US-Net: A lightweight network for simultaneous speckle suppression and texture enhancement in ultrasound images.

BACKGROUND: Numerous traditional filtering approaches and deep learning-based methods have been proposed to improve the quality of ultrasound (US) image data. However, their results tend to suffer from over-smoothing and loss of texture and fine details. Moreover, they perform poorly on images with different degradation levels and mainly focus on speckle reduction, even though texture and fine detail enhancement are of crucial importance in clinical diagnosis. METHODS: We propose an end-to-end framework termed US-Net for simultaneous speckle suppression and texture enhancement in US images. The architecture of US-Net is inspired by U-Net, whereby a feature refinement attention block (FRAB) is introduced to enable an effective learning of multi-level and multi-contextual representative features. Specifically, FRAB aims to emphasize high-frequency image information, which helps boost the restoration and preservation of fine-grained and textural details. Furthermore, our proposed US-Net is trained essentially with real US image data, whereby real US images embedded with simulated multi-level speckle noise are used as an auxiliary training set. RESULTS: Extensive quantitative and qualitative experiments indicate that although trained with only one US image data type, our proposed US-Net is capable of restoring images acquired from different body parts and scanning settings with different degradation levels, while exhibiting favorable performance against state-of-the-art image enhancement approaches. Furthermore, utilizing our proposed US-Net as a pre-processing stage for COVID-19 diagnosis results in a gain of 3.6% in diagnostic accuracy. CONCLUSIONS: The proposed framework can help improve the accuracy of ultrasound diagnosis.

SARS-CoV-2: preliminary study of infected human nasopharyngeal tissue by high resolution microscopy.

BACKGROUND: The novel coronavirus SARS-CoV-2 is the etiological agent of COVID-19. This virus has become one of the most dangerous in recent times with a very high rate of transmission. At present, several publications show the typical crown-shape of the novel coronavirus grown in cell cultures. However, an integral ultramicroscopy study done directly from clinical specimens has not been published. METHODS: Nasopharyngeal swabs were collected from 12 Cuban individuals, six asymptomatic and RT-PCR negative (negative control) and six others from a COVID-19 symptomatic and RT-PCR positive for SARS CoV-2. Samples were treated with an aldehyde solution and processed by scanning electron microscopy (SEM), confocal microscopy (CM) and, atomic force microscopy. Improvement and segmentation of coronavirus images were performed by a novel mathematical image enhancement algorithm. RESULTS: The images of the negative control sample showed the characteristic healthy microvilli morphology at the apical region of the nasal epithelial cells. As expected, they do not display virus-like structures. The images of the positive sample showed characteristic coronavirus-like particles and evident destruction of microvilli. In some regions, virions budding through the cell membrane were observed. Microvilli destruction could explain the anosmia reported by some patients. Virus-particles emerging from the cell-surface with a variable size ranging from 80 to 400 nm were observed by SEM. Viral antigen was identified in the apical cells zone by CM. CONCLUSIONS: The integral microscopy study showed that SARS-CoV-2 has a similar image to SARS-CoV. The application of several high-resolution microscopy techniques to nasopharyngeal samples awaits future use.

COVID-19 Diagnosis Using Capsule Network and Fuzzy C-Means and Mayfly Optimization Algorithm.

The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C-ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F1-score rate gives the uppermost value compared to the others.

Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms.

In recent years, computerized biomedical imaging and analysis have become extremely promising, more interesting, and highly beneficial. They provide remarkable information in the diagnoses of skin lesions. There have been developments in modern diagnostic systems that can help detect melanoma in its early stages to save the lives of many people. There is also a significant growth in the design of computer-aided diagnosis (CAD) systems using advanced artificial intelligence. The purpose of the present research is to develop a system to diagnose skin cancer, one that will lead to a high level of detection of the skin cancer. The proposed system was developed using deep learning and traditional artificial intelligence machine learning algorithms. The dermoscopy images were collected from the PH2 and ISIC 2018 in order to examine the diagnose system. The developed system is divided into feature-based and deep leaning. The feature-based system was developed based on feature-extracting methods. In order to segment the lesion from dermoscopy images, the active contour method was proposed. These skin lesions were processed using hybrid feature extractions, namely, the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods to extract the texture features. The obtained features were then processed using the artificial neural network (ANNs) algorithm. In the second system, the convolutional neural network (CNNs) algorithm was applied for the efficient classification of skin diseases; the CNNs were pretrained using large AlexNet and ResNet50 transfer learning models. The experimental results show that the proposed method outperformed the state-of-art methods for HP2 and ISIC 2018 datasets. Standard evaluation metrics like accuracy, specificity, sensitivity, precision, recall, and F-score were employed to evaluate the results of the two proposed systems. The ANN model achieved the highest accuracy for PH2 (97.50%) and ISIC 2018 (98.35%) compared with the CNN model. The evaluation and comparison, proposed systems for classification and detection of melanoma are presented.

Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening.

