Chest Computed Tomography Slices Research Articles

Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer Perceptron.

The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented. The lung tissues are segmented using Otsu's thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier's accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features. Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier's results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall's Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered. The MLP classifier's accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94%.

Diagnosis of Pulmonary Edema and Covid-19 from CT slices using Squirrel Search Algorithm, Support Vector Machine and Back Propagation Neural Network

A Computer Aided Diagnosis (CAD) framework to diagnose Pulmonary Edema (PE) and covid-19 from the chest Computed Tomography (CT) slices were developed and implemented in this work. The lung tissues have been segmented using Otsu’s thresholding method. The Regions of Interest (ROI) considered in this work were edema lesions and covid-19 lesions. For each ROI, the edema lesions and covid-19 lesions were elucidated by an expert radiologist, followed by texture and shape extraction. The extracted features were stored as feature vectors. The feature vectors were split into train and test set in the ratio of 80 : 20. A wrapper based feature selection approach using Squirrel Search Algorithm (SSA) with the Support Vector Machine (SVM) classifier’s accuracy as the fitness function was used to select the optimal features. The selected features were trained using the Back Propagation Neural Network (BPNN) classifier. This framework was tested on a real-time PE and covid-19 dataset. The BPNN classifier’s accuracy with SSA yielded 88.02%, whereas, without SSA it yielded 83.80%. Statistical analysis, namely Wilcoxon’s test, Kendall’s Rank Correlation Coefficient test and Mann Whitney U test were performed, which indicates that the proposed method has a significant impact on the accuracy, sensitivity and specificity of the novel dataset considered. Comparative experimentations of the proposed system with existing benchmark ML classifiers, namely Cat Boost, Ada Boost, XGBoost, RBF SVM, Poly SVM, Sigmoid SVM and Linear SVM classifiers demonstrate that the proposed system outperforms the benchmark classifiers’ results.

Feasibility of Simple Evaluation of Coronary Artery Calcium Using a Number of Chest Computed Tomography Slices: A Multicenter Study

Objective: We aimed to evaluate the visual measurements of coronary artery calcium (CAC) on nonelectrocardiogram (ECG)-gated chest computed tomography (CT) using a simple scoring method that involves counting the number of CT slices containing CAC. Materials and Methods: We analyzed 163 participants who underwent both coronary and chest CT examinations at six centers within 3 months. Agatston scores were calculated on standard ECG-gated scans and classified as none (0), mild (1-99), moderate (100-400), or severe (>400). Next, chest CT images were reconstructed to standard 5.0 mm axial slices. Then, CAC on chest CT scans was measured using two methods: the Weston score (sum of the assigned score of each vessel, range: 0-12) and number of slices showing CAC (Ca-slice#). Results: When the Weston score and Ca-slice# were divided into four levels according to the optimal divisional levels corresponding to the Agatston score classes, good agreements with the 4-grade Agatston score were observed (kappa value=0.610 and 0.794, respectively). The sensitivity and specificity of Ca-slice# ≥9 to identify severe Agatston scores of >400 were 86% and 96%, respectively. Conclusion: The Ca-slice#, a simple scoring method using chest CT scans, was in good agreement with the ECG-gated Agatston score.

Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning.

Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting millions of people's health and lives in jeopardy. Detecting infected patients early on chest computed tomography (CT) is critical in combating COVID-19. Harnessing uncertainty-aware consensus-assisted multiple instance learning (UC-MIL), we propose to diagnose COVID-19 using a new bilateral adaptive graph-based (BA-GCN) model that can use both 2D and 3D discriminative information in 3D CT volumes with arbitrary number of slices. Given the importance of lung segmentation for this task, we have created the largest manual annotation dataset so far with 7,768 slices from COVID-19 patients, and have used it to train a 2D segmentation model to segment the lungs from individual slices and mask the lungs as the regions of interest for the subsequent analyses. We then used the UC-MIL model to estimate the uncertainty of each prediction and the consensus between multiple predictions on each CT slice to automatically select a fixed number of CT slices with reliable predictions for the subsequent model reasoning. Finally, we adaptively constructed a BA-GCN with vertices from different granularity levels (2D and 3D) to aggregate multi-level features for the final diagnosis with the benefits of the graph convolution network's superiority to tackle cross-granularity relationships. Experimental results on three largest COVID-19 CT datasets demonstrated that our model can produce reliable and accurate COVID-19 predictions using CT volumes with any number of slices, which outperforms existing approaches in terms of learning and generalisation ability. To promote reproducible research, we have made the datasets, including the manual annotations and cleaned CT dataset, as well as the implementation code, available at https://doi.org/10.5281/zenodo.6361963.

CT-based COPD identification using multiple instance learning with two-stage attention

Background and objectiveChronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality worldwide. However, COPD remains underdiagnosed globally. Spirometry is currently the primary tool for diagnosing COPD, but it has unneglected difficulties in detecting mild COPD. Chest computed tomography (CT) has been validated for COPD diagnosis and quantification. Whereas many CT-based deep learning approaches have been developed to identify COPD, it remains challenging to characterize CT-based pathological alternations of COPD which are multidimensional and highly spatially heterogeneous, and the diagnosis performance still needs to be improved. MethodsA multiple instance learning (MIL) with two-stage attention (TSA-MIL) is proposed to identify COPD using CT images. Based on transfer learning, a Resnet-50 model pre-trained on natural images is used to extract multicomponent and multidimensional features of COPD abnormalities, in which a pseudo-color method is designed to transfer single-channel CT slices to RGB-like three channels and meanwhile increase the richness of feature representations. To generate more robust attention score for each instance, a two-stage attention module is utilized with the first stage aiming at discovering the key instance while the second stage correcting the attention score for each instance by calculating its average relative distance to the key instances; besides, an instance-level clustering over feature domain is exploited to further improve feature separability and therefore facilitate the subsequent attention module. CT scans, spirometry and demographic data of a total of 800 participants were collected from a large public hospital, with 720 and 80 participants used for model development and evaluation, respectively. In addition, data of 260 participants from another large hospital were also collected for external validation. Results and ConclusionsThe proposed TSA-MIL approach outperforms not only most of the advanced MIL models, but also other up-to-date COPD identification methods, with an accuracy of 0.9200 and an area under curve (AUC) of 0.9544 on the test set, and with an accuracy of 0.8115 and an AUC of 0.8737 on the external validation set without multicenter effect reduction, which is clinically acceptable. Therefore, this approach is promising to be a powerful tool for COPD diagnosis in clinical practice.

SSA-Net: Spatial self-attention network for COVID-19 pneumonia infection segmentation with semi-supervised few-shot learning.

Coronavirus disease (COVID-19) broke out at the end of 2019, and has resulted in an ongoing global pandemic. Segmentation of pneumonia infections from chest computed tomography (CT) scans of COVID-19 patients is significant for accurate diagnosis and quantitative analysis. Deep learning-based methods can be developed for automatic segmentation and offer a great potential to strengthen timely quarantine and medical treatment. Unfortunately, due to the urgent nature of the COVID-19 pandemic, a systematic collection of CT data sets for deep neural network training is quite difficult, especially high-quality annotations of multi-category infections are limited. In addition, it is still a challenge to segment the infected areas from CT slices because of the irregular shapes and fuzzy boundaries. To solve these issues, we propose a novel COVID-19 pneumonia lesion segmentation network, called Spatial Self-Attention network (SSA-Net), to identify infected regions from chest CT images automatically. In our SSA-Net, a self-attention mechanism is utilized to expand the receptive field and enhance the representation learning by distilling useful contextual information from deeper layers without extra training time, and spatial convolution is introduced to strengthen the network and accelerate the training convergence. Furthermore, to alleviate the insufficiency of labeled multi-class data and the long-tailed distribution of training data, we present a semi-supervised few-shot iterative segmentation framework based on re-weighting the loss and selecting prediction values with high confidence, which can accurately classify different kinds of infections with a small number of labeled image data. Experimental results show that SSA-Net outperforms state-of-the-art medical image segmentation networks and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage. Meanwhile, our semi-supervised iterative segmentation model can improve the learning ability in small and unbalanced training set and can achieve higher performance.

Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System.

The coronavirus disease 2019 (COVID-19) has caused millions of deaths and one of the greatest health crises of all time. In this disease, one of the most important aspects is the early detection of the infection to avoid the spread. In addition to this, it is essential to know how the disease progresses in patients, to improve patient care. This contribution presents a novel method based on a hierarchical intelligent system, that analyzes the application of deep learning models to detect and classify patients with COVID-19 using both X-ray and chest computed tomography (CT). The methodology was divided into three phases, the first being the detection of whether or not a patient suffers from COVID-19, the second step being the evaluation of the percentage of infection of this disease and the final phase is to classify the patients according to their severity. Stratification of patients suffering from COVID-19 according to their severity using automatic systems based on machine learning on medical images (especially X-ray and CT of the lungs) provides a powerful tool to help medical experts in decision making. In this article, a new contribution is made to a stratification system with three severity levels (mild, moderate and severe) using a novel histogram database (which defines how the infection is in the different CT slices for a patient suffering from COVID-19). The first two phases use CNN Densenet-161 pre-trained models, and the last uses SVM with LDA supervised learning algorithms as classification models. The initial stage detects the presence of COVID-19 through X-ray multi-class (COVID-19 vs. No-Findings vs. Pneumonia) and the results obtained for accuracy, precision, recall, and F1-score values are 88%, 91%, 87%, and 89%, respectively. The following stage manifested the percentage of COVID-19 infection in the slices of the CT-scans for a patient and the results in the metrics evaluation are 0.95 in Pearson Correlation coefficient, 5.14 in MAE and 8.47 in RMSE. The last stage finally classifies a patient in three degrees of severity as a function of global infection of the lungs and the results achieved are 95% accurate.

Automated deep learning-based segmentation of COVID-19 lesions from chest computed tomography images.

PurposeThe novel coronavirus COVID-19, which spread globally in late December 2019, is a global health crisis. Chest computed tomography (CT) has played a pivotal role in providing useful information for clinicians to detect COVID-19. However, segmenting COVID-19-infected regions from chest CT results is challenging. Therefore, it is desirable to develop an efficient tool for automated segmentation of COVID-19 lesions using chest CT. Hence, we aimed to propose 2D deep-learning algorithms to automatically segment COVID-19-infected regions from chest CT slices and evaluate their performance.Material and methodsHerein, 3 known deep learning networks: U-Net, U-Net++, and Res-Unet, were trained from scratch for automated segmenting of COVID-19 lesions using chest CT images. The dataset consists of 20 labelled COVID-19 chest CT volumes. A total of 2112 images were used. The dataset was split into 80% for training and validation and 20% for testing the proposed models. Segmentation performance was assessed using Dice similarity coefficient, average symmetric surface distance (ASSD), mean absolute error (MAE), sensitivity, specificity, and precision.ResultsAll proposed models achieved good performance for COVID-19 lesion segmentation. Compared with Res-Unet, the U-Net and U-Net++ models provided better results, with a mean Dice value of 85.0%. Compared with all models, U-Net gained the highest segmentation performance, with 86.0% sensitivity and 2.22 mm ASSD. The U-Net model obtained 1%, 2%, and 0.66 mm improvement over the Res-Unet model in the Dice, sensitivity, and ASSD, respectively. Compared with Res-Unet, U-Net++ achieved 1%, 2%, 0.1 mm, and 0.23 mm improvement in the Dice, sensitivity, ASSD, and MAE, respectively.ConclusionsOur data indicated that the proposed models achieve an average Dice value greater than 84.0%. Two-dimensional deep learning models were able to accurately segment COVID-19 lesions from chest CT images, assisting the radiologists in faster screening and quantification of the lesion regions for further treatment. Nevertheless, further studies will be required to evaluate the clinical performance and robustness of the proposed models for COVID-19 semantic segmentation.

Feature selection using competitive coevolution of bio-inspired algorithms for the diagnosis of pulmonary emphysema

A Computer-Aided Diagnosis (CAD) system to assist a radiologist for diagnosing pulmonary emphysema from chest Computed Tomography (CT) slices has been developed. The lung tissues are segmented from the chest CT slices using Spatial Fuzzy C-Means (SFCM) clustering algorithm and the Regions of Interest (ROIs) are extracted using pixel-based segmentation. The ROIs considered for this work are pulmonary emphysematous lesions namely, centrilobular emphysema, paraseptal emphysema and sub-pleural bullae. The extracted ROIs are then validated and labelled by an expert radiologist. From each ROI, features with respect to shape, texture and run-length are extracted. A competitive coevolution model is proposed for Feature Selection (FS). The model makes use of two bio-inspired algorithms namely, Spider Monkey Optimization (SMO) algorithm and Paddy Field Algorithm (PFA) as its building blocks. FS is performed as a wrapper approach, using the bio-inspired algorithms namely, SMO and PFA with the accuracy of the Support Vector Machine (SVM) classifier as the fitness function. Ten-fold cross validation technique is used in training the SVM classifier using the selected features. The model is tested using two datasets: Real-time emphysema dataset and CT emphysema database (CTED) dataset. The accuracy, precision, recall and specificity obtained for both the datasets are (81.95%, 93.74%), (72.92%, 90.61%), (72.92%, 90.61%), (86.46%, 95.3%), which are better compared to the performance of SMO and PFA algorithms applied individually for FS and the CAD system without performing FS.

Deep learning for lung disease segmentation on CT: Which reconstruction kernel should be used?

PurposeThe purpose of this study was to determine whether a single reconstruction kernel or both high and low frequency kernels should be used for training deep learning models for the segmentation of diffuse lung disease on chest computed tomography (CT). Materials and methodsTwo annotated datasets of COVID-19 pneumonia (323,960 slices) and interstitial lung disease (ILD) (4,284 slices) were used. Annotated CT images were used to train a U-Net architecture to segment disease. All CT slices were reconstructed using both a lung kernel (LK) and a mediastinal kernel (MK). Three different trainings, resulting in three different models were compared for each disease: training on LK only, MK only or LK+MK images. Dice similarity scores (DSC) were compared using the Wilcoxon signed-rank test. ResultsModels only trained on LK images performed better on LK images than on MK images (median DSC = 0.62 [interquartile range (IQR): 0.54, 0.69] vs. 0.60 [IQR: 0.50, 0.70], P < 0.001 for COVID-19 and median DSC = 0.62 [IQR: 0.56, 0.69] vs. 0.50 [IQR 0.43, 0.57], P < 0.001 for ILD). Similarly, models only trained on MK images performed better on MK images (median DSC = 0.62 [IQR: 0.53, 0.68] vs. 0.54 [IQR: 0.47, 0.63], P < 0.001 for COVID-19 and 0.69 [IQR: 0.61, 0.73] vs. 0.63 [IQR: 0.53, 0.70], P < 0.001 for ILD). Models trained on both kernels performed better or similarly than those trained on only one kernel. For COVID-19, median DSC was 0.67 (IQR: =0.59, 0.73) when applied on LK images and 0.67 (IQR: 0.60, 0.74) when applied on MK images (P < 0.001 for both). For ILD, median DSC was 0.69 (IQR: 0.63, 0.73) when applied on LK images (P = 0.006) and 0.68 (IQR: 0.62, 0.72) when applied on MK images (P > 0.99). ConclusionReconstruction kernels impact the performance of deep learning-based models for lung disease segmentation. Training on both LK and MK images improves the performance.

CARes-UNet: Content-aware residual UNet for lesion segmentation of COVID-19 from chest CT images.

