RT Journal Article
SR Electronic
T1 DeepLIIF: Deep Learning-Inferred Multiplex ImmunoFluorescence for IHC Image Quantification
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.05.01.442219
DO 10.1101/2021.05.01.442219
A1 Parmida Ghahremani
A1 Yanyun Li
A1 Arie Kaufman
A1 Rami Vanguri
A1 Noah Greenwald
A1 Michael Angelo
A1 Travis J. Hollmann
A1 Saad Nadeem
YR 2021
UL http://biorxiv.org/content/early/2021/07/27/2021.05.01.442219.abstract
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.