Translator Disclaimer
18 March 2019 Automated multi-class ground-truth labeling of H&E images for deep learning using multiplexed fluorescence microscopy
Author Affiliations +
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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gouthamrajan Nadarajan, Tyna Hope, Dan Wang, Alison Cheung, Fiona Ginty, Martin J. Yaffe, and Scott Doyle "Automated multi-class ground-truth labeling of H&E images for deep learning using multiplexed fluorescence microscopy", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560J (18 March 2019); doi: 10.1117/12.2512991; https://doi.org/10.1117/12.2512991
PROCEEDINGS
10 PAGES


SHARE
Advertisement
Advertisement
Back to Top