Label-free, live cell recognition (ie instance segmentation) and tracking using computer
vision-aided recognition can be a powerful tool that rapidly generates multi-modal readouts …

In silico labeling is the computational cross-modality image translation where the output
modality is a subcellular marker that is not specifically encoded in the input image, for …

To facilitate the characterization of unlabeled induced pluripotent stem cells (iPSCs) during
culture and expansion, we developed an AI pipeline for nuclear segmentation and mitosis …

Background Stem cell research, particularly in the domain of induced pluripotent stem cell
(iPSC) technology, has shown significant progress. The integration of artificial intelligence …

Label-free optical technologies have transformed cell culture monitoring, offering
noninvasive alternatives that preserve cellular integrity─ a critical factor in biomedical …

Significance Advancements in label-free microscopy could provide real-time, non-invasive
imaging with unique sources of contrast and automated standardized analysis to …

Significance Accurate cell segmentation and classification in three-dimensional (3D) images
are vital for studying live cell behavior and drug responses in 3D tissue culture. Evaluating …

Significance Heart disease is the leading cause of death in the United States, yet research is
limited by the inability to culture primary cardiac cells. Cardiomyocytes (CMs) derived from …

In silico labeling prediction of organelle fluorescence from label-free microscopy images has
the potential to revolutionize our understanding of cells as integrated complex systems …