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Nguyen TL, Pradeep S, Judson-Torres RL, Reed J, Teitell MA, Zangle TA. Quantitative Phase Imaging: Recent Advances and Expanding Potential in Biomedicine. ACS NANO 2022; 16:11516-11544. [PMID: 35916417 PMCID: PMC10112851 DOI: 10.1021/acsnano.1c11507] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Quantitative phase imaging (QPI) is a label-free, wide-field microscopy approach with significant opportunities for biomedical applications. QPI uses the natural phase shift of light as it passes through a transparent object, such as a mammalian cell, to quantify biomass distribution and spatial and temporal changes in biomass. Reported in cell studies more than 60 years ago, ongoing advances in QPI hardware and software are leading to numerous applications in biology, with a dramatic expansion in utility over the past two decades. Today, investigations of cell size, morphology, behavior, cellular viscoelasticity, drug efficacy, biomass accumulation and turnover, and transport mechanics are supporting studies of development, physiology, neural activity, cancer, and additional physiological processes and diseases. Here, we review the field of QPI in biology starting with underlying principles, followed by a discussion of technical approaches currently available or being developed, and end with an examination of the breadth of applications in use or under development. We comment on strengths and shortcomings for the deployment of QPI in key biomedical contexts and conclude with emerging challenges and opportunities based on combining QPI with other methodologies that expand the scope and utility of QPI even further.
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Javidi B, Carnicer A, Anand A, Barbastathis G, Chen W, Ferraro P, Goodman JW, Horisaki R, Khare K, Kujawinska M, Leitgeb RA, Marquet P, Nomura T, Ozcan A, Park Y, Pedrini G, Picart P, Rosen J, Saavedra G, Shaked NT, Stern A, Tajahuerce E, Tian L, Wetzstein G, Yamaguchi M. Roadmap on digital holography [Invited]. OPTICS EXPRESS 2021; 29:35078-35118. [PMID: 34808951 DOI: 10.1364/oe.435915] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 09/04/2021] [Indexed: 05/22/2023]
Abstract
This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography.
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Miccio L, Cimmino F, Kurelac I, Villone MM, Bianco V, Memmolo P, Merola F, Mugnano M, Capasso M, Iolascon A, Maffettone PL, Ferraro P. Perspectives on liquid biopsy for label‐free detection of “circulating tumor cells” through intelligent lab‐on‐chips. VIEW 2020. [DOI: 10.1002/viw.20200034] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Lisa Miccio
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | | | - Ivana Kurelac
- Dipartimento di Scienze Mediche e Chirurgiche Università di Bologna Bologna Italy
- Centro di Ricerca Biomedica Applicata (CRBA) Università di Bologna Bologna Italy
| | - Massimiliano M. Villone
- Dipartimento di Ingegneria Chimica dei Materiali e della Produzione Industriale Università degli Studi di Napoli “Federico II” Napoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Vittorio Bianco
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Pasquale Memmolo
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Francesco Merola
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Martina Mugnano
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Mario Capasso
- CEINGE Biotecnologie Avanzate Naples Italy
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche Università degli Studi di Napoli Federico II Naples Italy
| | - Achille Iolascon
- CEINGE Biotecnologie Avanzate Naples Italy
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche Università degli Studi di Napoli Federico II Naples Italy
| | - Pier Luca Maffettone
- Dipartimento di Ingegneria Chimica dei Materiali e della Produzione Industriale Università degli Studi di Napoli “Federico II” Napoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Pietro Ferraro
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
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Yi F, Moon I, Javidi B. Automated red blood cells extraction from holographic images using fully convolutional neural networks. BIOMEDICAL OPTICS EXPRESS 2017; 8:4466-4479. [PMID: 29082078 PMCID: PMC5654793 DOI: 10.1364/boe.8.004466] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 08/07/2017] [Accepted: 08/23/2017] [Indexed: 05/22/2023]
Abstract
In this paper, we present two models for automatically extracting red blood cells (RBCs) from RBCs holographic images based on a deep learning fully convolutional neural network (FCN) algorithm. The first model, called FCN-1, only uses the FCN algorithm to carry out RBCs prediction, whereas the second model, called FCN-2, combines the FCN approach with the marker-controlled watershed transform segmentation scheme to achieve RBCs extraction. Both models achieve good segmentation accuracy. In addition, the second model has much better performance in terms of cell separation than traditional segmentation methods. In the proposed methods, the RBCs phase images are first numerically reconstructed from RBCs holograms recorded with off-axis digital holographic microscopy. Then, some RBCs phase images are manually segmented and used as training data to fine-tune the FCN. Finally, each pixel in new input RBCs phase images is predicted into either foreground or background using the trained FCN models. The RBCs prediction result from the first model is the final segmentation result, whereas the result from the second model is used as the internal markers of the marker-controlled transform algorithm for further segmentation. Experimental results show that the given schemes can automatically extract RBCs from RBCs phase images and much better RBCs separation results are obtained when the FCN technique is combined with the marker-controlled watershed segmentation algorithm.
