Recent studies show that pulmonary vascular diseases may specifically affect arteries or veins through different physiologic mechanisms. To detect changes in the two vascular trees, physicians manually analyze the chest computed tomography (CT) image of the patients in search of abnormalities. This process is time-consuming, difficult to standardize and thus not feasible for large clinical studies or useful in real-world clinical decision making. Therefore, automatic separation of arteries and veins in CT images is becoming of great interest, as it may help physicians accurately diagnose pathological conditions. In this work, we present a novel, fully automatic approach to classifying vessels from chest CT images into arteries and veins. The algorithm follows three main steps: first, a scale-space particles segmentation to isolate vessels; then a 3D convolutional neural network (CNN) to obtain a first classification of vessels; finally, graph-cuts (GC) optimization to refine the results. To justify the usage of the proposed CNN architecture, we compared different 2D and 3D CNNs that may use local information from bronchus- and vessel-enhanced images provided to the network with different strategies. We also compared the proposed CNN approach with a Random Forests (RF) classifier. The methodology was trained and evaluated on the superior and inferior lobes of the right lung of eighteen clinical cases with non-contrast chest CT scans, in comparison with manual classification. The proposed algorithm achieves an overall accuracy of 94%, which is higher than the accuracy obtained using other CNN architectures and RF. Our method was also validated with contrast-enhanced CT scans of patients with Chronic Thromboembolic Pulmonary Hypertension (CTEPH) to demonstrate that our model generalizes well to contrast-enhanced modalities. The proposed method outperforms state-of-the-art methods, paving the way for future use of 3D CNN for A/V classification in CT images.

No abstract

BackgroundPrior studies of clinical prognostication in idiopathic pulmonary fibrosis (IPF) using computed tomography (CT) have often used subjective analyses or have evaluated quantitative measures in isolation. This study examined associations between both densitometric and local histogram based quantitative CT measurements with pulmonary function test (PFT) parameters and mortality. In addition, this study sought to compare risk prediction scores that incorporate quantitative CT measures with previously described systems.MethodsForty six patients with biopsy proven IPF were identified from a registry of patients with interstitial lung disease at Brigham and Women’s Hospital in Boston, MA. CT scans for each subject were visually scored using a previously published method. After a semi-automated method was used to segment the lungs from the surrounding tissue, densitometric measurements including the percent high attenuating area, mean lung density, skewness and kurtosis were made for the entirety of each patient’s lungs. A separate, automated tool was used to detect and quantify the percent of lung occupied by interstitial lung features. These analyses were used to create clinical and quantitative CT based risk prediction scores, and the performance of these was compared to the performance of clinical and visual analysis based methods.ResultsAll of the densitometric measures were correlated with forced vital capacity and diffusing capacity, as were the total amount of interstitial change and the percentage of interstitial change that was honeycombing measured using the local histogram method. Higher percent high attenuating area, higher mean lung density, lower skewness, lower kurtosis and a higher percentage of honeycombing were associated with worse transplant free survival. The quantitative CT based risk prediction scores performed similarly to the clinical and visual analysis based methods.ConclusionsBoth densitometric and feature based quantitative CT measures correlate with pulmonary function test measures and are associated with transplant free survival. These objective measures may be useful for identifying high risk patients and monitoring disease progression. Further work will be needed to validate these measures and the quantitative imaging based risk prediction scores in other cohorts.Electronic supplementary materialThe online version of this article (doi:10.1186/s12931-017-0527-8) contains supplementary material, which is available to authorized users.

Thirty-two patients suffering from migraine without aura were assessed during in interictal period to evaluate the contribution of cytokines to the pathophysiology of migraine. To this end, plasma levels of IFN-gamma, IL-4, IL-5, and IL-10 were measured by enzyme-linked immunosorbent assay (ELISA) techniques. Plasma levels of both IFN-gamma and IL-10 were not increased in the patients and did not differ significantly from healthy controls. Of interest, we observed a strong increase of IL-5 levels in 84.3% as well as increased IL-4 levels in 37.5% of patients with migraine without aura. These results suggests a preferential enhancement of some Th2-type cytokines, and may support the growing arguments of an immunoallergic mechanism in the pathophysiology of migraine.