Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images.

ABSTRACT:

Background

Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of the unique colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our

SUBMITTER: Fassler DJ 

PROVIDER: S-EPMC7385962 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature