Calcifications in the aortic wall, heart valves and coronary arteries are strong and independent predictors of cardiovascular disease (CVD) including myocardial infarction (MI), sudden cardiac death, and stroke. The Dutch lung cancer screening (NELSON) trial offers a possibility to investigate presence and risk of CVD in an asymptomatic high risk population.
In this project, we are developing automatic algorithms to measure existing and novel imaging biomarkers related to CVD in CT images from the NELSON trial. These biomarkers will enrich the phenotype information, enabling genetic studies and focusing on the identification of genes related to CVD risk. Finally, the combined information from imaging and genetics offers the possibility to investigate whether imaging markers and genetic analysis provide equivalent or complementary evidence with respect to CVD risk stratification.
Chest CT scan acquired in the Dutch-Belgian lung cancer screening trial. Calcifications in the coronary arteries and in the aorta that are visible in this image (high intensity areas), allow determination of the cardiovascular risks of this subject.
Researchers
Ivana Išgum
Max Viergever
Pim de Jong
Jessica van Setten
Jurica Šprem
Annelotte Vos
Bob de Vos
Journal Articles |
|
| 1. | B.D. de Vos, J.M. Wolterink, T. Leiner, P.A. de Jong, N. Lessmann, I. Išgum Direct automatic coronary calcium scoring in cardiac and chest CT Journal Article IEEE Transactions on Medical Imaging, 34 , pp. 123-136, 2019. @article{deVos2019, title = {Direct automatic coronary calcium scoring in cardiac and chest CT}, author = {B.D. de Vos, J.M. Wolterink, T. Leiner, P.A. de Jong, N. Lessmann, I. Išgum}, url = {https://ieeexplore.ieee.org/abstract/document/8643342 https://arxiv.org/abs/1902.05408}, year = {2019}, date = {2019-02-21}, journal = {IEEE Transactions on Medical Imaging}, volume = {34}, pages = {123-136}, abstract = {Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet demands of the increasing interest in quantification of CAC, i.e. coronary calcium scoring, especially as an unrequested finding for screening and research, automatic methods have been proposed. Current automatic calcium scoring methods are relatively computationally expensive and only provide scores for one type of CT. To address this, we propose a computationally efficient method that employs two ConvNets: the first performs registration to align the fields of view of input CTs and the second performs direct regression of the calcium score, thereby circumventing time-consuming intermediate CAC segmentation. Optional decision feedback provides insight in the regions that contributed to the calcium score. Experiments were performed using 903 cardiac CT and 1,687 chest CT scans. The method predicted calcium scores in less than 0.3 s. Intra-class correlation coefficient between predicted and manual calcium scores was 0.98 for both cardiac and chest CT. The method showed almost perfect agreement between automatic and manual CVD risk categorization in both datasets, with a linearly weighted Cohen's kappa of 0.95 in cardiac CT and 0.93 in chest CT. Performance is similar to that of state-of-the-art methods, but the proposed method is hundreds of times faster. By providing visual feedback, insight is given in the decision process, making it readily implementable in clinical and research settings.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet demands of the increasing interest in quantification of CAC, i.e. coronary calcium scoring, especially as an unrequested finding for screening and research, automatic methods have been proposed. Current automatic calcium scoring methods are relatively computationally expensive and only provide scores for one type of CT. To address this, we propose a computationally efficient method that employs two ConvNets: the first performs registration to align the fields of view of input CTs and the second performs direct regression of the calcium score, thereby circumventing time-consuming intermediate CAC segmentation. Optional decision feedback provides insight in the regions that contributed to the calcium score. Experiments were performed using 903 cardiac CT and 1,687 chest CT scans. The method predicted calcium scores in less than 0.3 s. Intra-class correlation coefficient between predicted and manual calcium scores was 0.98 for both cardiac and chest CT. The method showed almost perfect agreement between automatic and manual CVD risk categorization in both datasets, with a linearly weighted Cohen's kappa of 0.95 in cardiac CT and 0.93 in chest CT. Performance is similar to that of state-of-the-art methods, but the proposed method is hundreds of times faster. By providing visual feedback, insight is given in the decision process, making it readily implementable in clinical and research settings. |
| 2. | B.D. de Vos; F.F. Berendsen; M.A. Viergever; H. Sokooti; M. Staring; I. Išgum A deep learning framework for unsupervised affine and deformable image registration Journal Article Medical Image Analysis, 52 , pp. 128 - 143, 2019. @article{deVos2019b, title = {A deep learning framework for unsupervised affine and deformable image registration}, author = {B.D. de Vos and F.F. Berendsen and M.A. Viergever and H. Sokooti and M. Staring and I. Išgum}, url = {http://www.sciencedirect.com/science/article/pii/S1361841518300495}, year = {2019}, date = {2019-02-21}, journal = {Medical Image Analysis}, volume = {52}, pages = {128 - 143}, abstract = {Deep learning, Unsupervised learning, Affine image registration, Deformable image registration, Cardiac cine MRI, Chest CT\", abstract = \"Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Deep learning, Unsupervised learning, Affine image registration, Deformable image registration, Cardiac cine MRI, Chest CT", abstract = "Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster. |
