Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification Journal Article
| Authors: | Ghahremani, P.; Li, Y.; Kaufman, A.; Vanguri, R.; Greenwald, N.; Angelo, M.; Hollmann, T. J.; Nadeem, S. |
| Article Title: | 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. So far, however, clinical reporting is predominantly qualitative or semi-quantitative. By creating a multitask deep learning framework, 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 informative, but also more expensive, mpIF images, while simultaneously providing the essential ground truth for the superimposed brightfield IHC channels. A new nuclear-envelope stain, LAP2beta, with high (>95%) cell coverage is also introduced to improve cell delineation/segmentation and protein expression quantification on IHC slides. We show that DeepLIIF trained on clean IHC Ki67 data can generalize to noisy images as well as other nuclear and non-nuclear markers. © 2022, The Author(s), under exclusive licence to Springer Nature Limited. |
| Keywords: | cytology; fluorescence; patient care; diagnosis; costs; cells; image segmentation; single-step; single cells; immunohistochemical staining; learning frameworks; deep learning; cell segmentation; clinical reporting; deconvolutions; image quantification; immunofluorescence staining |
| Journal Title: | Nature Machine Intelligence |
| Volume: | 4 |
| Issue: | 4 |
| ISSN: | 2522-5839 |
| Publisher: | Nature Publishing Group |
| Date Published: | 2022-04-01 |
| Start Page: | 401 |
| End Page: | 412 |
| Language: | English |
| DOI: | 10.1038/s42256-022-00471-x |
| PROVIDER: | scopus |
| PMCID: | PMC9477216 |
| PUBMED: | 36118303 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 2 May 2022 -- Source: Scopus |
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