@article{Klooster2020,
title = {Multifocal cardiovascular calcification in patients with established cardiovascular disease; prevalence, risk factors, and relation with recurrent cardiovascular disease},
author = {C.C. van 't Klooster, H.M. Nathoe, J .Hjortnaes, M.L. Bots, I. Išgum, N. Lessmann, Y. van der Graaf, T. Leiner, F.L.J. Visseren, On behalf of the UCC-SMART-study group},
doi = {10.1016/j.ijcha.2020.100499},
year = {2020},
date = {2020-04-20},
journal = {International Journal of Cardiology, Heart & Vasculature},
volume = {27},
number = {100499},
abstract = {Aims
The aim is to investigate (multifocal) cardiovascular calcification in patients with established cardiovascular disease (CVD), regarding prevalence, risk factors, and relation with recurrent CVD or vascular interventions. Coronary artery calcification (CAC), thoracic aortic calcification (TAC) (including ascending aorta, aortic arch, descending aorta), mitral annular calcification (MAC), and aortic valve calcification (AVC) are studied.

Methods
The study concerned 568 patients with established CVD enrolled in the ORACLE cohort. All patients underwent computed tomography. Prevalence of site-specific and multifocal calcification was determined. Ordinal regression analyses were performed to quantify associations of risk factors with cardiovascular calcification, and Cox regression analyses to determine the relation between calcium scores and recurrent CVD or vascular interventions.

Results
Calcification was multifocal in 76% (N = 380) of patients with calcification. Age (per SD) was associated with calcification at all locations (lowest OR 2.17; 99%CI 1.54–3.11 for ascending aorta calcification). Diabetes mellitus and systolic blood pressure were associated with TAC, whereas male sex was a determinant of CAC. TAC and CAC were related to the combined endpoint CVD or vascular intervention (N = 68). In a model with all calcium scores combined, only CAC was related to the combined outcome (HR 1.39; 95%CI 1.15–1.68).

Conclusion
Cardiovascular calcification is generally multifocal in patients with established CVD. Differences in associations between risk factors and calcification at various anatomical locations stress the divergence in pathophysiological pathways. CAC is most strongly related to recurrent CVD or vascular interventions independent of traditional risk factors, and independent of heart valve and thoracic aorta calcification.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Lessmann2019,
title = {Iterative fully convolutional neural networks for automatic vertebra segmentation and identification},
author = {N. Lessmann, B. van Ginneken, P.A. de Jong, I. Išgum},
url = {https://arxiv.org/abs/1804.04383},
year = {2019},
date = {2019-02-12},
journal = {Medical Image Analysis},
volume = {53},
pages = {142-155},
abstract = {Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities. Most dedicated spine CT and MR scans as well as scans of the chest, abdomen or neck cover only part of the spine. Segmentation and identification should therefore not rely on the visibility of certain vertebrae or a certain number of vertebrae. We propose an iterative instance segmentation approach that uses a fully convolutional neural network to segment and label vertebrae one after the other, independently of the number of visible vertebrae. This instance-by-instance segmentation is enabled by combining the network with a memory component that retains information about already segmented vertebrae. The network iteratively analyzes image patches, using information from both image and memory to search for the next vertebra. To efficiently traverse the image, we include the prior knowledge that the vertebrae are always located next to each other, which is used to follow the vertebral column. The network concurrently performs multiple tasks, which are segmentation of a vertebra, regression of its anatomical label and prediction whether the vertebra is completely visible in the image, which allows to exclude incompletely visible vertebrae from further analyses. The predicted anatomical labels of the individual vertebrae are additionally refined with a maximum likelihood approach, choosing the overall most likely labeling if all detected vertebrae are taken into account. This method was evaluated with five diverse datasets, including multiple modalities (CT and MR), various fields of view and coverages of different sections of the spine, and a particularly challenging set of low-dose chest CT scans. For vertebra segmentation, the average Dice score was 94.9 ± 2.1% with an average absolute symmetric surface distance of 0.2 ± 10.1mm. The anatomical identification had an accuracy of 93 %, corresponding to a single case with mislabeled vertebrae. Vertebrae were classified as completely or incompletely visible with an accuracy of 97 %. The proposed iterative segmentation method compares favorably with state-of-the-art methods and is fast, flexible and generalizable.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Lessmann2019,
title = {Sex differences in coronary artery and thoracic aorta calcification and their association with cardiovascular mortality in heavy smokers},
author = {N. Lessmann, P.A. de Jong, C. Celeng, R.A.P. Takx, M.A. Viergever, B. van Ginneken, I. Išgum},
url = {https://dx.doi.org/10.1016/j.jcmg.2018.10.026},
year = {2019},
date = {2019-01-16},
journal = {JACC: Cardiovascular Imaging},
volume = {12},
number = {9},
pages = {1808-1817},
abstract = {Objectives: The aim of this study was to investigate sex differences in the prevalence, extent, and association of coronary artery calcium (CAC) and thoracic aorta calcium (TAC) scores with cardiovascular mortality in a population eligible for lung screening.
Background: CAC and TAC scores derived from chest computed tomography (CT) might be useful biomarkers for individualized cardiovascular disease prevention and could be especially relevant in high-risk populations such as heavy smokers. Therefore, it is important to know the prevalence of arterial calcifications in male and female heavy smokers, and if there are differences in the predictive value calcifications carry.
Methods: We performed a nested case–control study with 5,718 participants of the CT arm of the NLST (National Lung Screening Trial). Prevalence and extent of CAC and TAC were resampled to the full cohort to provide unbiased estimates of the typical calcium burden of male and female heavy smokers. Weighted Cox proportional hazards regression was used to assess differences in the association of CAC and TAC scores with all-cause and cardiovascular mortality.
Results: CAC was substantially more common and more severe in men (prevalence: 81% vs. 60%; median volume: 104 mm³ vs.12 mm³). Women had CAC comparable to that of men who were 10 years younger. TAC was equally common in men and women, with a tendency to be more pronounced in women (prevalence: 92% vs. 93%; median volume: 388 mm³ vs. 404 mm³). Both types of calcification were associated with increased cardiovascular and all-cause mortality. TAC scores improved the prediction of coronary heart disease mortality over CAC in men, but not in women. In both sexes, TAC, but not CAC, was associated with cardiovascular mortality other than coronary heart disease.
Conclusions: CAC develops later in women, whereas TAC develops equally in both sexes. CAC is strongly associated with coronary heart disease, whereas TAC is especially associated with extracardiac vascular mortality in either sex.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Lessmann2017,
title = {Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions},
author = {N. Lessmann, B. van Ginneken, M. Zreik, P.A. de Jong, B.D. de Vos, M.A. Viergever, I. Išgum},
url = {https://arxiv.org/pdf/1711.00349.pdf},
year = {2018},
date = {2018-02-01},
journal = {IEEE Transactions on Medical Imaging},
volume = {37},
number = {2},
pages = {615-625},
abstract = {Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}