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1.
Cells ; 11(19)2022 Sep 30.
Article in English | MEDLINE | ID: covidwho-2065729

ABSTRACT

The transient nature of RNA has rendered it one of the more difficult biological targets for imaging. This difficulty stems both from the physical properties of RNA as well as the temporal constraints associated therewith. These concerns are further complicated by the difficulty in imaging endogenous RNA within a cell that has been transfected with a target sequence. These concerns, combined with traditional concerns associated with super-resolution light microscopy has made the imaging of this critical target difficult. Recent advances have provided researchers the tools to image endogenous RNA in live cells at both the cellular and single-molecule level. Here, we review techniques used for labeling and imaging RNA with special emphases on various labeling methods and a virtual 3D super-resolution imaging technique.


Subject(s)
Imaging, Three-Dimensional , Single Molecule Imaging , Imaging, Three-Dimensional/methods , RNA , RNA, Messenger/genetics , Single Molecule Imaging/methods
2.
BMC Biol ; 20(1): 183, 2022 08 23.
Article in English | MEDLINE | ID: covidwho-2038744

ABSTRACT

BACKGROUND: Efficient tools allowing the extraction of 2D surfaces from 3D-microscopy data are essential for studies aiming to decipher the complex cellular choreography through which epithelium morphogenesis takes place during development. Most existing methods allow for the extraction of a single and smooth manifold of sufficiently high signal intensity and contrast, and usually fail when the surface of interest has a rough topography or when its localization is hampered by other surrounding structures of higher contrast. Multiple surface segmentation entails laborious manual annotations of the various surfaces separately. RESULTS: As automating this task is critical in studies involving tissue-tissue or tissue-matrix interaction, we developed the Zellige software, which allows the extraction of a non-prescribed number of surfaces of varying inclination, contrast, and texture from a 3D image. The tool requires the adjustment of a small set of control parameters, for which we provide an intuitive interface implemented as a Fiji plugin. CONCLUSIONS: As a proof of principle of the versatility of Zellige, we demonstrate its performance and robustness on synthetic images and on four different types of biological samples, covering a wide range of biological contexts.


Subject(s)
Algorithms , Microscopy , Image Processing, Computer-Assisted/methods , Imaging, Three-Dimensional/methods , Microscopy/methods , Software
3.
Stud Health Technol Inform ; 295: 542-544, 2022 Jun 29.
Article in English | MEDLINE | ID: covidwho-1924044

ABSTRACT

For many clinical goals like surgical planning and radiotherapy treatment planning is necessary to understand the anatomical structures of the organ that is targeted. At the same time the 2D/3D shape of the organ is important to be reconstructed for the benefit of the doctors. For that reason, accurate segmentation techniques must be proposed to overcome the big data medical image storage problem. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.


Subject(s)
COVID-19 , Imaging, Three-Dimensional , COVID-19/diagnostic imaging , Humans , Image Processing, Computer-Assisted/methods , Imaging, Three-Dimensional/methods , Tomography, X-Ray Computed/methods
4.
Nat Methods ; 19(4): 479-485, 2022 04.
Article in English | MEDLINE | ID: covidwho-1764194

ABSTRACT

The recent development of solvent- and polymer-based brain-clearing techniques has advanced our ability to visualize the mammalian nervous system in three dimensions. However, it remains challenging to image the mammalian body en bloc. Here we developed HYBRiD (hydrogel-based reinforcement of three-dimensional imaging solvent-cleared organs (DISCO)), by recombining components of organic- and polymer-based clearing pipelines. We achieved high transparency and protein retention, as well as compatibility with direct fluorescent imaging and immunostaining in cleared mammalian bodies. Using parvalbumin- and somatostatin-Cre models, we demonstrated the utility of HYBRiD for whole-body imaging of genetically encoded fluorescent reporters without antibody enhancement of signals in newborn and juvenile mice. Using K18-hACE2 transgenic mice, HYBRiD enabled perfusion-free clearing and visualization of SARS-CoV-2 infection in a whole mouse chest, revealing macroscopic and microscopic features of viral pathology in the same sample. HYBRiD offers a simple and universal solution to visualize large heterogeneous body parts or entire animals for basic and translational research.


