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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] [What about the content of this article? (0)] [Affiliation(s)] [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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2
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Volpe G, Wählby C, Tian L, Hecht M, Yakimovich A, Monakhova K, Waller L, Sbalzarini IF, Metzler CA, Xie M, Zhang K, Lenton ICD, Rubinsztein-Dunlop H, Brunner D, Bai B, Ozcan A, Midtvedt D, Wang H, Sladoje N, Lindblad J, Smith JT, Ochoa M, Barroso M, Intes X, Qiu T, Yu LY, You S, Liu Y, Ziatdinov MA, Kalinin SV, Sheridan A, Manor U, Nehme E, Goldenberg O, Shechtman Y, Moberg HK, Langhammer C, Špačková B, Helgadottir S, Midtvedt B, Argun A, Thalheim T, Cichos F, Bo S, Hubatsch L, Pineda J, Manzo C, Bachimanchi H, Selander E, Homs-Corbera A, Fränzl M, de Haan K, Rivenson Y, Korczak Z, Adiels CB, Mijalkov M, Veréb D, Chang YW, Pereira JB, Matuszewski D, Kylberg G, Sintorn IM, Caicedo JC, Cimini BA, Bell MAL, Saraiva BM, Jacquemet G, Henriques R, Ouyang W, Le T, Gómez-de-Mariscal E, Sage D, Muñoz-Barrutia A, Lindqvist EJ, Bergman J. Roadmap on Deep Learning for Microscopy. ArXiv 2023:2303.03793. [PMID: 36945686 PMCID: PMC10029045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.
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Bai B, Wang H, Li Y, de Haan K, Colonnese F, Wan Y, Zuo J, Doan NB, Zhang X, Zhang Y, Li J, Yang X, Dong W, Darrow MA, Kamangar E, Lee HS, Rivenson Y, Ozcan A. Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning. BME Front 2022; 2022:9786242. [PMID: 37850170 PMCID: PMC10521710 DOI: 10.34133/2022/9786242] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 08/25/2022] [Indexed: 10/19/2023] Open
Abstract
The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies, and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis. HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist, which typically takes one day to prepare in a laboratory, increasing analysis time and associated costs. Here, we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis, in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs) to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts. A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual HER2 staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.
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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
| | - Hongda Wang
- 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
| | - Kevin de Haan
- 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
| | | | - Yujie Wan
- Physics and Astronomy Department, University of California, Los Angeles, CA 90095, USA
| | - Jingyi Zuo
- Computer Science Department, University of California, Los Angeles, CA, USA
| | - Ngan B. Doan
- Translational Pathology Core Laboratory, University of California, Los Angeles, CA 90095, USA
| | - Xiaoran Zhang
- Electrical and Computer Engineering Department, 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 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
| | - Jingxi 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
| | - 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
| | - Wenjie Dong
- Statistics Department, University of California, Los Angeles, CA 90095, USA
| | - Morgan Angus Darrow
- Department of Pathology and Laboratory Medicine, University of California at Davis, Sacramento, CA 95817, USA
| | - Elham Kamangar
- Department of Pathology and Laboratory Medicine, University of California at Davis, Sacramento, CA 95817, USA
| | - Han Sung Lee
- Department of Pathology and Laboratory Medicine, University of California at Davis, Sacramento, CA 95817, USA
| | - Yair Rivenson
- 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
- Department of Surgery, University of California, Los Angeles, CA 90095, USA
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Li J, Garfinkel J, Zhang X, Wu D, Zhang Y, de Haan K, Wang H, Liu T, Bai B, Rivenson Y, Rubinstein G, Scumpia PO, Ozcan A. Biopsy-free in vivo virtual histology of skin using deep learning. Light Sci Appl 2021; 10:233. [PMID: 34795202 PMCID: PMC8602311 DOI: 10.1038/s41377-021-00674-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/22/2021] [Accepted: 10/28/2021] [Indexed: 05/09/2023]
Abstract
An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors. The process is cumbersome and time-consuming, often leading to unnecessary biopsies and scars. Emerging noninvasive optical technologies such as reflectance confocal microscopy (RCM) can provide label-free, cellular-level resolution, in vivo images of skin without performing a biopsy. Although RCM is a useful diagnostic tool, it requires specialized training because the acquired images are grayscale, lack nuclear features, and are difficult to correlate with tissue pathology. Here, we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of the epidermis, dermal-epidermal junction, and superficial dermis layers. The network was trained under an adversarial learning scheme, which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth. We show that this trained neural network can be used to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma, and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features to traditional histology from the same excised tissue. This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies.
