Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Deep Learning-Inferred Multiplex ImmunoFluorescence for IHC Image Quantification

Parmida Ghahremani, Yanyun Li, Arie Kaufman, Rami Vanguri, View ORCID ProfileNoah Greenwald, View ORCID ProfileMichael Angelo, Travis J. Hollmann, View ORCID ProfileSaad Nadeem
doi: https://doi.org/10.1101/2021.05.01.442219
Parmida Ghahremani
1Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yanyun Li
3Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Arie Kaufman
1Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rami Vanguri
3Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Noah Greenwald
2Department of Pathology, Stanford University, Stanford, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Noah Greenwald
Michael Angelo
2Department of Pathology, Stanford University, Stanford, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Michael Angelo
Travis J. Hollmann
3Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Saad Nadeem
4Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Saad Nadeem
  • For correspondence: nadeems@mskcc.org
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

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 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 Statement

The authors have declared no competing interest.

Footnotes

  • ↵* Co-senior authors

  • Revised manuscript

  • https://github.com/nadeemlab/DeepLIIF

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted October 08, 2021.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Deep Learning-Inferred Multiplex ImmunoFluorescence for IHC Image Quantification
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Deep Learning-Inferred Multiplex ImmunoFluorescence for IHC Image Quantification
Parmida Ghahremani, Yanyun Li, Arie Kaufman, Rami Vanguri, Noah Greenwald, Michael Angelo, Travis J. Hollmann, Saad Nadeem
bioRxiv 2021.05.01.442219; doi: https://doi.org/10.1101/2021.05.01.442219
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Deep Learning-Inferred Multiplex ImmunoFluorescence for IHC Image Quantification
Parmida Ghahremani, Yanyun Li, Arie Kaufman, Rami Vanguri, Noah Greenwald, Michael Angelo, Travis J. Hollmann, Saad Nadeem
bioRxiv 2021.05.01.442219; doi: https://doi.org/10.1101/2021.05.01.442219

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4234)
  • Biochemistry (9135)
  • Bioengineering (6784)
  • Bioinformatics (23999)
  • Biophysics (12129)
  • Cancer Biology (9534)
  • Cell Biology (13777)
  • Clinical Trials (138)
  • Developmental Biology (7635)
  • Ecology (11701)
  • Epidemiology (2066)
  • Evolutionary Biology (15512)
  • Genetics (10644)
  • Genomics (14325)
  • Immunology (9482)
  • Microbiology (22839)
  • Molecular Biology (9090)
  • Neuroscience (48989)
  • Paleontology (355)
  • Pathology (1482)
  • Pharmacology and Toxicology (2570)
  • Physiology (3845)
  • Plant Biology (8331)
  • Scientific Communication and Education (1471)
  • Synthetic Biology (2296)
  • Systems Biology (6190)
  • Zoology (1301)