Subject(s)
COVID-19 , Hydrogels , Animals , Imaging, Three-Dimensional/methods , Mammals , Mice , Polymers , SARS-CoV-2 , Solvents
5.
Sci Rep ; 12(1): 1847, 2022 02 03.
Article in English | MEDLINE | ID: covidwho-1671622

ABSTRACT

Automatic segmentation of infected lesions from computed tomography (CT) of COVID-19 patients is crucial for accurate diagnosis and follow-up assessment. The remaining challenges are the obvious scale difference between different types of COVID-19 lesions and the similarity between the lesions and normal tissues. This work aims to segment lesions of different scales and lesion boundaries correctly by utilizing multiscale and multilevel features. A novel multiscale dilated convolutional network (MSDC-Net) is proposed against the scale difference of lesions and the low contrast between lesions and normal tissues in CT images. In our MSDC-Net, we propose a multiscale feature capture block (MSFCB) to effectively capture multiscale features for better segmentation of lesions at different scales. Furthermore, a multilevel feature aggregate (MLFA) module is proposed to reduce the information loss in the downsampling process. Experiments on the publicly available COVID-19 CT Segmentation dataset demonstrate that the proposed MSDC-Net is superior to other existing methods in segmenting lesion boundaries and large, medium, and small lesions, and achieves the best results in Dice similarity coefficient, sensitivity and mean intersection-over-union (mIoU) scores of 82.4%, 81.1% and 78.2%, respectively. Compared with other methods, the proposed model has an average improvement of 10.6% and 11.8% on Dice and mIoU. Compared with the existing methods, our network achieves more accurate segmentation of lesions at various scales and lesion boundaries, which will facilitate further clinical analysis. In the future, we consider integrating the automatic detection and segmentation of COVID-19, and conduct research on the automatic diagnosis system of COVID-19.


Subject(s)
COVID-19/diagnostic imaging , Neural Networks, Computer , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , Datasets as Topic , Female , Humans , Imaging, Three-Dimensional/methods , Male
6.
Nat Methods ; 18(12): 1532-1541, 2021 12.
Article in English | MEDLINE | ID: covidwho-1504972

ABSTRACT

Imaging intact human organs from the organ to the cellular scale in three dimensions is a goal of biomedical imaging. To meet this challenge, we developed hierarchical phase-contrast tomography (HiP-CT), an X-ray phase propagation technique using the European Synchrotron Radiation Facility (ESRF)'s Extremely Brilliant Source (EBS). The spatial coherence of the ESRF-EBS combined with our beamline equipment, sample preparation and scanning developments enabled us to perform non-destructive, three-dimensional (3D) scans with hierarchically increasing resolution at any location in whole human organs. We applied HiP-CT to image five intact human organ types: brain, lung, heart, kidney and spleen. HiP-CT provided a structural overview of each whole organ followed by multiple higher-resolution volumes of interest, capturing organotypic functional units and certain individual specialized cells within intact human organs. We demonstrate the potential applications of HiP-CT through quantification and morphometry of glomeruli in an intact human kidney and identification of regional changes in the tissue architecture in a lung from a deceased donor with coronavirus disease 2019 (COVID-19).


Subject(s)
COVID-19/pathology , Image Processing, Computer-Assisted/methods , Imaging, Three-Dimensional/methods , Lung/pathology , SARS-CoV-2 , Tomography, X-Ray Computed/methods , Humans , Kidney/anatomy & histology , Synchrotrons
8.
Viruses ; 13(4)2021 04 02.
Article in English | MEDLINE | ID: covidwho-1167763

ABSTRACT

The visualization of cellular ultrastructure over a wide range of volumes is becoming possible by increasingly powerful techniques grouped under the rubric "volume electron microscopy" or volume EM (vEM). Focused ion beam scanning electron microscopy (FIB-SEM) occupies a "Goldilocks zone" in vEM: iterative and automated cycles of milling and imaging allow the interrogation of microns-thick specimens in 3-D at resolutions of tens of nanometers or less. This bestows on FIB-SEM the unique ability to aid the accurate and precise study of architectures of virus-cell interactions. Here we give the virologist or cell biologist a primer on FIB-SEM imaging in the context of vEM and discuss practical aspects of a room temperature FIB-SEM experiment. In an in vitro study of SARS-CoV-2 infection, we show that accurate quantitation of viral densities and surface curvatures enabled by FIB-SEM imaging reveals SARS-CoV-2 viruses preferentially located at areas of plasma membrane that have positive mean curvatures.


Subject(s)
COVID-19/pathology , Host Microbial Interactions , Image Interpretation, Computer-Assisted/methods , Imaging, Three-Dimensional/methods , Microscopy, Electron, Scanning/methods , SARS-CoV-2 , Animals , Cell Communication , Cell Membrane , Chlorocebus aethiops , Epithelial Cells/virology , Humans , Lung , Vero Cells
9.
Comput Math Methods Med ; 2021: 6633755, 2021.
Article in English | MEDLINE | ID: covidwho-1140372

ABSTRACT

AIM: COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. METHODS: In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. RESULTS: The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. CONCLUSION: This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.


