Deep Learning-Inferred Multiplex ImmunoFluorescence for Immunohistochemical Image Quantification.

ABSTRACT: Reporting biomarkers assessed by routine immunohistochemical (IHC) staining of tissue is broadly used in diagnostic pathology laboratories for patient care. To date, clinical reporting is predominantly qualitative or semi-quantitative. By creating a multitask deep learning framework referred to as 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 expensive-yet-informative mpIF images, while simultaneously providing the essential ground truth for the superimposed brightfield IHC channels. Moreover, a new n

SUBMITTER: Ghahremani P 

PROVIDER: S-EPMC9477216 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature