and the extent to which fine-tuning the network improved performance for these cell types. We found that recall increased moderately with fine-tuning, while precision did not. Further work will focus on developing robust methods of segmenting a larger variety of T cell markers in both tissue contexts with high fidelity.
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