Subject(s)
COVID-19/diagnostic imaging , Community-Acquired Infections/diagnostic imaging , Diagnosis, Computer-Assisted/methods , Imaging, Three-Dimensional/methods , Neural Networks, Computer , Pneumonia/diagnostic imaging , Tuberculosis, Pulmonary/diagnostic imaging , Algorithms , COVID-19/complications , Community-Acquired Infections/complications , Databases, Factual , Humans , Medical Informatics , Pneumonia/complications , Radiography, Thoracic , Reproducibility of Results , Retrospective Studies , Software , Stochastic Processes , Tomography, X-Ray Computed , Tuberculosis, Pulmonary/complications
10.
J Vis Exp ; (166)2020 12 19.
Article in English | MEDLINE | ID: covidwho-1067800

ABSTRACT

Segmentation is a complex task, faced by radiologists and researchers as radiomics and machine learning grow in potentiality. The process can either be automatic, semi-automatic, or manual, the first often not being sufficiently precise or easily reproducible, and the last being excessively time consuming when involving large districts with high-resolution acquisitions. A high-resolution CT of the chest is composed of hundreds of images, and this makes the manual approach excessively time consuming. Furthermore, the parenchymal alterations require an expert evaluation to be discerned from the normal appearance; thus, a semi-automatic approach to the segmentation process is, to the best of our knowledge, the most suitable when segmenting pneumonias, especially when their features are still unknown. For the studies conducted in our institute on the imaging of COVID-19, we adopted 3D Slicer, a freeware software produced by the Harvard University, and combined the threshold with the paint brush instruments to achieve fast and precise segmentation of aerated lung, ground glass opacities, and consolidations. When facing complex cases, this method still requires a considerable amount of time for proper manual adjustments, but provides an extremely efficient mean to define segments to use for further analysis, such as the calculation of the percentage of the affected lung parenchyma or texture analysis of the ground glass areas.


Subject(s)
COVID-19/diagnostic imaging , Imaging, Three-Dimensional/standards , Lung/diagnostic imaging , SARS-CoV-2 , Software/standards , Tomography, X-Ray Computed/standards , COVID-19/epidemiology , Humans , Imaging, Three-Dimensional/methods , Pneumonia/diagnostic imaging , Pneumonia/epidemiology , Tomography, X-Ray Computed/methods
11.
PLoS One ; 15(12): e0243388, 2020.
Article in English | MEDLINE | ID: covidwho-1067393

ABSTRACT

The use of high quality facemasks is indispensable in the light of the current COVID pandemic. This study proposes a fully automatic technique to design a face specific mask. Through the use of stereophotogrammetry, computer-assisted design and three-dimensional (3D) printing, we describe a protocol for manufacturing facemasks perfectly adapted to the individual face characteristics. The face specific mask was compared to a universal design of facemask and different filter container's designs were merged with the mask body. Subjective assessment of the face specific mask demonstrated tight closure at the nose, mouth and chin area, and permits the normal wearing of glasses. A screw-drive locking system is advised for easy assembly of the filter components. Automation of the process enables high volume production but still allows sufficient designer interaction to answer specific requirements. The suggested protocol can be used to provide more comfortable, effective and sustainable solution compared to a single use, standardized mask. Subsequent research on printing materials, sterilization technique and compliance with international regulations will facilitate the introduction of the face specific mask in clinical practice as well as for general use.


Subject(s)
Computer-Aided Design , Masks , Printing, Three-Dimensional , COVID-19/epidemiology , COVID-19/prevention & control , Face/anatomy & histology , Face/diagnostic imaging , Humans , Imaging, Three-Dimensional/methods , Pandemics/prevention & control , Photogrammetry/methods , Proof of Concept Study , Universal Design
13.
Oral Oncol ; 113: 105033, 2021 02.
Article in English | MEDLINE | ID: covidwho-843400