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Affiliation(s)
- Faliu Yi
- Department of Clinical Science, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Inkyu Moon
- Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science & Technology, Daegu 42988, South Korea
| | - Bahram Javidi
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT 06269, USA
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A survey for the applications of content-based microscopic image analysis in microorganism classification domains. Artif Intell Rev 2017. [DOI: 10.1007/s10462-017-9572-4] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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Jo Y, Jung J, Kim MH, Park H, Kang SJ, Park Y. Label-free identification of individual bacteria using Fourier transform light scattering. OPTICS EXPRESS 2015; 23:15792-805. [PMID: 26193558 DOI: 10.1364/oe.23.015792] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Rapid identification of bacterial species is crucial in medicine and food hygiene. In order to achieve rapid and label-free identification of bacterial species at the single bacterium level, we propose and experimentally demonstrate an optical method based on Fourier transform light scattering (FTLS) measurements and statistical classification. For individual rod-shaped bacteria belonging to four bacterial species (Listeria monocytogenes, Escherichia coli, Lactobacillus casei, and Bacillus subtilis), two-dimensional angle-resolved light scattering maps are precisely measured using FTLS technique. The scattering maps are then systematically analyzed, employing statistical classification in order to extract the unique fingerprint patterns for each species, so that a new unidentified bacterium can be identified by a single light scattering measurement. The single-bacterial and label-free nature of our method suggests wide applicability for rapid point-of-care bacterial diagnosis.
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Seo S, Isikman SO, Sencan I, Mudanyali O, Su TW, Bishara W, Erlinger A, Ozcan A. High-throughput lens-free blood analysis on a chip. Anal Chem 2010; 82:4621-7. [PMID: 20450181 PMCID: PMC2892055 DOI: 10.1021/ac1007915] [Citation(s) in RCA: 82] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We present a detailed investigation of the performance of lens-free holographic microscopy toward high-throughput on-chip blood analysis. Using a spatially incoherent source that is emanating from a large aperture, automated counting of red blood cells with minimal sample preparation steps at densities reaching up to approximately 0.4 x 10(6) cells/muL is presented. Using the same lens-free holographic microscopy platform, we also characterize the volume of the red blood cells at the single-cell level through recovery of the optical phase information of each cell. We further demonstrate the measurement of the hemoglobin concentration of whole blood samples as well as automated counting of white blood cells, also yielding spatial resolution at the subcellular level sufficient to differentiate granulocytes, monocytes, and lymphocytes from each other. These results uncover the prospects of lens-free holographic on-chip imaging to provide a useful tool for global health problems, especially by facilitating whole blood analysis in resource-poor environments.
