University of Connecticut

Siddharth Rawat

Bahram Javidi

Adam Markman

Arun Anand

Satoru Komatsu

The present disclosure provides improved systems and methods for automated cell identification/classification. More particularly, the present disclosure provides advantageous systems and methods for automated cell identification/classification using shearing interferometry with a digital holographic microscope. The present disclosure provides for a compact, low-cost, and field-portable 3D printed system for automatic cell identification/classification using a common path shearing interferometry with digital holographic microscopy. This system has demonstrated good results for sickle cell disease identification with human blood cells. The present disclosure provides that a robust, low cost cell identification/classification system based on shearing interferometry can be used for accurate cell identification. For example, by combining both the static features of the cell along with information on the cell motility, classification can be performed to determine the type of cell present in addition to the state of the cell (e.g., diseased vs. healthy).

Automated cell identification using shearing interferometry

Portable common path shearing interferometry-based holographic microscopy systems. The system includes a light source, a sample holder, a microscope objective lens, a shear plate and an imaging device positioned in a common path shearing interferometry configuration. A housing is configured to receive and hold the shear plate and maintain a position of the shear plate relative to the microscope objective lens.

Portable common path shearing interferometry-based holographic microscopy system with augmented reality visualization

Embodiments of the present disclosure include systems and methods for cell identification using a lens-less cell identification sensor. Randomly distributed cells can be illuminated by a light source such as a laser. The object beam can be passed through one or more diffusers. Pattern recognition is applied on the captured optical signature to classify the cells. For example, features can be extracted and a trained classifier can be used to classify the cells. The cell classes can be accurately identified even when multiple cells of the same class are inspected.

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Storrs, CT, United States of America

Siddharth Rawat