Automatic detection of coronaries ostia in computed tomography angiography volume data

Background: Heart coronaries emerge from the ascending aorta lateral sides from two points called the coronaries ostia. To automatically segment the heart coronaries; there must be a starting point (seed) for the segmentation. In this paper we present a fully automatic approach to segment the coronaries ostia towards automatic seeding for heart coronaries segmentation.Methods: Our algorithm takes as an input a CTA volume of segmented aorta cross sections that represents our region of interest. Then the ostia detection algorithm traverses that volume looking for the ostia points in an automatic fashion. The proposed algorithm depends on the anatomical features of the ostia. The main anatomic feature of the ostia is that it appears like a curvature or corner on the segmented ascending aorta cross section. Therefore we adopted in our methodology a modified version of Harris Corner Detection; besides inducing some anatomical features of the ostia location with respect to the aortic valve.Results: The proposed algorithm is tested and validated on the computed tomography angiography database provided by the Rotterdam coronary artery algorithm evaluation framework. The proposed automatic ostia detection algorithm succeeded to detect both ostia points in all the test cases. Also, the detected ostia points’ coordinates are validated versus a ground truth provided by the same framework with deviation between the results of the detection process and the ground truth having a min of 0 pixels and a max of 10 pixels for all test cases.Conclusions: Thus the proposed algorithm gives accurate results in comparison with the ground truth, which proves the efficiency of the proposed algorithm and its applicability to be extended as a seed for heart coronaries segmentation

Sentiment-Based Spatiotemporal Prediction Framework for Pandemic Outbreaks Awareness Using Social Networks Data Classification

According to the World Health Organization, several factors have affected the accurate reporting of SARS-CoV-2 outbreak status, such as limited data collection resources, cultural and educational diversity, and inconsistent outbreak reporting from different sectors. Driven by this challenging situation, this study investigates the potential expediency of using social network data to develop reliable early information surveillance and warning system for pandemic outbreaks. As such, an enhanced framework of three inherently interlinked subsystems is proposed. The first subsystem includes data collection and integration mechanisms, data preprocessing, and hybrid sentiment analysis tools to identify tweet sentiment taxonomies and quantitatively estimate public awareness. The second subsystem comprises the feature extraction unit that identifies, selects, embeds, and balances feature vectors and the classifier fitting and training unit. This subsystem is designed to capture the most effective linguistic feature combinations with more spatial evidence by using a variety of approaches, including linear classifiers, MLPs, RNNs, and CNNs, as well as pre-trained word embedding algorithms. The last is the modeling and situational awareness evaluation subsystem, which measures temporal associations between pandemic-relevant social network activities and officially announced infection counts in the most hazardous geolocations. The proposed framework was developed and tested using a combination of static datasets and real-time scraped Twitter data. The results of these experiments showed the remarkable performance of the framework in assessing the temporal associations between public awareness and outbreak status. It also showed that the Decision Tree Classifier with Unigram+TF-IDF feature vectors outperformed other conventional models for sentiment classification and geolocation classification with an accuracy of 94.3% and 80.8, respectively. As indicated, conventional machine learning algorithms didn’t achieve a precision of more than 80%, while, for instance, MLP with self-embedding layer, Word2Vec, and GloVe pre-trained word embedding resulted in very poor accuracy of 10%, 36%, and 32%, respectively. However, adding the PoS tag one-hot encoding embedding increased the validation accuracy from 36% to approximately 89%, while the best performance for the second subsystem was achieved by Bi-LSTM with RoBERTa word embedding, with an accuracy of 96%. The achieved results reveal that the proposed framework can proactively capture the potential hazards associated with the prevalence of infectious diseases as an effective early detection and info-surveillance awareness system

Automatic detection of coronaries ostia in computed tomography angiography volume data

Background: Heart coronaries emerge from the ascending aorta lateral sides from two points called the coronaries ostia. To automatically segment the heart coronaries; there must be a starting point (seed) for the segmentation. In this paper we present a fully automatic approach to segment the coronaries ostia towards automatic seeding for heart coronaries segmentation.Methods: Our algorithm takes as an input a CTA volume of segmented aorta cross sections that represents our region of interest. Then the ostia detection algorithm traverses that volume looking for the ostia points in an automatic fashion. The proposed algorithm depends on the anatomical features of the ostia. The main anatomic feature of the ostia is that it appears like a curvature or corner on the segmented ascending aorta cross section. Therefore we adopted in our methodology a modified version of Harris Corner Detection; besides inducing some anatomical features of the ostia location with respect to the aortic valve.Results: The proposed algorithm is tested and validated on the computed tomography angiography database provided by the Rotterdam coronary artery algorithm evaluation framework. The proposed automatic ostia detection algorithm succeeded to detect both ostia points in all the test cases. Also, the detected ostia points’ coordinates are validated versus a ground truth provided by the same framework with deviation between the results of the detection process and the ground truth having a min of 0 pixels and a max of 10 pixels for all test cases.Conclusions: Thus the proposed algorithm gives accurate results in comparison with the ground truth, which proves the efficiency of the proposed algorithm and its applicability to be extended as a seed for heart coronaries segmentation