PurposeCoronavirus disease 2019 (COVID‐19) has caused a serious global health crisis. It has been proven that the deep learning method has great potential to assist doctors in diagnosing COVID‐19 by automatically segmenting the lesions in computed tomography (CT) slices. However, there are still several challenges restricting the application of these methods, including high variation in lesion characteristics and low contrast between lesion areas and healthy tissues. Moreover, the lack of high‐quality labeled samples and large number of patients lead to the urgency to develop a high accuracy model, which performs well not only under supervision but also with semi‐supervised methods.MethodsWe propose a content‐aware lung infection segmentation deep residual network (content‐aware residual UNet (CARes‐UNet)) to segment the lesion areas of COVID‐19 from the chest CT slices. In our CARes‐UNet, the residual connection was used in the convolutional block, which alleviated the degradation problem during the training. Then, the content‐aware upsampling modules were introduced to improve the performance of the model while reducing the computation cost. Moreover, to achieve faster convergence, an advanced optimizer named Ranger was utilized to update the model's parameters during training. Finally, we employed a semi‐supervised segmentation framework to deal with the problem of lacking pixel‐level labeled data.ResultsWe evaluated our approach using three public datasets with multiple metrics and compared its performance to several models. Our method outperforms other models in multiple indicators, for instance in terms of Dice coefficient on COVID‐SemiSeg Dataset, CARes‐UNet got the score 0.731, and semi‐CARes‐UNet further boosted it to 0.776. More ablation studies were done and validated the effectiveness of each key component of our proposed model.ConclusionsCompared with the existing neural network methods applied to the COVID‐19 lesion segmentation tasks, our CARes‐UNet can gain more accurate segmentation results, and semi‐CARes‐UNet can further improve it using semi‐supervised learning methods while presenting a possible way to solve the problem of lack of high‐quality annotated samples. Our CARes‐UNet and semi‐CARes‐UNet can be used in artificial intelligence‐empowered computer‐aided diagnosis system to improve diagnostic accuracy in this ongoing COVID‐19 pandemic.

Computer-Aided Diagnosis System for Diagnosis of Cavitary and Miliary Tuberculosis Using Improved Artificial Bee Colony Optimization

A framework for Computer-Aided Diagnosis (CAD) to diagnose Cavitary TB and Miliary TB from chest Computed Tomography (CT) slices has been designed and implemented. The lung tissues from the CT slices are segmented using region-based Active Contour Model (ACM) and the Region of Interests (ROIs) labelled by an expert radiologist are extracted. Features based on shape and texture are extracted from each ROI. A wrapper-based Improved Artificial Bee Colony Optimization (I-ABCO) algorithm with the accuracy of the Support Vector Machine (SVM) classifier as the fitness function is used to select the optimal subset of features. The search process of I-ABCO is improved using two evaluation functions, namely, rough dependency measure (RDM) and mutual information (MI), to promote better exploitation of the search space. The selected features are used to train the Radial Basis Function Neural Network (RBFNN) classifier, using ten-fold cross validation and the performance is evaluated. Experimentation has been performed on CT slices using two datasets: Tuberculosis dataset and Lung Image Database Consortium-Image Database Resource Initiative (LIDC-IDRI) dataset. The accuracy of the CAD systems using I-ABCO with MI and I-ABCO with RDM for both datasets are (88.34%, 92.63%) and (87.32%, 90.17%), respectively. The CAD system using the basic Artificial Bee Colony Optimization (ABCO) algorithm for feature selection is also experimented with the same datasets and an accuracy of (85.68%, 88%) is achieved. The performance of the proposed algorithms outperforms the ABCO algorithm in diagnostic accuracy, which ensures that selecting the optimal feature subset efficiently has an impact on the classification accuracy.

Dual attention multiple instance learning with unsupervised complementary loss for COVID-19 screening.

Chest computed tomography (CT) based analysis and diagnosis of the Coronavirus Disease 2019 (COVID-19) plays a key role in combating the outbreak of the pandemic that has rapidly spread worldwide. To date, the disease has infected more than 18 million people with over 690k deaths reported. Reverse transcription polymerase chain reaction (RT-PCR) is the current gold standard for clinical diagnosis but may produce false positives; thus, chest CT based diagnosis is considered more viable. However, accurate screening is challenging due to the difficulty in annotation of infected areas, curation of large datasets, and the slight discrepancies between COVID-19 and other viral pneumonia. In this study, we propose an attention-based end-to-end weakly supervised framework for the rapid diagnosis of COVID-19 and bacterial pneumonia based on multiple instance learning (MIL). We further incorporate unsupervised contrastive learning for improved accuracy with attention applied both in spatial and latent contexts, herein we propose Dual Attention Contrastive based MIL (DA-CMIL). DA-CMIL takes as input several patient CT slices (considered as bag of instances) and outputs a single label. Attention based pooling is applied to implicitly select key slices in the latent space, whereas spatial attention learns slice spatial context for interpretable diagnosis. A contrastive loss is applied at the instance level to encode similarity of features from the same patient against representative pooled patient features. Empirical results show that our algorithm achieves an overall accuracy of 98.6% and an AUC of 98.4%. Moreover, ablation studies show the benefit of contrastive learning with MIL.

