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 are presenting a single step solution to nuclear segmentation and quantitative single-cell IHC scoring. Leveraging a unique de novo dataset of co-registered IHC and multiplex immunoflourescence (mpIF) data generated from the same tissue section, we simultaneously segment and translate low-cost and prevalent IHC slides to more expensive-yet-informative mpIF images. Moreover, a nuclear-pore marker, LAP2beta, is co-registered to improve cell segmentation and protein expression quantification on IHC slides. By formulating the IHC quantification as cell instance segmentation/classification rather than cell detection problem, we show that our model trained on clean IHC Ki67 data can generalize to more noisy and artifact-ridden images as well as other nuclear and non-nuclear markers such as CD3, CD8, BCL2, BCL6, MYC, MUM1, CD10 and TP53. We thoroughly evaluate our method on publicly available benchmark datasets as well as against pathologists' semi-quantitative scoring. The code, trained models, and the resultant embeddings for all the datasets used in this paper will be released at https://github.com/nadeemlab/DeepLIIF.
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