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Affiliation(s)
- 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
| | | | - Xiaoran Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
| | - Di Wu
- Computer Science Department, 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
| | - 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
| | - Hongda Wang
- 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
| | - Bijie Bai
- 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
| | - Yair Rivenson
- 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
| | | | - Philip O Scumpia
- Division of Dermatology, University of California, Los Angeles, CA, 90095, USA.
- Department of Dermatology, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, 90073, 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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5
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Ghodrati V, Rivenson Y, Prosper A, de Haan K, Ali F, Yoshida T, Bedayat A, Nguyen KL, Finn JP, Hu P. Automatic segmentation of peripheral arteries and veins in ferumoxytol-enhanced MR angiography. Magn Reson Med 2021; 87:984-998. [PMID: 34611937 DOI: 10.1002/mrm.29026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 09/03/2021] [Accepted: 09/09/2021] [Indexed: 11/10/2022]
Abstract
PURPOSE To automate the segmentation of the peripheral arteries and veins in the lower extremities based on ferumoxytol-enhanced MR angiography (FE-MRA). METHODS Our automated pipeline has 2 sequential stages. In the first stage, we used a 3D U-Net with local attention gates, which was trained based on a combination of the Focal Tversky loss with region mutual loss under a deep supervision mechanism to segment the vasculature from the high-resolution FE-MRA datasets. In the second stage, we used time-resolved images to separate the arteries from the veins. Because the ultimate segmentation quality of the arteries and veins relies on the performance of the first stage, we thoroughly evaluated the different aspects of the segmentation network and compared its performance in blood vessel segmentation with currently accepted state-of-the-art networks, including Volumetric-Net, DeepVesselNet-FCN, and Uception. RESULTS We achieved a competitive F1 = 0.8087 and recall = 0.8410 for blood vessel segmentation compared with F1 = (0.7604, 0.7573, 0.7651) and recall = (0.7791, 0.7570, 0.7774) obtained with Volumetric-Net, DeepVesselNet-FCN, and Uception. For the artery and vein separation stage, we achieved F1 = (0.8274/0.7863) in the calf region, which is the most challenging region in peripheral arteries and veins segmentation. CONCLUSION Our pipeline is capable of fully automatic vessel segmentation based on FE-MRA without need for human interaction in <4 min. This method improves upon manual segmentation by radiologists, which routinely takes several hours.
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Affiliation(s)
- Vahid Ghodrati
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, California, USA
| | - Yair Rivenson
- Electrical and Computer Engineering Department, University of California, Los Angeles, California, USA
| | - Ashley Prosper
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Kevin de Haan
- Electrical and Computer Engineering Department, University of California, Los Angeles, California, USA
| | - Fadil Ali
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, California, USA
| | - Takegawa Yoshida
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Arash Bedayat
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Kim-Lien Nguyen
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Department of Medicine (Cardiology), David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - J Paul Finn
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Peng Hu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, California, USA
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Göröcs Z, Baum D, Song F, de Haan K, Ceylan Koydemir H, Qiu Y, Cai Z, Skandakumar T, Peterman S, Tamamitsu M, Ozcan A. Label-free detection of Giardia lamblia cysts using a deep learning-enabled portable imaging flow cytometer. Lab Chip 2020; 20:4404-4412. [PMID: 32808619 DOI: 10.1039/d0lc00708k] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We report a field-portable and cost-effective imaging flow cytometer that uses deep learning and holography to accurately detect Giardia lamblia cysts in water samples at a volumetric throughput of 100 mL h-1. This flow cytometer uses lens free color holographic imaging to capture and reconstruct phase and intensity images of microscopic objects in a continuously flowing sample, and automatically identifies Giardia lamblia cysts in real-time without the use of any labels or fluorophores. The imaging flow cytometer is housed in an environmentally-sealed enclosure with dimensions of 19 cm × 19 cm × 16 cm and weighs 1.6 kg. We demonstrate that this portable imaging flow cytometer coupled to a laptop computer can detect and quantify, in real-time, low levels of Giardia contamination (e.g., <10 cysts per 50 mL) in both freshwater and seawater samples. The field-portable and label-free nature of this method has the potential to allow rapid and automated screening of drinking water supplies in resource limited settings in order to detect waterborne parasites and monitor the integrity of the filters used for water treatment.