Lung Lesion Localization of COVID-19 From Chest CT Image: A Novel Weakly Supervised Learning Method.

Chest computed tomography (CT) image data is necessary for early diagnosis, treatment, and prognosis of Coronavirus Disease 2019 (COVID-19). Artificial intelligence has been tried to help clinicians in improving the diagnostic accuracy and working efficiency of CT. Whereas, existing supervised approaches on CT image of COVID-19 pneumonia require voxel-based annotations for training, which take a lot of time and effort. This paper proposed a weakly-supervised method for COVID-19 lesion localization based on generative adversarial network (GAN) with image-level labels only. We first introduced a GAN-based framework to generate normal-looking CT slices from CT slices with COVID-19 lesions. We then developed a novel feature match strategy to improve the reality of generated images by guiding the generator to capture the complex texture of chest CT images. Finally, the localization map of lesions can be easily obtained by subtracting the output image from its corresponding input image. By adding a classifier branch to the GAN-based framework to classify localization maps, we can further develop a diagnosis system with improved classification accuracy. Three CT datasets from hospitals of Sao Paulo, Italian Society of Medical and Interventional Radiology, and China Medical University about COVID-19 were collected in this article for evaluation. Our weakly supervised learning method obtained AUC of 0.883, dice coefficient of 0.575, accuracy of 0.884, sensitivity of 0.647, specificity of 0.929, and F1-score of 0.640, which exceeded other widely used weakly supervised object localization methods by a significant margin. We also compared the proposed method with fully supervised learning methods in COVID-19 lesion segmentation task, the proposed weakly supervised method still leads to a competitive result with dice coefficient of 0.575. Furthermore, we also analyzed the association between illness severity and visual score, we found that the common severity cohort had the largest sample size as well as the highest visual score which suggests our method can help rapid diagnosis of COVID-19 patients, especially in massive common severity cohort. In conclusion, we proposed this novel method can serve as an accurate and efficient tool to alleviate the bottleneck of expert annotation cost and advance the progress of computer-aided COVID-19 diagnosis.

Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning

Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images.

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.

Computer-Aided Diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms

Pulmonary emphysema is a condition characterized by the destruction and permanent enlargement of the alveoli of the lungs. The destruction of gas-exchanging alveoli causes shortness of breath followed by a chronic cough and sputum production. A Computer-Aided Diagnosis (CAD) framework for diagnosing pulmonary emphysema from chest Computed Tomography (CT) slices has been designed and implemented in this study. The process of implementing the CAD framework includes segmenting the lung tissues and extracting the regions of interest (ROIs) using the Spatial Intuitionistic Fuzzy C-Means clustering algorithm. The ROIs that were considered in this work were emphysematous lesions — namely, centrilobular, paraseptal, and bullae that were labelled by an expert radiologist. The shape, texture, and run-length features were extracted from each ROI. A wrapper approach that employed four bio-inspired algorithms — namely, Moth–Flame Optimization (MFO), Firefly Optimization (FFO), Artificial Bee Colony Optimization, and Ant Colony Optimization — with the accuracy of the support vector machine classifier as the fitness function was used to select the optimal feature subset. The selected features of each bio-inspired algorithm were trained independently using the Extreme Learning Machine classifier based on the tenfold cross-validation technique. The framework was tested on real-time and public emphysema datasets to perform binary classification of lung CT slices of patients with and without the presence of emphysema. The framework that used MFO and FFO for feature selection produced superior results regarding accuracy, precision, recall, and specificity for the real-time dataset and the public dataset, respectively, when compared to the other bio-inspired algorithms.

