Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification

Multiplex immunofluorescence imaging can provide a wealth of data compared to immunohistochemical staining, which is cheaper and more widely available. Ghahremani et al. present DeepLIIF, a GAN-based cell segmentation and classification approach, which is trained on co-registered images of these two modalities to provide the insights from the more data-rich muliplex data from simpler IHC images. Reporting biomarkers assessed by routine immunohistochemical (IHC) staining of tissue is broadly used in diagnostic pathology laboratories for patient care. So far, however, clinical reporting is predominantly qualitative or semi-quantitative. By creating a multitask deep learning framework, DeepLIIF, we present a single-step solution to stain deconvolution/separation, cell segmentation and quantitative single-cell IHC scoring. Leveraging a unique de novo dataset of co-registered IHC and multiplex immunofluorescence (mpIF) staining of the same slides, we segment and translate low-cost and prevalent IHC slides to more informative, but also more expensive, mpIF images, while simultaneously providing the essential ground truth for the superimposed brightfield IHC channels. A new nuclear-envelope stain, LAP2beta, with high (>95%) cell coverage is also introduced to improve cell delineation/segmentation and protein expression quantification on IHC slides. We show that DeepLIIF trained on clean IHC Ki67 data can generalize to noisy images as well as other nuclear and non-nuclear markers.