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Huang B, Kang L, Tsang VTC, Lo CTK, Wong TTW. Deep learning-assisted smartphone-based quantitative microscopy for label-free peripheral blood smear analysis. BIOMEDICAL OPTICS EXPRESS 2024; 15:2636-2651. [PMID: 38633093 PMCID: PMC11019683 DOI: 10.1364/boe.511384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/24/2024] [Accepted: 01/30/2024] [Indexed: 04/19/2024]
Abstract
Hematologists evaluate alterations in blood cell enumeration and morphology to confirm peripheral blood smear findings through manual microscopic examination. However, routine peripheral blood smear analysis is both time-consuming and labor-intensive. Here, we propose using smartphone-based autofluorescence microscopy (Smart-AM) for imaging label-free blood smears at subcellular resolution with automatic hematological analysis. Smart-AM enables rapid and label-free visualization of morphological features of normal and abnormal blood cells (including leukocytes, erythrocytes, and thrombocytes). Moreover, assisted with deep-learning algorithms, this technique can automatically detect and classify different leukocytes with high accuracy, and transform the autofluorescence images into virtual Giemsa-stained images which show clear cellular features. The proposed technique is portable, cost-effective, and user-friendly, making it significant for broad point-of-care applications.
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Affiliation(s)
- Bingxin Huang
- Translational and Advanced Bioimaging Laboratory, Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China
| | - Lei Kang
- Translational and Advanced Bioimaging Laboratory, Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China
| | - Victor T. C. Tsang
- Translational and Advanced Bioimaging Laboratory, Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China
| | - Claudia T. K. Lo
- Translational and Advanced Bioimaging Laboratory, Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China
| | - Terence T. W. Wong
- Translational and Advanced Bioimaging Laboratory, Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China
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2
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Li Y, Pillar N, Li J, Liu T, Wu D, Sun S, Ma G, de Haan K, Huang L, Zhang Y, Hamidi S, Urisman A, Keidar Haran T, Wallace WD, Zuckerman JE, Ozcan A. Virtual histological staining of unlabeled autopsy tissue. Nat Commun 2024; 15:1684. [PMID: 38396004 PMCID: PMC10891155 DOI: 10.1038/s41467-024-46077-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.
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Affiliation(s)
- Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Nir Pillar
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Jingxi Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Tairan Liu
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Di Wu
- Computer Science Department, University of California, Los Angeles, CA, 90095, USA
| | - Songyu Sun
- Computer Science Department, University of California, Los Angeles, CA, 90095, USA
| | - Guangdong Ma
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- School of Physics, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Kevin de Haan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Luzhe Huang
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Sepehr Hamidi
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Anatoly Urisman
- Department of Pathology, University of California, San Francisco, CA, 94143, USA
| | - Tal Keidar Haran
- Department of Pathology, Hadassah Hebrew University Medical Center, Jerusalem, 91120, Israel
| | - William Dean Wallace
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Jonathan E Zuckerman
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA.
- Department of Surgery, University of California, Los Angeles, CA, 90095, USA.
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3
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Tarimo SA, Jang MA, Ngasa EE, Shin HB, Shin H, Woo J. WBC YOLO-ViT: 2 Way - 2 stage white blood cell detection and classification with a combination of YOLOv5 and vision transformer. Comput Biol Med 2024; 169:107875. [PMID: 38154163 DOI: 10.1016/j.compbiomed.2023.107875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/24/2023] [Accepted: 12/18/2023] [Indexed: 12/30/2023]
Abstract
Accurate detection and classification of white blood cells, otherwise known as leukocytes, play a critical role in diagnosing and monitoring various illnesses. However, conventional methods, such as manual classification by trained professionals, must be revised in terms of accuracy, efficiency, and potential bias. Moreover, applying deep learning techniques to detect and classify white blood cells using microscopic images is challenging owing to limited data, resolution noise, irregular shapes, and varying colors from different sources. This study presents a novel approach integrating object detection and classification for numerous type-white blood cell. We designed a 2-way approach to use two types of images: WBC and nucleus. YOLO (fast object detection) and ViT (powerful image representation capabilities) are effectively integrated into 16 classes. The proposed model demonstrates an exceptional 96.449% accuracy rate in classification.
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Affiliation(s)
- Servas Adolph Tarimo
- Department of Future Convergence Technology, Soonchunhyang University, Asan, South Korea
| | - Mi-Ae Jang
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Emmanuel Edward Ngasa
- Department of Future Convergence Technology, Soonchunhyang University, Asan, South Korea
| | - Hee Bong Shin
- Department of Laboratory Medicine, Soonchunhyang University Bucheon Hospital, Bucheon, South Korea.
| | - HyoJin Shin
- Department of ICT Convergence, Soonchunhyang University, Asan, South Korea
| | - Jiyoung Woo
- Department of ICT Convergence, Soonchunhyang University, Asan, South Korea.
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Fanous MJ, Pillar N, Ozcan A. Digital staining facilitates biomedical microscopy. FRONTIERS IN BIOINFORMATICS 2023; 3:1243663. [PMID: 37564725 PMCID: PMC10411189 DOI: 10.3389/fbinf.2023.1243663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023] Open
Abstract
Traditional staining of biological specimens for microscopic imaging entails time-consuming, laborious, and costly procedures, in addition to producing inconsistent labeling and causing irreversible sample damage. In recent years, computational "virtual" staining using deep learning techniques has evolved into a robust and comprehensive application for streamlining the staining process without typical histochemical staining-related drawbacks. Such virtual staining techniques can also be combined with neural networks designed to correct various microscopy aberrations, such as out-of-focus or motion blur artifacts, and improve upon diffracted-limited resolution. Here, we highlight how such methods lead to a host of new opportunities that can significantly improve both sample preparation and imaging in biomedical microscopy.