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Affiliation(s)
- Zoltán Göröcs
- Electrical and Computer Engineering Department, University of California, Los Angeles, 420 Westwood Plaza, Engineering IV. 68-119, Los Angeles, CA 90095, USA.
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Liu T, de Haan K, Bai B, Rivenson Y, Luo Y, Wang H, Karalli D, Fu H, Zhang Y, FitzGerald J, Ozcan A. Deep Learning-Based Holographic Polarization Microscopy. ACS Photonics 2020; 7:3023-3034. [PMID: 34368395 PMCID: PMC8345334 DOI: 10.1021/acsphotonics.0c01051] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Polarized light microscopy provides high contrast to birefringent specimen and is widely used as a diagnostic tool in pathology. However, polarization microscopy systems typically operate by analyzing images collected from two or more light paths in different states of polarization, which lead to relatively complex optical designs, high system costs, or experienced technicians being required. Here, we present a deep learning-based holographic polarization microscope that is capable of obtaining quantitative birefringence retardance and orientation information of specimen from a phase-recovered hologram, while only requiring the addition of one polarizer/analyzer pair to an inline lensfree holographic imaging system. Using a deep neural network, the reconstructed holographic images from a single state of polarization can be transformed into images equivalent to those captured using a single-shot computational polarized light microscope (SCPLM). Our analysis shows that a trained deep neural network can extract the birefringence information using both the sample specific morphological features as well as the holographic amplitude and phase distribution. To demonstrate the efficacy of this method, we tested it by imaging various birefringent samples including, for example, monosodium urate and triamcinolone acetonide crystals. Our method achieves similar results to SCPLM both qualitatively and quantitatively, and due to its simpler optical design and significantly larger field-of-view this method has the potential to expand the access to polarization microscopy and its use for medical diagnosis in resource limited settings.
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Affiliation(s)
- Tairan Liu
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Kevin de Haan
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Bijie Bai
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Yair Rivenson
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Yi Luo
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Hongda Wang
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - David Karalli
- Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States
| | - Hongxiang Fu
- Computational and Systems Biology Department, University of California, Los Angeles, California 90095, United States
| | - Yibo Zhang
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - John FitzGerald
- Division of Rheumatology, Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, California 90095, United States
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, Department of Bioengineering, California NanoSystems Institute, and Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, California 90095, United States
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8
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Rivenson Y, de Haan K, Wallace WD, Ozcan A. Emerging Advances to Transform Histopathology Using Virtual Staining. BME Front 2020; 2020:9647163. [PMID: 37849966 PMCID: PMC10521663 DOI: 10.34133/2020/9647163] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 07/28/2020] [Indexed: 10/19/2023] Open
Abstract
In an age where digitization is widespread in clinical and preclinical workflows, pathology is still predominantly practiced by microscopic evaluation of stained tissue specimens affixed on glass slides. Over the last decade, new high throughput digital scanning microscopes have ushered in the era of digital pathology that, along with recent advances in machine vision, have opened up new possibilities for Computer-Aided-Diagnoses. Despite these advances, the high infrastructural costs related to digital pathology and the perception that the digitization process is an additional and nondirectly reimbursable step have challenged its widespread adoption. Here, we discuss how emerging virtual staining technologies and machine learning can help to disrupt the standard histopathology workflow and create new avenues for the diagnostic paradigm that will benefit patients and healthcare systems alike via digital pathology.
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Affiliation(s)
- Yair Rivenson
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA
- Bioengineering Department, University of California, Los Angeles, CA, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
| | - Kevin de Haan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA
- Bioengineering Department, University of California, Los Angeles, CA, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
| | - W. Dean Wallace
- Department of Pathology and Laboratory Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA
- Bioengineering Department, University of California, Los Angeles, CA, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
- Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
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9
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de Haan K, Ceylan Koydemir H, Rivenson Y, Tseng D, Van Dyne E, Bakic L, Karinca D, Liang K, Ilango M, Gumustekin E, Ozcan A. Automated screening of sickle cells using a smartphone-based microscope and deep learning. NPJ Digit Med 2020; 3:76. [PMID: 32509973 PMCID: PMC7244537 DOI: 10.1038/s41746-020-0282-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 04/28/2020] [Indexed: 12/13/2022] Open
Abstract
Sickle cell disease (SCD) is a major public health priority throughout much of the world, affecting millions of people. In many regions, particularly those in resource-limited settings, SCD is not consistently diagnosed. In Africa, where the majority of SCD patients reside, more than 50% of the 0.2-0.3 million children born with SCD each year will die from it; many of these deaths are in fact preventable with correct diagnosis and treatment. Here, we present a deep learning framework which can perform automatic screening of sickle cells in blood smears using a smartphone microscope. This framework uses two distinct, complementary deep neural networks. The first neural network enhances and standardizes the blood smear images captured by the smartphone microscope, spatially and spectrally matching the image quality of a laboratory-grade benchtop microscope. The second network acts on the output of the first image enhancement neural network and is used to perform the semantic segmentation between healthy and sickle cells within a blood smear. These segmented images are then used to rapidly determine the SCD diagnosis per patient. We blindly tested this mobile sickle cell detection method using blood smears from 96 unique patients (including 32 SCD patients) that were imaged by our smartphone microscope, and achieved ~98% accuracy, with an area-under-the-curve of 0.998. With its high accuracy, this mobile and cost-effective method has the potential to be used as a screening tool for SCD and other blood cell disorders in resource-limited settings.
