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Intravital Microscopy

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Deep learning for in vivo near-infrared imaging.

Proceedings of the National Academy of Sciences of the United States of America
Detecting fluorescence in the second near-infrared window (NIR-II) up to ∼1,700 nm has emerged as a novel in vivo imaging modality with high spatial and temporal resolution through millimeter tissue depths. Imaging in the NIR-IIb window (1,500-1,700 ...

Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy.

Scientific reports
Microvascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcir...

A machine learning approach for single cell interphase cell cycle staging.

Scientific reports
The cell nucleus is a tightly regulated organelle and its architectural structure is dynamically orchestrated to maintain normal cell function. Indeed, fluctuations in nuclear size and shape are known to occur during the cell cycle and alterations in...

ACME: Automatic feature extraction for cell migration examination through intravital microscopy imaging.

Medical image analysis
Cell detection and tracking applied to in vivo fluorescence microscopy has become an essential tool in biomedicine to characterize 4D (3D space plus time) biological processes at the cellular level. Traditional approaches to cell motion analysis by m...

In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment.

Journal of biomedical optics
SIGNIFICANCE: There have been numerous academic and commercial efforts to develop high-resolution in vivo microscopes for a variety of clinical use cases, including early disease detection and surgical guidance. While many high-profile studies, comme...

Enhancing Total Optical Throughput of Microscopy with Deep Learning for Intravital Observation.

Small methods
The significance of performing large-depth dynamic microscopic imaging in vivo for life science research cannot be overstated. However, the optical throughput of the microscope limits the available information per unit of time, i.e., it is difficult ...

Improving flat fluorescence microscopy in scattering tissue through deep learning strategies.

Optics express
Intravital microscopy in small animals growingly contributes to the visualization of short- and long-term mammalian biological processes. Miniaturized fluorescence microscopy has revolutionized the observation of live animals' neural circuits. The te...

Intravital microscopy visualizes innate immune crosstalk and function in tissue microenvironment.

European journal of immunology
Significant advances have been made in the field of intravital microscopy (IVM) on myeloid cells due to the growing number of validated fluorescent probes and reporter mice. IVM provides a visualization platform to directly observe cell behavior and ...

Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy.

Nature communications
Ultrasound localization microscopy (ULM) enables deep tissue microvascular imaging by localizing and tracking intravenously injected microbubbles circulating in the bloodstream. However, conventional localization techniques require spatially isolated...

Structured adaptive boosting trees for detection of multicellular aggregates in fluorescence intravital microscopy.

Microvascular research
Fluorescence intravital microscopy captures large data sets of dynamic multicellular interactions within various organs such as the lungs, liver, and brain of living subjects. In medical imaging, edge detection is used to accurately identify and deli...