ABSTRACT

BACKGROUND: The COVID-19 pandemic has swept across the globe with massive effects on health care systems as well as global economies. Enhanced testing has been put forward as a means to reduce transmission while awaiting the development of targeted therapy or effective vaccination. However, achieving accurate testing necessitates proper nasopharyngeal swab techniques. METHODS AND RESULTS: We aimed to design and investigate the utility of an anatomically accurate three-dimensional (3D) printed model of the nose in the training for nasopharyngeal swabs. These models were implemented during training sessions for healthcare workers. All participants surveyed felt that the 3D printed models were useful and beneficial in the training of nasopharyngeal swab techniques. CONCLUSIONS: 3D printed nose models are a useful tool in nasopharyngeal swab training. Their usage may help to facilitate the training of potential swabbing manpower in the upscaling of testing capabilities and volumes in this COVID-19 era.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , COVID-19/epidemiology , Diagnostic Tests, Routine/methods , Imaging, Three-Dimensional/methods , Nasopharynx/virology , Nose/anatomy & histology , Pandemics , SARS-CoV-2 , Specimen Handling/methods , COVID-19/virology , Health Personnel/education , Humans , Printing, Three-Dimensional
16.
Nat Commun ; 11(1): 4080, 2020 08 14.
Article in English | MEDLINE | ID: covidwho-717116

ABSTRACT

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.


Subject(s)
Artificial Intelligence , Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , Aged, 80 and over , Algorithms , Betacoronavirus/isolation & purification , COVID-19 , COVID-19 Testing , Child , Child, Preschool , Coronavirus Infections/diagnosis , Coronavirus Infections/virology , Deep Learning , Female , Humans , Imaging, Three-Dimensional/methods , Lung/diagnostic imaging , Male , Middle Aged , Pandemics , Pneumonia, Viral/virology , Radiographic Image Interpretation, Computer-Assisted/methods , SARS-CoV-2 , Young Adult
17.
IEEE Trans Med Imaging ; 39(8): 2584-2594, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-690554

ABSTRACT

Automated Screening of COVID-19 from chest CT is of emergency and importance during the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 is still a massive challenge due to the spatial complexity of 3D volumes, the labeling difficulty of infection areas, and the slight discrepancy between COVID-19 and other viral pneumonia in chest CT. While a few pioneering works have made significant progress, they are either demanding manual annotations of infection areas or lack of interpretability. In this paper, we report our attempt towards achieving highly accurate and interpretable screening of COVID-19 from chest CT with weak labels. We propose an attention-based deep 3D multiple instance learning (AD3D-MIL) where a patient-level label is assigned to a 3D chest CT that is viewed as a bag of instances. AD3D-MIL can semantically generate deep 3D instances following the possible infection area. AD3D-MIL further applies an attention-based pooling approach to 3D instances to provide insight into each instance's contribution to the bag label. AD3D-MIL finally learns Bernoulli distributions of the bag-level labels for more accessible learning. We collected 460 chest CT examples: 230 CT examples from 79 patients with COVID-19, 100 CT examples from 100 patients with common pneumonia, and 130 CT examples from 130 people without pneumonia. A series of empirical studies show that our algorithm achieves an overall accuracy of 97.9%, AUC of 99.0%, and Cohen kappa score of 95.7%. These advantages endow our algorithm as an efficient assisted tool in the screening of COVID-19.


Subject(s)
Coronavirus Infections/diagnostic imaging , Deep Learning , Imaging, Three-Dimensional/methods , Pneumonia, Viral/diagnostic imaging , Algorithms , Betacoronavirus , COVID-19 , Humans , Lung/diagnostic imaging , Pandemics , Radiography, Thoracic , SARS-CoV-2 , Tomography, X-Ray Computed
18.
Radiology ; 296(2): E65-E71, 2020 08.
Article in English | MEDLINE | ID: covidwho-657750

ABSTRACT

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity. Results The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (±standard deviation) was 49 years ± 15, and there were slightly more men than women (1838 vs 1484, respectively; P = .29). The per-scan sensitivity and specificity for detecting COVID-19 in the independent test set was 90% (95% confidence interval [CI]: 83%, 94%; 114 of 127 scans) and 96% (95% CI: 93%, 98%; 294 of 307 scans), respectively, with an area under the receiver operating characteristic curve of 0.96 (P < .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020 Online supplemental material is available for this article.


Subject(s)
Artificial Intelligence , Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , Aged , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques/methods , Community-Acquired Infections/diagnostic imaging , Coronavirus Infections/diagnosis , Deep Learning , Diagnosis, Differential , Female , Humans , Imaging, Three-Dimensional/methods , Male , Middle Aged , Pandemics , ROC Curve , Radiographic Image Interpretation, Computer-Assisted/methods , Retrospective Studies , SARS-CoV-2 , Sensitivity and Specificity , Tomography, X-Ray Computed/methods
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