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Affiliation(s)
- 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
| | - Hatice Ceylan Koydemir
- 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
| | - Yair Rivenson
- 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
| | - Derek Tseng
- 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
| | - Elizabeth Van Dyne
- Department of Pediatrics, Division of Hematology-Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
| | - Lissette Bakic
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
| | - Doruk Karinca
- Department of Computer Science, University of California, Los Angeles, CA 90095 USA
| | - Kyle Liang
- Department of Computer Science, University of California, Los Angeles, CA 90095 USA
| | - Megha Ilango
- Department of Computer Science, University of California, Los Angeles, CA 90095 USA
| | - Esin Gumustekin
- Department of Neuroscience, 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, David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
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10
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Zhang Y, de Haan K, Rivenson Y, Li J, Delis A, Ozcan A. Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue. Light Sci Appl 2020; 9:78. [PMID: 32411363 PMCID: PMC7203145 DOI: 10.1038/s41377-020-0315-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 04/14/2020] [Accepted: 04/15/2020] [Indexed: 05/16/2023]
Abstract
Histological staining is a vital step in diagnosing various diseases and has been used for more than a century to provide contrast in tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However, this process is time consuming, labour intensive, expensive and destructive to the specimen. Recently, the ability to virtually stain unlabelled tissue sections, entirely avoiding the histochemical staining step, has been demonstrated using tissue-stain-specific deep neural networks. Here, we present a new deep-learning-based framework that generates virtually stained images using label-free tissue images, in which different stains are merged following a micro-structure map defined by the user. This approach uses a single deep neural network that receives two different sources of information as its input: (1) autofluorescence images of the label-free tissue sample and (2) a "digital staining matrix", which represents the desired microscopic map of the different stains to be virtually generated in the same tissue section. This digital staining matrix is also used to virtually blend existing stains, digitally synthesizing new histological stains. We trained and blindly tested this virtual-staining network using unlabelled kidney tissue sections to generate micro-structured combinations of haematoxylin and eosin (H&E), Jones' silver stain, and Masson's trichrome stain. Using a single network, this approach multiplexes the virtual staining of label-free tissue images with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created in the same tissue cross section, which is currently not feasible with standard histochemical staining methods.
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Affiliation(s)
- 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
| | - 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
| | - Yair Rivenson
- 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
| | - Apostolos Delis
- Department of Computer Science, 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, David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
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11
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Liu T, Wei Z, Rivenson Y, de Haan K, Zhang Y, Wu Y, Ozcan A. Deep learning-based color holographic microscopy. J Biophotonics 2019; 12:e201900107. [PMID: 31309728 DOI: 10.1002/jbio.201900107] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 07/13/2019] [Accepted: 07/14/2019] [Indexed: 06/10/2023]
Abstract
We report a framework based on a generative adversarial network that performs high-fidelity color image reconstruction using a single hologram of a sample that is illuminated simultaneously by light at three different wavelengths. The trained network learns to eliminate missing-phase-related artifacts, and generates an accurate color transformation for the reconstructed image. Our framework is experimentally demonstrated using lung and prostate tissue sections that are labeled with different histological stains. This framework is envisaged to be applicable to point-of-care histopathology and presents a significant improvement in the throughput of coherent microscopy systems given that only a single hologram of the specimen is required for accurate color imaging.
