Abstract 2256: Efficient large-scale cell classification and analysis for MultiOmyxTMassays: A deep learning approach

Abstract

Abstract Traditional immunohistochemistry (IHC) techniques utilize one slide per biomarker. When clinical samples are precious and the number of serial sections is limited, comprehensive biomarker profiling becomes difficult with IHC. MultiOmyx is a proprietary, multiplexing methodology capable of staining up to 60 biomarkers on the same slide. The output of the assay enables quantitative profiling of tissues at a single cell level. The assay generates data for millions of cells with billions of queryable data points. To detect and classify cells efficiently at this large scale, we developed an image analysis framework using Deep Learning. Our framework consists of seven major steps: (1) manual annotation of a small subset of the nuclear staining channel (DAPI); (2) training of a fully convolutional neural network [1] on this annotation-set to generate a feature map identifying cell centers; (3) application of the trained network in (2) on the nuclear stain (DAPI) of the entire dataset to delineate individual cells; (4) manual annotation of a small subset of each of the other biomarker channels; (5) training of a convolutional neural network on these annotation-sets for binary classification of each of the biomarkers; (6) application of these classifiers to the entire dataset; (7) combination of the binary classification results to identify phenotypes of interest. For our output, we provide both visual label maps and classification summary tables for individual and co-localized biomarkers at the region of interest level and the entire slide level. In addition, combining the phenotype and location information allows us to visualize complex spatial relationships in the tissue. The benefits of using this Deep Learning framework are greatly felt through increased time efficiency without a loss in accuracy, when compared to more traditional computer vision methods requiring high levels of parameter fine-tuning. For future work, we plan on fully automating the approach as more manual annotation-sets are generated. Citation Format: Mate L. Nagy, Arezoo Hanifi, Ahalya Tirupsur, Geoffrey Wong, Jun Fang, Nicholas Hoe, Qingyan Au, Raghav K. Padmanabhan. Efficient large-scale cell classification and analysis for MultiOmyxTMassays: A deep learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2256.