Proceedings Article10.1109/WIO.2018.8643459
Automatic cell identification and visualization using digital holographic microscopy with head mounted augmented reality devices: An Overview
Timothy OrConnor,Siddharth Rawat,Adam Markman,Bahram Javidi +3 more
- 26 Feb 2018
- pp 1-3
15
TL;DR: A compact and field-portable digital holographic microscopy system based on shearing geometry and integrated with a head mounted augmented reality device for cell identification and visualization that may allow for quickly and conveniently visualizing cells through an augmentedreality device and extracting relevant information with potential applications of rapid diagnosis by healthcare professionals working in remote areas.
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Abstract: We overview a compact and field-portable digital holographic microscopy system based on shearing geometry and integrated with a head mounted augmented reality device for cell identification and visualization. Customized smart glasses containing an external camera connect directly to the 3D-printed system to record holograms of biological specimens. Following hologram acquisition, regions of interest containing biological cells are segmented and digitally reconstructed to generate a three-dimensional (3D) pseudocolor optical path length profile. From the optical path length profiles, morphological features are extracted and inputted into several classification models for comparison including random forest classifier, support vector machines, and k-nearest neighbor, each yielding a high classification accuracy. After successful classification of the target cell, the classification result along with a pseudocolor 3D rendering of the cell’s optical path length profile, and its extracted feature values are displayed to the augmented reality device for the user. The system was tested on both living and non-living samples, including feature extraction from video data of live paramecium. The overviewed system may allow for quickly and conveniently visualizing cells through an augmented reality device and extracting relevant information with potential applications of rapid diagnosis by healthcare professionals working in remote areas. We acknowledge support from the National Science Foundation (Directorate for Engineering (ENG) (NSF ECCS 1545687, NSF/IIS-1422179)).
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Citations
Roadmap on digital holography [Invited].
Bahram Javidi,Artur Carnicer,Arun Anand,George Barbastathis,Wen Chen,Pietro Ferraro,Joseph W. Goodman,Ryoichi Horisaki,Kedar Khare,Malgorzata Kujawinska,Rainer A. Leitgeb,Pierre Marquet,Takanori Nomura,Aydogan Ozcan,YongKeun Park,Giancarlo Pedrini,Pascal Picart,Joseph Rosen,Genaro Saavedra,Natan T. Shaked,Adrian Stern,Enrique Tajahuerce,Lei Tian,Gordon Wetzstein,Masahiro Yamaguchi +24 more
TL;DR: This Roadmap article on digital holography provides an overview of a vast array of research activities in the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications.
83
Machine learning-based in-line holographic sensing of unstained malaria-infected red blood cells.
TL;DR: A new automatic sensing method using digital in‐line holographic microscopy combined with machine learning algorithms was proposed to sensitively detect unstained malaria‐infected red blood cells (iRBCs) and will be beneficial and valuable in the diagnosis of malaria.
75
An optical study of drug resistance detection in endometrial cancer cells by dynamic and quantitative phase imaging.
TL;DR: The results demonstrate that morphological characteristics have the potential to be utilized to distinguish the drug sensitivity of endometrial cancer cells, and it may provide new perspectives to establish optical methods to detect drug sensitivity and guide chemotherapy in endometricrial cancer.
20
Learning-based automatic sensing and size classification of microparticles using smartphone holographic microscopy
TL;DR: A new sensing platform for automatic identification of microparticle types through the synergistic integration of smartphone-based digital in-line holographic microscopy (DIHM) and machine-learning algorithms is proposed.
9
Digital in-line holographic microscopy for label-free identification and tracking of biological cells
Ji‐Hwan Kim,Sang Joon Lee +1 more
TL;DR: DIHM is a label-free technique for identifying and tracking biological cells in 3D space, providing precise measurements of cell behaviors and morphology.
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