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Affiliation(s)
- Tairan Liu
- Electrical and Computer Engineering Department, University of California, Los Angeles, California
- Bioengineering Department, University of California, Los Angeles, California
- California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Zhensong Wei
- Electrical and Computer Engineering Department, University of California, Los Angeles, California
| | - Yair Rivenson
- Electrical and Computer Engineering Department, University of California, Los Angeles, California
- Bioengineering Department, University of California, Los Angeles, California
- California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Kevin de Haan
- Electrical and Computer Engineering Department, University of California, Los Angeles, California
- Bioengineering Department, University of California, Los Angeles, California
- California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Yibo Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, California
- Bioengineering Department, University of California, Los Angeles, California
- California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Yichen Wu
- Electrical and Computer Engineering Department, University of California, Los Angeles, California
- Bioengineering Department, University of California, Los Angeles, California
- California NanoSystems Institute (CNSI), University of California, Los Angeles, California
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, California
- Bioengineering Department, University of California, Los Angeles, California
- California NanoSystems Institute (CNSI), University of California, Los Angeles, California
- Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, California
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12
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Tok S, de Haan K, Tseng D, Usanmaz CF, Ceylan Koydemir H, Ozcan A. Early detection of E. coli and total coliform using an automated, colorimetric and fluorometric fiber optics-based device. Lab Chip 2019; 19:2925-2935. [PMID: 31372607 DOI: 10.1039/c9lc00652d] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Lack of access to clean water is a major global issue that affects millions of people worldwide. Drinking contaminated water can be extremely hazardous, so it is imperative that it is tested sufficiently. One method commonly used to determine the quality of water is testing for both E. coli and total coliform. Here, we present a cost-effective and automated device which can concurrently test drinking water samples for both E. coli and total coliform using an EPA-approved reagent. Equipped with a Raspberry Pi microcontroller and camera, we perform automated periodic measurements of both the absorption and fluorescence of the water under test over 24 hours. In each test, 100 mL of the water sample is split into a custom designed 40-well plate, where the transmitted blue light and the fluorescent light (under UV excitation) are collected by 520 individual optical fibers. Images of these fiber outputs are then acquired periodically, and digitally processed to determine the presence of the bacteria in each well of the 40-well plate. We demonstrate that this cost-effective device, weighing 1.66 kg, can automatically detect the presence of both E. coli and total coliform in drinking water within ∼16 hours, down to a level of one colony-forming unit (CFU) per 100 mL. Furthermore, due to its automated analysis, this approach is also more sensitive than a manual count performed by an expert, reducing the time needed to determine whether the water under test is safe to drink or not.
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Affiliation(s)
- Sabiha Tok
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA. and Bioengineering Department, University of California, Los Angeles, CA 90095, USA and California NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA and Department of Biophysics, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Kevin de Haan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA. and Bioengineering Department, University of California, Los Angeles, CA 90095, USA and California NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA
| | - Derek Tseng
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA. and Bioengineering Department, University of California, Los Angeles, CA 90095, USA and California NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA
| | - Can Firat Usanmaz
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey
| | - Hatice Ceylan Koydemir
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA. and Bioengineering Department, University of California, Los Angeles, CA 90095, USA and California NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA. and Bioengineering Department, University of California, Los Angeles, CA 90095, USA and California NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA and Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
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13
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de Haan K, Ballard ZS, Rivenson Y, Wu Y, Ozcan A. Resolution enhancement in scanning electron microscopy using deep learning. Sci Rep 2019; 9:12050. [PMID: 31427691 PMCID: PMC6700066 DOI: 10.1038/s41598-019-48444-2] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 08/06/2019] [Indexed: 11/17/2022] Open
Abstract
We report resolution enhancement in scanning electron microscopy (SEM) images using a generative adversarial network. We demonstrate the veracity of this deep learning-based super-resolution technique by inferring unresolved features in low-resolution SEM images and comparing them with the accurately co-registered high-resolution SEM images of the same samples. Through spatial frequency analysis, we also report that our method generates images with frequency spectra matching higher resolution SEM images of the same fields-of-view. By using this technique, higher resolution SEM images can be taken faster, while also reducing both electron charging and damage to the samples.