Stepwise Diagnosis and Dynamic Change&nbsp;Prediction AI System for COVID-19

Background: As the COVID-19 pandemic continues to spread worldwide, there is still no accurate rapid diagnostic test for COVID-19, or monitoring tool for patient’s clinical course. An artificial-intelligence system (CoviDet) was therefore developed and applied on chest computed tomography (CT), to evaluate its application for rapid diagnosis and potential monitoring of COVID-19 patients. Methods: 1,201,074 CT slices from 2527 patients were grouped into 3 main groups: COVID-19 positive group, non-COVID-19 viral pneumonia group and control group. They were used to train and validate CoviDet novel stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. A subset of COVID-19 patients with more than 3 consecutive CT images were selected for training and validation of the auto-segmentation and monitoring algorithm. Findings: CoviDet outperforms radiologists, and can diagnose COVID-19 based on CT alone with sensitivity of 0.93, specificity of 0.95 and AUC of 0.98 (95%CI 0.97-0.99; P<0.001). Its auto-segmentation analyses co-related well with those by radiologists, with a Dice's coefficient of 0.77. Its can generate a predictive curve of patient’s clinical course if serial CT assessments are available. Interpretation: CoviDet is a useful aid for radiologist to diagnose COVID-19 rapidly and accurately based on CT, with or without clinical data. It can produce an objective predictive curve of patient’s clinical course for easy visualisation. In principle, CoviDet could be used on a web-based platform with built auto masking of privacy data, whereby clinicians can upload anonymised CT for COVID-19 analysis without sensitive clinical information, from anywhere in the world. Funding Statement: This study is supported by High-level university construction project of Guangzhou medical university (Grant No. 20182737, 201721007, 201715907, 2017160107); the Guangdong high level hospital construction reaching peak plan. Declaration of Interests: Qiang Du is the Founder and CEO of Beijing XiaoBaiShiJi Network Technical Co. Ltd. All other authors declare no competing interests. Ethics Approval Statement: This study is approved by the ethics committee of the First Affiliated Hospital of Guangzhou Medical University.

Quantitative Computed Tomography Measurement of Tracheal Cross-Sectional Areas in Relapsing Polychondritis: Correlations with Spirometric Values

Background: Although tracheal stenosis occurs in relapsing polychondritis (RP), no studies exist that have clarified correlations between quantitative airway measurement and spirometry in RP patients. Objectives: The aim of this study was to investigate correlations between the cross-sectional area (CSA) of the trachea and spirometric values in patients with RP. Methods: The institutional review board approved this retrospective study, and written informed consent was waived. Twenty-six patients with RP underwent spirometry and chest computed tomography (CT) at full inspiration and end-expiration. On inspiratory and expiratory chest CT images, CSA at the intrathoracic trachea was measured for all CT slices, and the mean and minimum tracheal CSA were obtained. Correlations between the tracheal CSA and spirometric values were assessed by Spearman's rank correlation analysis. Results: Tracheal CSA measurements for inspiratory and expiratory scans were significantly correlated with FEV₁, FEV_25-75%, and peak flow values (ρ = 0.51-0.86, p < 0.01). During each inspiratory or expiratory phase, the minimum tracheal CSA achieved a higher correlation coefficient with spirometric values than the mean CSA. Conclusion: Tracheal dimensions for both inspiratory and expiratory CT are significant predictors of pulmonary function in patients with RP. The narrowest tracheal dimension likely determines the severity of airflow limitation in RP.

Quantitative Analysis for Breast Density Estimation in Low Dose Chest CT Scans

A computational method was developed for the measurement of breast density using chest computed tomography (CT) images and the correlation between that and mammographic density. Sixty-nine asymptomatic Asian women (138 breasts) were studied. With the marked lung area and pectoralis muscle line in a template slice, demons algorithm was applied to the consecutive CT slices for automatically generating the defined breast area. The breast area was then analyzed using fuzzy c-mean clustering to separate fibroglandular tissue from fat tissues. The fibroglandular clusters obtained from all CT slices were summed then divided by the summation of the total breast area to calculate the percent density for CT. The results were compared with the density estimated from mammographic images. For CT breast density, the coefficient of variations of intraoperator and interoperator measurement were 3.00 % (0.59 %-8.52 %) and 3.09 % (0.20 %-6.98 %), respectively. Breast density measured from CT (22 ± 0.6 %) was lower than that of mammography (34 ± 1.9 %) with Pearson correlation coefficient of r=0.88. The results suggested that breast density measured from chest CT images correlated well with that from mammography. Reproducible 3D information on breast density can be obtained with the proposed CT-based quantification methods.