Fernandezulriksen0966

Early diagnosis is critical for the prevention and control of the coronavirus disease 2019 (COVID-19). We attempted to apply a protocol using teleultrasound, which is supported by the 5G network, to explore the feasibility of solving the problem of early imaging assessment of COVID-19. Four male patients with confirmed or suspected COVID-19 were hospitalized in isolation wards in two different cities. Ultrasound specialists, located in two other different cities, carried out the robot-assisted teleultrasound and remote consultation in order to settle the problem of early cardiopulmonary evaluation. Lung ultrasound, brief echocardiography, and blood volume assessment were performed. Whenever difficulties of remote manipulation and diagnosis occurred, the alternative examination was repeated by a specialist from another city, and in sequence, remote consultation was conducted immediately to meet the consensus. The ultrasound specialists successfully completed the telerobotic ultrasound. Lung ultrasound indicated signs of pneumonia with varying degrees in all cases and mild pleural effusion in one case. No abnormalities of cardiac structure and function and blood volume were detected. Remote consultation on the issue of manipulation practice, and the diagnosis in one case was conducted. The cardiopulmonary information was delivered to the frontline clinicians immediately for further treatment. The practice of teleultrasound protocol makes early diagnosis and repeated assessment available in the isolation ward. Ultrasound specialists can be protected from infection, and personal protective equipment can be spared. Quality control can be ensured by remote consultations among doctors. This protocol is worth consideration as a feasible strategy for early imaging assessment in the COVID-19 pandemic.Inflammation of the gastrointestinal (GI) tract accompanies several diseases, including Crohn's disease. TDO inhibitor Currently, video capsule endoscopy and deep bowel enteroscopy are the main means for direct visualisation of the bowel surface. However, the use of optical imaging limits visualisation to the luminal surface only, which makes early-stage diagnosis difficult. In this study, we propose a learning enabled microultrasound ( μ US) system that aims to classify inflamed and non-inflamed bowel tissues. μ US images of the caecum, small bowel and colon were obtained from mice treated with agents to induce inflammation. Those images were then used to train three deep learning networks and to provide a ground truth of inflammation status. The classification accuracy was evaluated using 10-fold evaluation and additional B-scan images. Our deep learning approach allowed robust differentiation between healthy tissue and tissue with early signs of inflammation that is not detectable by current endoscopic methods or by human inspection of the μ US images. The methods may be a foundation for future early GI disease diagnosis and enhanced management with computer-aided imaging.The morphology reconstruction (tracing) of neurons in 3D microscopy images is important to neuroscience research. However, this task remains very challenging because of the low signal-to-noise ratio (SNR) and the discontinued segments of neurite patterns in the images. In this paper, we present a neuronal structure segmentation method based on the ray-shooting model and the Long Short-Term Memory (LSTM)-based network to enhance the weak-signal neuronal structures and remove background noise in 3D neuron microscopy images. Specifically, the ray-shooting model is used to extract the intensity distribution features within a local region of the image. And we design a neural network based on the dual channel bidirectional LSTM (DC-BLSTM) to detect the foreground voxels according to the voxel-intensity features and boundary-response features extracted by multiple ray-shooting models that are generated in the whole image. This way, we transform the 3D image segmentation task into multiple 1D ray/sequence segmentation tasks, which makes it much easier to label the training samples than many existing Convolutional Neural Network (CNN) based 3D neuron image segmentation methods. In the experiments, we evaluate the performance of our method on the challenging 3D neuron images from two datasets, the BigNeuron dataset and the Whole Mouse Brain Sub-image (WMBS) dataset. Compared with the neuron tracing results on the segmented images produced by other state-of-the-art neuron segmentation methods, our method improves the distance scores by about 32% and 27% in the BigNeuron dataset, and about 38% and 27% in the WMBS dataset.Cancer diagnosis, prognosis, and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data. However, most deep learning-based objective outcome prediction and grading paradigms are based on histology or genomics alone and do not make use of the complementary information in an intuitive manner. In this work, we propose Pathomic Fusion, an interpretable strategy for end-to-end multimodal fusion of histology image and genomic (mutations, CNV, RNASeq) features for survival outcome prediction. Our approach models pairwise feature interactions across modalities by taking the Kronecker product of unimodal feature representations, and controls the expressiveness of each representation via a gatingbased attention mechanism. Following supervised learning, we are able to interpret and saliently localize features across each modality, and understand how feature importance shifts when conditioning on multimodal input. We validate our approach using glioma and clear cell renal cell carcinoma datasets from the Cancer Genome Atlas (TCGA), which contains paired wholeslide image, genotype, and transcriptome data with ground truth survival and histologic grade labels. In a 15-fold cross-validation, our results demonstrate that the proposed multimodal fusion paradigm improves prognostic determinations from ground truth grading and molecular subtyping, as well as unimodal deep networks trained on histology and genomic data alone. The proposed method establishes insight and theory on how to train deep networks on multimodal biomedical data in an intuitive manner, which will be useful for other problems in medicine that seek to combine heterogeneous data streams for understanding diseases and predicting response and resistance to treatment. Code and trained models are made available at https//github.com/mahmoodlab/PathomicFusion.