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Affiliation(s)
- Michael John Fanous
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, United States
| | - Nir Pillar
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, United States
- Bioengineering Department, University of California, Los Angeles, CA, United States
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, United States
- Bioengineering Department, University of California, Los Angeles, CA, United States
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, United States
- Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States
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Bai B, Yang X, Li Y, Zhang Y, Pillar N, Ozcan A. Deep learning-enabled virtual histological staining of biological samples. LIGHT, SCIENCE & APPLICATIONS 2023; 12:57. [PMID: 36864032 PMCID: PMC9981740 DOI: 10.1038/s41377-023-01104-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue. However, the current histological staining workflow requires tedious sample preparation steps, specialized laboratory infrastructure, and trained histotechnologists, making it expensive, time-consuming, and not accessible in resource-limited settings. Deep learning techniques created new opportunities to revolutionize staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, and accurate alternatives to standard chemical staining methods. These techniques, broadly referred to as virtual staining, were extensively explored by multiple research groups and demonstrated to be successful in generating various types of histological stains from label-free microscopic images of unstained samples; similar approaches were also used for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this Review, we provide a comprehensive overview of the recent research advances in deep learning-enabled virtual histological staining techniques. The basic concepts and the typical workflow of virtual staining are introduced, followed by a discussion of representative works and their technical innovations. We also share our perspectives on the future of this emerging field, aiming to inspire readers from diverse scientific fields to further expand the scope of deep learning-enabled virtual histological staining techniques and their applications.
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Affiliation(s)
- Bijie Bai
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
| | - Xilin Yang
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
| | - Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
| | - Nir Pillar
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
- Bioengineering Department, University of California, Los Angeles, 90095, USA.
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA.
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Gorti V, Kaza N, Williams EK, Lam WA, Robles FE. Compact and low-cost deep-ultraviolet microscope system for label-free molecular imaging and point-of-care hematological analysis. BIOMEDICAL OPTICS EXPRESS 2023; 14:1245-1255. [PMID: 36950241 PMCID: PMC10026585 DOI: 10.1364/boe.482294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/10/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Deep-ultraviolet (UV) microscopy enables label-free, high-resolution, quantitative molecular imaging and enables unique applications in biomedicine, including the potential for fast hematological analysis at the point-of-care. UV microscopy has been shown to quantify hemoglobin content and white blood cells (five-part differential), providing a simple alternative to the current gold standard, the hematological analyzer. Previously, however, the UV system comprised a bulky broadband laser-driven plasma light source along with a large and expensive camera and 3D translation stage. Here, we present a modified deep-UV microscope system with a compact footprint and low-cost components. We detail the novel design with simple, inexpensive optics and hardware to enable fast and accurate automated imaging. We characterize the system, including a modified low-cost web-camera and custom automated 3D translation stage, and demonstrate its ability to scan and capture large area images. We further demonstrate the capability of the system by imaging and analyzing blood smears, using previously trained networks for automatic segmentation, classification (including 5-part white blood cell differential), and colorization. The developed system is approximately 10 times less expensive than previous configurations and can serve as a point-of-care hematology analyzer, as well as be applied broadly in biomedicine as a simple compact, low-cost, quantitative molecular imaging system.
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Affiliation(s)
- Viswanath Gorti
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
| | - Nischita Kaza
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Evelyn Kendall Williams
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
| | - Wilbur A. Lam
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
- Aflac Cancer and Blood Disorders Center of Children’s Healthcare of Atlanta and Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Francisco E. Robles
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
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Ojaghi A, Kendall Williams E, Kaza N, Gorti V, Choi H, Torey J, Wiley T, Turner B, Jackson S, Park S, Lam WA, Robles FE. Label-free deep-UV microscopy detection and grading of neutropenia using a passive microfluidic device. OPTICS LETTERS 2022; 47:6005-6008. [PMID: 37219158 DOI: 10.1364/ol.472691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 10/26/2022] [Indexed: 05/24/2023]
Abstract
Neutropenia is a condition comprising an abnormally low number of neutrophils, a type of white blood cell, which puts patients at an increased risk of severe infections. Neutropenia is especially common among cancer patients and can disrupt their treatment or even be life-threatening in severe cases. Therefore, routine monitoring of neutrophil counts is crucial. However, the current standard of care to assess neutropenia, the complete blood count (CBC), is resource-intensive, time-consuming, and expensive, thereby limiting easy or timely access to critical hematological information such as neutrophil counts. Here, we present a simple technique for fast, label-free neutropenia detection and grading via deep-ultraviolet (deep-UV) microscopy of blood cells in polydimethylsiloxane (PDMS)-based passive microfluidic devices. The devices can potentially be manufactured in large quantities at a low cost, requiring only 1 μL of whole blood for operation. We show that the absolute neutrophil counts (ANC) obtained from our proposed microfluidic device-enabled deep-UV microscopy system are highly correlated with those from CBCs using commercial hematology analyzers in patients with moderate and severe neutropenia, as well as healthy donors. This work lays the foundation for the development of a compact, easy-to-use UV microscope system to track neutrophil counts that is suitable for low-resource, at-home, or point-of-care settings.
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