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Affiliation(s)
- 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
| | - Zachary S Ballard
- 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
| | - Yair Rivenson
- 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.
| | - Yichen Wu
- 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
| | - 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, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
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14
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Liu T, de Haan K, Rivenson Y, Wei Z, Zeng X, Zhang Y, Ozcan A. Deep learning-based super-resolution in coherent imaging systems. Sci Rep 2019; 9:3926. [PMID: 30850721 PMCID: PMC6408569 DOI: 10.1038/s41598-019-40554-1] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 02/19/2019] [Indexed: 11/28/2022] Open
Abstract
We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems. We demonstrate that this framework can enhance the resolution of both pixel size-limited and diffraction-limited coherent imaging systems. The capabilities of this approach are experimentally validated by super-resolving complex-valued images acquired using a lensfree on-chip holographic microscope, the resolution of which was pixel size-limited. Using the same GAN-based approach, we also improved the resolution of a lens-based holographic imaging system that was limited in resolution by the numerical aperture of its objective lens. This deep learning-based super-resolution framework can be broadly applied to enhance the space-bandwidth product of coherent imaging systems using image data and convolutional neural networks, and provides a rapid, non-iterative method for solving inverse image reconstruction or enhancement problems in optics.
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Affiliation(s)
- 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
| | - 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
| | - Yair Rivenson
- 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
| | - Zhensong Wei
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
| | - Xin Zeng
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
| | - Yibo 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
| | - 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, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
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15
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Rivenson Y, Liu T, Wei Z, Zhang Y, de Haan K, Ozcan A. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light Sci Appl 2019; 8:23. [PMID: 30728961 PMCID: PMC6363787 DOI: 10.1038/s41377-019-0129-y] [Citation(s) in RCA: 142] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 01/05/2019] [Accepted: 01/11/2019] [Indexed: 05/03/2023]
Abstract
Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections into images that are equivalent to the brightfield microscopy images of the same samples that are histologically stained. Through pairs of image data (QPI and the corresponding brightfield images, acquired after staining), we train a generative adversarial network and demonstrate the effectiveness of this virtual-staining approach using sections of human skin, kidney, and liver tissue, matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin, Jones' stain, and Masson's trichrome stain, respectively. This digital-staining framework may further strengthen various uses of label-free QPI techniques in pathology applications and biomedical research in general, by eliminating the need for histological staining, reducing sample preparation related costs and saving time. Our results provide a powerful example of some of the unique opportunities created by data-driven image transformations enabled by deep learning.
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Affiliation(s)
- Yair Rivenson
- 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
| | - Zhensong Wei
- 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
| | - Yibo 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
| | - 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
| | - 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, David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
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16
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Wu Y, Luo Y, Chaudhari G, Rivenson Y, Calis A, de Haan K, Ozcan A. Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram. Light Sci Appl 2019; 8:25. [PMID: 30854197 PMCID: PMC6401162 DOI: 10.1038/s41377-019-0139-9] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 02/15/2019] [Accepted: 02/15/2019] [Indexed: 05/05/2023]
Abstract
Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a single-snapshot hologram. However, unlike a conventional bright-field microscopy image, the quality of holographic reconstructions is compromised by interference fringes as a result of twin images and out-of-plane objects. Here, we demonstrate that cross-modality deep learning using a generative adversarial network (GAN) can endow holographic images of a sample volume with bright-field microscopy contrast, combining the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of incoherent bright-field microscopy. We illustrate the performance of this "bright-field holography" method through the snapshot imaging of bioaerosols distributed in 3D, matching the artifact-free image contrast and axial sectioning performance of a high-NA bright-field microscope. This data-driven deep-learning-based imaging method bridges the contrast gap between coherent and incoherent imaging, and enables the snapshot 3D imaging of objects with bright-field contrast from a single hologram, benefiting from the wave-propagation framework of holography.
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Affiliation(s)
- Yichen Wu
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
- Bioengineering Department, University of California, Los Angeles, CA 90095 USA
- California Nano Systems Institute (CNSI), University of California, Los Angeles, CA 90095 USA
| | - Yilin Luo
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
- Bioengineering Department, University of California, Los Angeles, CA 90095 USA
- California Nano Systems Institute (CNSI), University of California, Los Angeles, CA 90095 USA
| | - Gunvant Chaudhari
- David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
| | - Yair Rivenson
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
- Bioengineering Department, University of California, Los Angeles, CA 90095 USA
- California Nano Systems Institute (CNSI), University of California, Los Angeles, CA 90095 USA
| | - Ayfer Calis
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA
- Bioengineering Department, University of California, Los Angeles, CA 90095 USA
- California Nano Systems Institute (CNSI), University of California, Los Angeles, CA 90095 USA
| | - 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 Nano Systems Institute (CNSI), 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 Nano Systems Institute (CNSI), University of California, Los Angeles, CA 90095 USA
- David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
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