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Affiliation(s)
- Sungkyu Seo
- Electrical Engineering Department, University of California, Los Angeles, California 90095
- Department of Electronics and Information Engineering, Korea University, Jochiwon, Korea
| | - Serhan O. Isikman
- Electrical Engineering Department, University of California, Los Angeles, California 90095
| | - Ikbal Sencan
- Electrical Engineering Department, University of California, Los Angeles, California 90095
| | - Onur Mudanyali
- Electrical Engineering Department, University of California, Los Angeles, California 90095
| | - Ting-Wei Su
- Electrical Engineering Department, University of California, Los Angeles, California 90095
| | - Waheb Bishara
- Electrical Engineering Department, University of California, Los Angeles, California 90095
| | - Anthony Erlinger
- Electrical Engineering Department, University of California, Los Angeles, California 90095
| | - Aydogan Ozcan
- Electrical Engineering Department, University of California, Los Angeles, California 90095
- California NanoSystems Institute (CNSI), University of California, Los Angeles, California 90095
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Moon I, Javidi B. 3-D visualization and identification of biological microorganisms using partially temporal incoherent light in-line computational holographic imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2008; 27:1782-90. [PMID: 19033094 DOI: 10.1109/tmi.2008.927339] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
We present a new method for three-dimensional (3-D) visualization and identification of biological microorganisms using partially temporal incoherent light in-line (PTILI) computational holographic imaging and multivariate statistical methods. For 3-D data acquisition of biological microorganisms, the band-pass filtered white light is used to illuminate a biological sample. The transversely and longitudinally diffracted pattern of the biological sample is magnified by microscope objective (MO) and is optically recorded with an image sensor array interfaced with a computer. Three-dimensional reconstruction of the biological sample from the diffraction pattern is accomplished by using computational Fresnel propagation method. Principal components analysis and nonparametric inference algorithms are applied to the 3-D complex amplitude biological sample for identification purposes. Experiments indicate that the proposed system can be useful for identifying biological microorganisms. To the best of our knowledge, this is the first report on using PTILI computational holographic microscopy for identification of biological microorganisms.
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Affiliation(s)
- Inkyu Moon
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT 06269 USA.
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Moon I, Javidi B. Three-dimensional visualization of objects in scattering medium by use of computational integral imaging. OPTICS EXPRESS 2008; 16:13080-13089. [PMID: 18711547 DOI: 10.1364/oe.16.013080] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
In this paper, we propose a method to three-dimensionally visualize objects in a scattering medium using integral imaging. Our approach is based on a particular use of the interference phenomenon between the ballistic photons getting through the scattering medium and the scattered photons being scattered by the medium. For three-dimensional (3D) sensing of the scattered objects, the synthetic aperture integral imaging system under coherent illumination records the scattered elemental images of the objects. Then, the computational geometrical ray propagation algorithm is applied to the scattered elemental images in order to eliminate the interference patterns between scattered and object beams. The original 3D information of the scattered objects is recovered by multiple imaging channels, each with a unique perspective of the object. We present both simulation and experimental results with virtual and real objects to demonstrate the proposed concepts.
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Affiliation(s)
- Inkyu Moon
- Dept of Electrical and Computer Engineering, U-2157, University of Connecticut, Storrs, CT 06269-2157, USA.
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Moon I, Javidi B. Three-dimensional identification of stem cells by computational holographic imaging. J R Soc Interface 2007; 4:305-13. [PMID: 17251147 PMCID: PMC2359842 DOI: 10.1098/rsif.2006.0175] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
We present an optical imaging system and mathematical algorithms for three-dimensional sensing and identification of stem cells. Data acquisition of stem cells is based on holographic microscopy in the Fresnel domain by illuminating the cells with a laser. In this technique, the holograms of stem cells are optically recorded with an image sensor array interfaced with a computer and three-dimensional images of the stem cells are reconstructed from the Gabor-filtered digital holograms. The Gabor wavelet transformation for feature extraction of the digital hologram is performed to improve the process of identification. The inverse Fresnel transformation of the Gabor-filtered digital hologram is performed to reconstruct the multi-scale three-dimensional images of the stem cells at different depths along the longitudinal direction. For recognition and classification of stem cells, a statistical approach using an empirical cumulative density function is introduced. The experiments indicate that the proposed system can be potentially useful for recognizing and classifying stem cells. To the best of our knowledge, this is the first report on using three-dimensional holographic microscopy for automated identification of stem cells.
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