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Proceedings Paper

Automated multi-class ground-truth labeling of H&E images for deep learning using multiplexed fluorescence microscopy
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Paper Abstract

Manual annotation of Hematoxylin and Eosin (H&E) stained tissue images for deep learning classification is difficult, time consuming, and error-prone particularly for multi-class and rare-class problems. Chemical probes in immunohistochemistry (IHC) or immunofluorescence (IF) can automatically tag cellular structures; however, chemical labeling is difficult to use in training a deep classifier for H&E images (e.g. through serial sectioning and registration). In this work, we leverage the novel Multiplexed Immuno-Fluorescencent (MxIF) microscopy method developed by General Electric Global Research Center (GE GRC) which allows sequential, stain-image-bleach (SSB) application of protein markers on formalin-fixed, paraffin-embedded(FFPE) samples followed by traditional H&E staining to build chemically-annotated tissue maps of nuclei, cytoplasm, and cell membranes. This allows us to automate the creation of ground truth class-label maps for training an H&E-based tissue classifier. In this study, a tissue microarray consisting of 149 breast cancer and normal tissue cores were stained using MxIF for our three analytes, followed by traditional H&E staining. The MxIF stains for each TMA core were combined to create a “Virtual H&E” image, which is registered with the corresponding real H&E images. Each MxIF stained spot was segmented to obtain a class-label map for each analyte, which was then applied to the real H&E image to build a dataset consisting of the three analytes. A convolutional neural network (CNN) was then trained to classify this dataset. This system achieved an overall accuracy of 70%, suggesting that the MxIF system can provide useful labels for identifying hard to distinguish structures. A U-net was also trained to generate pseudo-IF stains from H&E and resulted in similar results.

Paper Details

Date Published: 18 March 2019
PDF: 10 pages
Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560J (18 March 2019); doi: 10.1117/12.2512991
Show Author Affiliations
Gouthamrajan Nadarajan, Univ. at Buffalo SUNY (United States)
Tyna Hope, Biomarker Imaging Research Lab., (BIRL), Sunnybrook Research Institute, Univ. of Toronto (Canada)
Dan Wang, Biomarker Imaging Research Lab., (BIRL), Sunnybrook Research Institute, Univ. of Toronto (Canada)
Alison Cheung, Biomarker Imaging Research Lab., (BIRL), Sunnybrook Research Institute, Univ. of Toronto (Canada)
Fiona Ginty, General Electric Global Research Ctr. (United States)
Martin J. Yaffe, Biomarker Imaging Research Lab., (BIRL), Sunnybrook Research Institute, Univ. of Toronto (Canada)
Scott Doyle, Univ. at Buffalo SUNY (United States)


Published in SPIE Proceedings Vol. 10956:
Medical Imaging 2019: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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