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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Automatic cell identification and visualization using digital holographic microscopy with head mounted augmented

Timothy O'Connor, Siddharth Rawat, Adam Markman

    Applied Optics
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    PubMed
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    This study introduces an augmented reality (AR) system integrated with digital holographic microscopy for automated cell identification. The novel AR smart glasses enable rapid visualization and analysis of cell morphology for quick diagnoses.

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    Area of Science:

    • Biomedical Imaging
    • Optical Microscopy
    • Augmented Reality

    Background:

    • Automated cell identification is crucial for diagnostics.
    • Digital holographic microscopy (DHM) offers non-invasive 3D cell imaging.
    • Augmented reality (AR) has potential in medical visualization.

    Purpose of the Study:

    • To develop and demonstrate a compact imaging system integrating DHM with AR for automated cell identification and visualization.
    • To enable real-time 3D cell profiling and classification using AR smart glasses.

    Main Methods:

    • A shearing interferometer produced holograms of biological cells.
    • Customized smart glasses with an external camera recorded holograms.
    • Image segmentation, digital reconstruction, and feature extraction (e.g., optical path length, volume, area) were performed.
    • Machine learning classifiers (Random Forest, SVM, KNN) were used for cell classification.

    Main Results:

    • The system successfully generated 3D pseudocolor optical path length profiles of cells.
    • Extracted morphological features were used for automated cell classification.
    • AR device displayed 3D renderings, features, and cell types in real-time.

    Conclusions:

    • The integrated DHM-AR system provides automated cell identification and visualization.
    • This technology facilitates rapid cell analysis and diagnosis for healthcare workers.
    • This represents the first integration of DHM with AR for automated cell analysis.