Growth of the thoracic aorta in the smoking population: The Danish Lung Cancer Screening Trial

Background: Although the descending aortic diameter is larger in smokers, data about thoracic aortic growth is missing. Our aim is to present the distribution of thoracic aortic growth in smokers and to compare it with literature of the general population. Methods: Current and ex-smokers aged 50–70 years from the longitudinal Danish Lung Cancer Screening Trial, were included. Mean and 95th percentile of annual aortic growth of the ascending aortic (AA) and descending aortic (DA) diameters were calculated with the first and last non-contrast computed tomography scans during follow-up. Determinants of change in aortic diameter over time were investigated with linear mixed models. Results: A total of 1987 participants (56% male, mean age 57.4 ± 4.8 years) were included. During a median follow-up of 48 months, mean AA and DA growth rates were comparable between males (AA 0.12 ± 0.31 mm/year and DA 0.10 ± 0.30 mm/year) and females (AA 0.11 ± 0.29 mm/year and DA 0.13 ± 0.27 mm/year). The 95th percentile ranged from 0.42 to 0.47 mm/year, depending on sex and location. Aortic growth was comparable between current and ex-smokers and aortic growth was not associated with pack-years. Our findings are consistent with aortic growth rates of 0.08 to 0.17 mm/years in the general population. Larger aortic growth was associated with lower age, increased height, absence of medication for hypertension or hypercholesterolemia and lower Agatston s

An end-to-end approach to segmentation in medical images with CNN and posterior-CRF

Conditional Random Fields (CRFs) are often used to improve the output of an initial segmentation model, such as a convolutional neural network (CNN). Conventional CRF approaches in medical imaging use manually defined features, such as intensity to improve appearance similarity or location to improve spatial coherence. These features work well for some tasks, but can fail for others. For example, in medical image segmentation applications where different anatomical structures can have similar intensity values, an intensity-based CRF may produce incorrect results. As an alternative, we propose Posterior-CRF, an end-to-end segmentation method that uses CNN-learned features in a CRF and optimizes the CRF and CNN parameters concurrently. We validate our method on three medical image segmentation tasks: aorta and pulmonary artery segmentation in non-contrast CT, white matter hyperintensities segmentation in multi-modal MRI, and ischemic stroke lesion segmentation in multi-modal MRI. We compare this with the state-of-the-art CNN-CRF methods. In all applications, our proposed method outperforms the existing methods in terms of Dice coefficient, average volume difference, and lesion-wise F1 score

Assessment of fully automatic segmentation of pulmonary artery and aorta on noncontrast CT with optimal surface graph cuts

Purpose: Accurate segmentation of the pulmonary arteries and aorta is important due to the association of the diameter and the shape of these vessels with several cardiovascular diseases and with the risk of exacerbations and death in patients with chronic obstructive pulmonary disease. We propose a fully automatic method based on an optimal surface graph-cut algorithm to quantify the full 3D shape and the diameters of the pulmonary arteries and aorta in noncontrast computed tomography (CT) scans. Methods: The proposed algorithm first extracts seed points in the right and left pulmonary arteries, the pulmonary trunk, and the ascending and descending aorta by using multi-atlas registration. Subsequently, the centerlines of the pulmonary arteries and aorta are extracted by a minimum cost path tracking between the extracted seed points, with a cost based on a combination of lumen intensity similarity and multiscale medialness in three planes. The centerlines are refined by applying the path tracking algorithm to curved multiplanar reformatted scans and are then smoothed and dilated nonuniformly according to the extracted local vessel radius from the medialness filter. The resulting coarse estimates of the vessels are used as initialization for a graph-cut segmentation. Once the vessels are segmented, the diameters of the pulmonary artery (PA) and the ascending aorta (AA) and the (Formula presented.) ratio are automatically calculated both in a single axial slice and in a 10 mm volume around the automatically extracted PA bifurcation level. The method is evaluated on noncontrast CT scans from the Danish Lung Cancer Screening Trial (DLCST). Segmentation accuracy is determined by comparing with manual annotations on 25 CT scans. Intraclass correlation (ICC) between manual and automatic diameters, both measured in axial slices at the PA bifurcation level, is computed on an additional 200 CT scans. Repeatability of the automated 3D volumetric diameter and (Formula presented.) ratio calculations (perpendicular to the vessel axis) are evaluated on 118 scan–rescan pairs with an average in-between time of 3 months. Results: We obtained a Dice segmentation overlap of 0.94 ± 0.02 for pulmonary arteries and 0.96 ± 0.01 for the aorta, with a mean surface distance of 0.62 ± 0.33 mm and 0.43 ± 0.07 mm, respectively. ICC between manual and automatic in-slice diameter measures was 0.92 for PA, 0.97 for AA, and 0.90 for the (Formula presented.) ratio, and for automatic diameters in 3D volumes around the PA bifurcation level between scan and rescan was 0.89, 0.95, and 0.86, respectively. Conclusion: The proposed automatic segmentation method can reliably extract diameters of the large arteries in non-ECG-gated noncontrast CT scans such as are acquired in lung cancer screening