PHT-bot: Deep-Learning based system for automatic risk stratification of COPD patients based upon signs of Pulmonary Hypertension

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality worldwide. Identifying those at highest risk of deterioration would allow more effective distribution of preventative and surveillance resources. Secondary pulmonary hypertension is a manifestation of advanced COPD, which can be reliably diagnosed by the main Pulmonary Artery (PA) to Ascending Aorta (Ao) ratio. In effect, a PA diameter to Ao diameter ratio of greater than 1 has been demonstrated to be a reliable marker of increased pulmonary arterial pressure. Although clinically valuable and readily visualized, the manual assessment of the PA and the Ao diameters is time consuming and under-reported. The present study describes a non invasive method to measure the diameters of both the Ao and the PA from contrast-enhanced chest Computed Tomography (CT). The solution applies deep learning techniques in order to select the correct axial slice to measure, and to segment both arteries. The system achieves test Pearson correlation coefficient scores of 93% for the Ao and 92% for the PA. To the best of our knowledge, it is the first such fully automated solution

Automation Process for Morphometric Analysis of Volumetric CT Data from Pulmonary Vasculature in Rats

With advances in medical imaging scanners, it has become commonplace to generate large multidimensional datasets. These datasets require tools for a rapid, thorough analysis. To address this need, we have developed an automated algorithm for morphometric analysis incorporating A Visualization Workshop computational and image processing libraries for three-dimensional segmentation, vascular tree generation and structural hierarchical ordering with a two-stage numeric optimization procedure for estimating vessel diameters. We combine this new technique with our mathematical models of pulmonary vascular morphology to quantify structural and functional attributes of lung arterial trees. Our physiological studies require repeated measurements of vascular structure to determine differences in vessel biomechanical properties between animal models of pulmonary disease. Automation provides many advantages including significantly improved speed and minimized operator interaction and biasing. The results are validated by comparison with previously published rat pulmonary arterial micro-CT data analysis techniques, in which vessels were manually mapped and measured using intense operator intervention

Automatic Detection of Pulmonary Embolism in CTA Images Using Machine Learning

In this study, a novel computer-aided detection (CAD) method is introduced to detect pulmonary embolism (PE) in computed tomography angiography (CTA) images. This method consists of lung vessel segmentation, PE candidate detection, feature extraction, feature selection and classification of PE. PE candidates are determined in lung vessel tree. Then, feature extraction is carried out based on morphological properties of PEs. Stepwise feature selection method is used to find the best set of the features. Artificial neural network (ANN), k-nearest neighbours (KNN) and support vector machines (SVM) are used as classifiers. The CAD system is evaluated for 33 CTA datasets with 10 fold cross-validation. The sensitivities of these classifiers are obtained as 98.3 %, 57.3 % and 73 % at 10.2, 5.7 and 8.2 false positives per dataset respectively

SEMIAUTOMATIC DETECTION OF STENOSIS AND OCCLUSION OF PULMONARY ARTERIES FOR PATIENTS WITH CHRONIC THROMBOEMBOLIC PULMONARY HYPERTENSION

Chronic thromboembolic pulmonary hypertension (CTEPH) is a severe lung disease defined by the presence of chronic blood clots in the pulmonary arteries accompanied by severe health complications. It is necessary to go through a large set of axial sections from Computed tomography pulmonary angiogram (CTPA) for diagnosing the disease, which is difficult and time consuming for the radiologist. The radiologist's experience plays a significant role, same as subjective factors such as attention and fatigue. In this work we pursued the design and development of the algorithm for semiautomatic detection of pulmonary artery stenoses and clots for diagnosing CTEPH, which is based on the implementation of semantic segmentation using deep convolutional neural networks. Specifically, it is about the use of the DeepLab V3 + model embedded in the Xception architecture. Within this work we focused on stenoses and clots located in larger pulmonary arteries. Anonymized data of patients diagnosed with CTEPH and one healthy patient in the term of the presence of the disease were used for realization of this work. Statistical analysis of the results is divided into two parts: analysis of the created algorithm based on comparison of outputs with ground truth data (manually marked references) and analysis of pathology detection on new data based on comparison of predictions with reference images from the radiologist. The proposed algorithm correctly detects present vascular pathology in 83% of cases (sensitivity) and precisely selects cases where the investigated pathology does not occur in 72% of cases (specificity). The calculated Matthews correlation coefficient is 0.53. This means that the predictive ability of the algorithm is moderate positive. The designed and developed image analysis algorithm offers the radiologist a "second opinion" and it also could enable to increase the sensitivity of CTEPH diagnostics in cooperation with a radiologist.