16 research outputs found
AI-powered transmitted light microscopy for functional analysis of live cells
Get PDFTransmitted light microscopy can readily visualize the morphology of living cells. Here, we introduce artificial-intelligence-powered transmitted light microscopy (AIM) for subcellular structure identification and labeling-free functional analysis of live cells. AIM provides accurate images of subcellular organelles; allows identification of cellular and functional characteristics (cell type, viability, and maturation stage); and facilitates live cell tracking and multimodality analysis of immune cells in their native form without labeling
Micro-patterned agarose gel devices for single-cell high-throughput microscopy of E. coli cells
No full textIterative unsupervised domain adaptation for generalized cell detection from brightfield z-stacks
No full textAutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques
No full textQuanti.us: a tool for rapid, flexible, crowd-based annotation of images
No full textWe describe Quanti.us, a crowd-based image-annotation platform that provides an accurate alternative to computational algorithms for difficult image-analysis problems. We used Quanti.us for a variety of medium-throughput image-analysis tasks and achieved 10–50× savings in analysis time compared with that required for the same task by a single expert annotator. We show equivalent deep learning performance for Quanti.us-derived and expert-derived annotations, which should allow scalable integration with tailored machine learning algorithms
DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data
No full text
