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

Segmentation of aorta and main pulmonary artery of non-contrast CT images using U-Net for chronic thromboembolic pulmonary hypertension : evaluation of robustness to contacts with blood vessels

Enlargement of the pulmonary artery is a morphological abnormality of pulmonary hypertension patients. Diameters of the aorta and main pulmonary artery (MPA) are useful for predicting the presence of pulmonary hypertension. A major problem in the automatic segmentation of the aorta and MPA from non-contrast CT images is the invisible boundary caused by contact with blood vessels. In this study, we applied U-Net to the segmentation of the aorta and MPA from non-contrast CT images for normal and chronic thromboembolic pulmonary hypertension (CTEPH) cases and evaluated the robustness to the contacts between blood vessels. Our approach of the segmentation consists of three steps: (1) detection of trachea branch point, (2) cropping region of interest centered to the trachea branch point, and (3) segmentation of the aorta and MPA using U-Net. The segmentation performances were compared in seven methods: 2D U-Net, 2D U-Net with pre-trained VGG-16 encoder, 2D U-Net with pre-trained VGG-19 encoder, 2D Attention U-Net, 3D U-Net, an ensemble method of them, and our conventional method. The aorta and MPA segmentation methods using these U-Net achieved higher performance than a conventional method. Although the contact boundaries of blood vessels caused lower performance compared with the non-contact boundaries, the mean boundary distances were below about one pixel

Automated Assessment of Aortic and Main Pulmonary Arterial Diameters using Model-Based Blood Vessel Segmentation for Predicting Chronic Thromboembolic Pulmonary Hypertension in Low-Dose CT Lung Screening

Chronic thromboembolic pulmonary hypertension (CTEPH) is characterized by obstruction of the pulmonary vasculature by residual organized thrombi. A morphological abnormality inside mediastinum of CTEPH patient is enlargement of pulmonary artery. This paper presents an automated assessment of aortic and main pulmonary arterial diameters for predicting CTEPH in low-dose CT lung screening. The distinctive feature of our method is to segment aorta and main pulmonary artery using both of prior probability and vascular direction which were estimated from mediastinal vascular region using principal curvatures of four-dimensional hyper surface. The method was applied to two datasets, 64 low-dose CT scans of lung cancer screening and 19 normal-dose CT scans of CTEPH patients through the training phase with 121 low-dose CT scans. This paper demonstrates effectiveness of our method for predicting CTEPH in low-dose CT screening

Clin Respir J

Rationale:Occupational exposures at the WTC site after September 11, 2001 have been associated with several presumably inflammatory lower airway diseases. Pulmonary arterial enlargement, as suggested by an increased ratio of the diameter of the pulmonary artery to the diameter of the aorta (PAAr) has been reported as a computed tomographic (CT) scan marker of adverse respiratory health outcomes, including WTC-related disease. In this study, we sought to utilize a novel quantitative CT (QCT) measurement of PAAr to test the hypothesis that an increased ratio is associated with FEV1 below each subject\u2019s statistically determined lower limit of normal (FEV1<LLN).Methods:In a group of 1,180 WTC workers and volunteers, we examined whether FEV1<LLN was associated with an increased QCT-measured PAAr, adjusting for previously identified important covariates.Results:Unadjusted analyses showed a statistically significant association of FEV1<LLN with PAAr (35.3% vs. 24.7%, p=0.0001), as well as with height, body mass index, early arrival at the WTC disaster site, shorter WTC exposure duration, posttraumatic stress disorder checklist (PCL) score, wall area percent, and evidence of bronchodilator response. The multivariate logistic regression model confirmed the association of FEV1<LLN with PAAr (OR 1.63, 95% CI 1.21, 2.20, p=0.0015) and all the unadjusted associations, except for PCL score.Conclusions:In WTC workers, FEV1<LLN is associated with elevated PAAr which, although likely multifactorial, may be related to distal vasculopathy, as has been hypothesized for chronic obstructive pulmonary disease.Trial registration:ClinicalTrials.gov identifier .20192020-10-01T00:00:00Z200-2017-93325/CDC/NIOSH/U01 OH011300/OH/NIOSH CDC HHS/United StatesU01 OH011697/OH/NIOSH CDC HHS/United StatesU01 OH010401/OH/NIOSH CDC HHS/United StatesR01 HL119326/HL/NHLBI NIH HHS/United States31347281PMC6783324859