Claire McQuin

TL;DR: CellProfiler 3.0 is described, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional image stacks, increasingly common in biomedical research.

TL;DR: The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction.

TL;DR: An evaluation framework is presented to measure accuracy, types of errors, and computational efficiency of deep learning strategies and classical approaches; and it is shown that deep learning improves accuracy and can reduce the number of biologically relevant errors by half.

TL;DR: This work presents an evaluation framework to measure accuracy, types of errors, and computational efficiency; and uses it to compare two deep learning strategies (U-Net and DeepCell) alongside a classical approach implemented in CellProfiler.

TL;DR: A strategy to avoid human subjectivity by assessing the quality of red blood cells using imaging flow cytometry and deep learning is developed, which revealed a chronological progression of morphological changes that better predicted blood quality, as measured by physiological hemolytic assay readouts, than the conventional expert-assessed morphology classification system.