PT - JOURNAL ARTICLE AU - Parmida Ghahremani AU - Yanyun Li AU - Arie Kaufman AU - Rami Vanguri AU - Noah Greenwald AU - Michael Angelo AU - Travis J. Hollmann AU - Saad Nadeem TI - DeepLIIF: Deep Learning-Inferred Multiplex ImmunoFluorescence for IHC Image Quantification AID - 10.1101/2021.05.01.442219 DP - 2021 Jan 01 TA - bioRxiv PG - 2021.05.01.442219 4099 - http://biorxiv.org/content/early/2021/07/27/2021.05.01.442219.short 4100 - http://biorxiv.org/content/early/2021/07/27/2021.05.01.442219.full AB - 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 nuclear envelop stain, LAP2beta, with high (>95%) cell coverage is introduced to improve cell delineation/segmentation and protein expression quantification on IHC slides. By simultaneously translating input IHC images to clean/separated mpIF channels and performing cell segmentation/classification, 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, the pre-trained models along with easy-to-run containerized docker files as well as Google CoLab project are available at https://github.com/nadeemlab/deepliif.Competing Interest StatementThe authors have declared no competing interest.