Rapid screening of red blood cells for active infection of COVID-19 is presented using a compact and field-portable, 3D-printed shearing digital holographic microscope. Video holograms of thin blood smears are recorded, individual red blood cells are segmented for feature extraction, then a bi-directional long short-term memory network is used to classify between healthy and COVID positive red blood cells based on their spatiotemporal behavior. Individuals are then classified based on the simple majority of their cells' classifications. The proposed system may be beneficial for under-resourced healthcare systems. To the best of our knowledge, this is the first report of digital holographic microscopy for rapid screening of COVID-19.

Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images.

Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.

Teleproctoring for Neurovascular Procedures: Demonstration of Concept Using Optical See-Through Head-Mounted Display, Interactive Mixed Reality, and Virtual Space Sharing-A Critical Need Highlighted by the COVID-19 Pandemic.

BACKGROUND AND PURPOSE: Physician training and onsite proctoring are critical for safely introducing new biomedical devices, a process that has been disrupted by the pandemic. A teleproctoring concept using optical see-through head-mounted displays with a proctor's ability to see and, more important, virtually interact in the operator's visual field is presented. MATERIALS AND METHODS: Test conditions were created for simulated proctoring using a bifurcation aneurysm flow model for WEB device deployment. The operator in the angiography suite wore a Magic Leap-1 optical see-through head-mounted display to livestream his or her FOV to a proctor's computer in an adjacent building. A Web-based application (Spatial) was used for the proctor to virtually interact in the operator's visual space. Tested elements included the quality of the livestream, communication, and the proctor's ability to interact in the operator's environment using mixed reality. A hotspot and a Wi-Fi-based network were tested. RESULTS: The operator successfully livestreamed the angiography room environment and his FOV of the monitor to the remotely located proctor. The proctor communicated and guided the operator through the procedure over the optical see-through head-mounted displays, a process that was repeated several times. The proctor used mixed reality and virtual space sharing to successfully project images, annotations, and data in the operator's FOV for highlighting any device or procedural aspects. The livestream latency was 0.71 (SD, 0.03) seconds for Wi-Fi and 0.86 (SD, 0.3) seconds for the hotspot (P = .02). The livestream quality was subjectively better over the Wi-Fi. CONCLUSIONS: New technologies using head-mounted displays and virtual space sharing could offer solutions applicable to remote proctoring in the neurointerventional space.

Multi-window back-projection residual networks for reconstructing COVID-19 CT super-resolution images.

BACKGROUND AND OBJECTIVE: With the increasing problem of coronavirus disease 2019 (COVID-19) in the world, improving the image resolution of COVID-19 computed tomography (CT) becomes a very important task. At present, single-image super-resolution (SISR) models based on convolutional neural networks (CNN) generally have problems such as the loss of high-frequency information and the large size of the model due to the deep network structure. METHODS: In this work, we propose an optimization model based on multi-window back-projection residual network (MWSR), which outperforms most of the state-of-the-art methods. Firstly, we use multi-window to refine the same feature map at the same time to obtain richer high/low frequency information, and fuse and filter out the features needed by the deep network. Then, we develop a back-projection network based on the dilated convolution, using up-projection and down-projection modules to extract image features. Finally, we merge several repeated and continuous residual modules with global features, merge the information flow through the network, and input them to the reconstruction module. RESULTS: The proposed method shows the superiority over the state-of-the-art methods on the benchmark dataset, and generates clear COVID-19 CT super-resolution images. CONCLUSION: Both subjective visual effects and objective evaluation indicators are improved, and the model specifications are optimized. Therefore, the MWSR method can improve the clarity of CT images of COVID-19 and effectively assist the diagnosis and quantitative assessment of COVID-19.

Cardiac involvement in a pediatric patient with COVID-19: Looking beyond the nonspecific global cardiac injury.

We report a case of a 17-year-old healthy male presenting with multisystem hyperinflammatory shock temporally associated with COVID-19. Cardiac involvement was suspected based on evidence of significant cardiac injury (elevated cardiac biomarkers, electrocardiographic and echocardiographic abnormalities). Cardiac magnetic resonance imaging was performed demonstrating global biventricular systolic dysfunction, as well as a small area of T2 hyperintensity and mid-wall late gadolinium enhancement. This case discusses the varied cardiac involvement in pediatric patients with COVID-19 infection and highlights that cardiac injury is not just limited to hyperinflammatory syndrome related global dysfunction but a more focal myocarditis can also be seen.

Contrast-Enhanced Ultrasound in Patients With COVID-19: Pneumonia, Acute Respiratory Distress Syndrome, or Something Else?

Coronavirus disease 2019 (COVID-19) represents a very heterogeneous disease. Some aspects of COVID-19 pneumonia question the real nature of ground glass opacities and its consolidative lesions. It has been hypothesized that COVID-19 lung involvement could represent not only a viral effect but also an immune response induced by the infection, causing epithelial/endothelial lesions and coagulation disorders. We report 3 cases of COVID-19 pneumonia in which contrast-enhanced ultrasound was suggestive of consolidations with perfusion defects, at least in part caused by ischemic or necrotic changes and not only by inflammatory or atelectasis events.