A cell-level quality control workflow for high-throughput image analysis.

ABSTRACT: BACKGROUND:Image-based high throughput (HT) screening provides a rich source of information on dynamic cellular response to external perturbations. The large quantity of data generated necessitates computer-aided quality control (QC) methodologies to flag imaging and staining artifacts. Existing image- or patch-level QC methods require separate thresholds to be simultaneously tuned for each image quality metric used, and also struggle to distinguish between artifacts and valid cellular phenotypes. As a result, extensive time and effort must be spent on per-assay QC feature thresholding, and valid images and phenotypes may be discarded while image- and cell-level artifacts go undetected. RESULTS:We present a novel cell-level QC workflow built on machine learning approaches for classifying a

SUBMITTER: Qiu M 

PROVIDER: S-EPMC7333376 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature