Binary Classification of COVID-19 CT Images Using CNN

COVID-19 pandemic has hit the world with such a force that the world's leading economies are finding it challenging to come out of it. Countries with the best medical facilities are even cannot handle the increasing number of cases and fatalities. This disease causes significant damage to the lungs and respiratory system of humans, leading to their death. Computed tomography (CT) images of the respiratory system are analyzed in the proposed work to classify the infected people with non-infected people. Deep learning binary classification algorithms have been applied, which have shown an accuracy of 86.9% on 746 CT images of chest having COVID-19 related symptoms.

Deep segmentation of the liver and the hepatic tumors from abdomen tomography images

A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two output-classified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation.

Postmortem brain 7T MRI with minimally invasive pathological correlation in deceased COVID-19 subjects

Background Brain abnormalities are a concern in COVID-19, so we used minimally invasive autopsy (MIA) to investigate it, consisting of brain 7T MR and CT images and tissue sampling via transethmoidal route with at least three fragments: the first one for reverse transcription polymerase chain reaction (RT-PCR) analysis and the remaining fixed and stained with hematoxylin and eosin. Two mouse monoclonal anti-coronavirus (SARS-CoV-2) antibodies were employed in immunohistochemical (IHC) reactions. Results Seven deceased COVID-19 patients underwent MIA with brain MR and CT images, six of them with tissue sampling. Imaging findings included infarcts, punctate brain hemorrhagic foci, subarachnoid hemorrhage and signal abnormalities in the splenium, basal ganglia, white matter, hippocampi and posterior cortico-subcortical. Punctate brain hemorrhage was the most common finding (three out of seven cases). Brain histological analysis revealed reactive gliosis, congestion, cortical neuron eosinophilic degeneration and axonal disruption in all six cases. Other findings included edema (5 cases), discrete perivascular hemorrhages (5), cerebral small vessel disease (3), perivascular hemosiderin deposits (3), Alzheimer type II glia (3), abundant corpora amylacea (3), ischemic foci (1), periventricular encephalitis foci (1), periventricular vascular ectasia (1) and fibrin thrombi (1). SARS-CoV-2 RNA was detected with RT-PCR in 5 out of 5 and IHC in 6 out 6 patients (100%). Conclusions Despite limited sampling, MIA was an effective tool to evaluate underlying pathological brain changes in deceased COVID-19 patients. Imaging findings were varied, and pathological features corroborated signs of hypoxia, alterations related to systemic critically ill and SARS-CoV-2 brain invasion.

Deep Ensemble Learning-Based Models for Diagnosis of COVID-19 from Chest CT Images

Novel coronavirus (COVID-19) has been endangering human health and life since 2019. The timely quarantine, diagnosis, and treatment of infected people are the most necessary and important work. The most widely used method of detecting COVID-19 is real-time polymerase chain reaction (RT-PCR). Along with RT-PCR, computed tomography (CT) has become a vital technique in diagnosing and managing COVID-19 patients. COVID-19 reveals a number of radiological signatures that can be easily recognized through chest CT. These signatures must be analyzed by radiologists. It is, however, an error-prone and time-consuming process. Deep Learning-based methods can be used to perform automatic chest CT analysis, which may shorten the analysis time. The aim of this study is to design a robust and rapid medical recognition system to identify positive cases in chest CT images using three Ensemble Learning-based models. There are several techniques in Deep Learning for developing a detection system. In this paper, we employed Transfer Learning. With this technique, we can apply the knowledge obtained from a pre-trained Convolutional Neural Network (CNN) to a different but related task. In order to ensure the robustness of the proposed system for identifying positive cases in chest CT images, we used two Ensemble Learning methods namely Stacking and Weighted Average Ensemble (WAE) to combine the performances of three fine-tuned Base-Learners (VGG19, ResNet50, and DenseNet201). For Stacking, we explored 2-Levels and 3-Levels Stacking. The three generated Ensemble Learning-based models were trained on two chest CT datasets. A variety of common evaluation measures (accuracy, recall, precision, and F1-score) are used to perform a comparative analysis of each method. The experimental results show that the WAE method provides the most reliable performance, achieving a high recall value which is a desirable outcome in medical applications as it poses a greater risk if a true infected patient is not identified.

Automatic Aortic Valve Cusps Segmentation from CT Images Based on the Cascading Multiple Deep Neural Networks

Accurate morphological information on aortic valve cusps is critical in treatment planning. Image segmentation is necessary to acquire this information, but manual segmentation is tedious and time consuming. In this paper, we propose a fully automatic aortic valve cusps segmentation method from CT images by combining two deep neural networks, spatial configuration-Net for detecting anatomical landmarks and U-Net for segmentation of aortic valve components. A total of 258 CT volumes of end systolic and end diastolic phases, which include cases with and without severe calcifications, were collected and manually annotated for each aortic valve component. The collected CT volumes were split 6:2:2 for the training, validation and test steps, and our method was evaluated by five-fold cross validation. The segmentation was successful for all CT volumes with 69.26 s as mean processing time. For the segmentation results of the aortic root, the right-coronary cusp, the left-coronary cusp and the non-coronary cusp, mean Dice Coefficient were 0.95, 0.70, 0.69, and 0.67, respectively. There were strong correlations between measurement values automatically calculated based on the annotations and those based on the segmentation results. The results suggest that our method can be used to automatically obtain measurement values for aortic valve morphology.