| 3. | J. Šprem, B.D. de Vos, N. Lessmann, R.W. van Hamersvelt, M.J.W. Greuter, P.A. de Jong, T. Leiner, M.A. Viergever, I. Išgum Coronary calcium scoring with partial volume correction in anthropomorphic thorax phantom and screening chest CT images Journal Article PLoS One, 13 (12), pp. e0209318, 2018. @article{Šprem2018b, title = {Coronary calcium scoring with partial volume correction in anthropomorphic thorax phantom and screening chest CT images}, author = {J. Šprem, B.D. de Vos, N. Lessmann, R.W. van Hamersvelt, M.J.W. Greuter, P.A. de Jong, T. Leiner, M.A. Viergever, I. Išgum}, url = {https://doi.org/10.1371/journal.pone.0209318}, year = {2018}, date = {2018-12-20}, journal = {PLoS One}, volume = {13}, number = {12}, pages = {e0209318}, abstract = {Introduction: The amount of coronary artery calcium determined in CT scans is a well established predictor of cardiovascular events. However, high interscan variability of coronary calcium quantification may lead to incorrect cardiovascular risk assignment. Partial volume effect contributes to high interscan variability. Hence, we propose a method for coronary calcium quantification employing partial volume Methods: Two phantoms containing artificial coronary artery calcifications and 293 subject chest CT scans were used. The first and second phantom contained nine calcifications and the second phantom contained three artificial arteries with three calcifications of different volumes, shapes and densities. The first phantom was scanned five times with and without extension rings. The second phantom was scanned three times without and with simulated cardiac motion (10 and 30 mm/s). Chest CT scans were acquired without ECG-synchronization and reconstructed using sharp and soft kernels. Coronary calcifications were annotated employing the clinically used intensity value thresholding (130 HU). Thereafter, a threshold separating each calcification from its background was determined using an Expectation-Maximization algorithm. Finally, for each lesion the partial content of calcification in each voxel was determined depending on its intensity and the determined threshold. Results: Clinical calcium scoring resulted in overestimation of calcium volume for medium and high density calcifications in the first phantom, and overestimation of calcium volume for high density and underestimation for low density calcifications in the second phantom. With induced motion these effects were further emphasized. The proposed quantification resulted in better accuracy and substantially lower over- and underestimation of calcium volume even in presence of motion. In chest CT, the agreement between calcium scores from the two reconstructions improved when proposed method was Conclusion: Compared with clinical calcium scoring, proposed quantification provides a better estimate of the true calcium volume in phantoms and better agreement in calcium scores between different subject scan reconstructions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Introduction: The amount of coronary artery calcium determined in CT scans is a well established predictor of cardiovascular events. However, high interscan variability of coronary calcium quantification may lead to incorrect cardiovascular risk assignment. Partial volume effect contributes to high interscan variability. Hence, we propose a method for coronary calcium quantification employing partial volume Methods: Two phantoms containing artificial coronary artery calcifications and 293 subject chest CT scans were used. The first and second phantom contained nine calcifications and the second phantom contained three artificial arteries with three calcifications of different volumes, shapes and densities. The first phantom was scanned five times with and without extension rings. The second phantom was scanned three times without and with simulated cardiac motion (10 and 30 mm/s). Chest CT scans were acquired without ECG-synchronization and reconstructed using sharp and soft kernels. Coronary calcifications were annotated employing the clinically used intensity value thresholding (130 HU). Thereafter, a threshold separating each calcification from its background was determined using an Expectation-Maximization algorithm. Finally, for each lesion the partial content of calcification in each voxel was determined depending on its intensity and the determined threshold. Results: Clinical calcium scoring resulted in overestimation of calcium volume for medium and high density calcifications in the first phantom, and overestimation of calcium volume for high density and underestimation for low density calcifications in the second phantom. With induced motion these effects were further emphasized. The proposed quantification resulted in better accuracy and substantially lower over- and underestimation of calcium volume even in presence of motion. In chest CT, the agreement between calcium scores from the two reconstructions improved when proposed method was Conclusion: Compared with clinical calcium scoring, proposed quantification provides a better estimate of the true calcium volume in phantoms and better agreement in calcium scores between different subject scan reconstructions. |
| 4. | J. Šprem; B.D. de Vos; N. Lessmann; P.A. de Jong; M.A. Viergever; I. Išgum Impact of automatically detected motion artifacts on coronary calcium scoring in chest CT Journal Article Journal of Medical Imaging, 5 (4), pp. 044007 , 2018. @article{Šprem2018, title = {Impact of automatically detected motion artifacts on coronary calcium scoring in chest CT}, author = {J. Šprem and B.D. de Vos and N. Lessmann and P.A. de Jong and M.A. Viergever and I. Išgum}, url = {https://doi.org/10.1117/1.JMI.5.4.044007}, year = {2018}, date = {2018-12-11}, journal = {Journal of Medical Imaging}, volume = {5}, number = {4}, pages = {044007 }, abstract = {The amount of coronary artery calcification (CAC) quantified in CT scans enables prediction of cardiovascular disease (CVD) risk. However, interscan variability of CAC quantification is high, especially in scans made without ECG synchronization. We propose a method for automatic detection of CACs that are severely affected by cardiac motion. Subsequently, we evaluate the impact of such CACs on CAC quantification and CVD risk determination. This study includes 1000 baseline and 585 one year follow-up low-dose chest CTs from the National Lung Screening Trial. 415 baseline scans are used to train and evaluate a convolutional neural network that identifies observer determined CACs affected by severe motion artifacts. Thereafter, 585 paired scans acquired at baseline and follow-up were used to evaluate the impact of severe motion artifacts on CAC quantification and risk categorization. Based on the CAC amount the scans were categorized into four standard CVD risk categories. The method identified CACs affected by severe motion artifacts with 85.2% accuracy. Moreover, reproducibility of CAC scores in scan pairs is higher in scans containing mostly CACs not affected by severe cardiac motion. Hence, the proposed method enables identification of scans affected by severe cardiac motion where CAC quantification may not be reproducible.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The amount of coronary artery calcification (CAC) quantified in CT scans enables prediction of cardiovascular disease (CVD) risk. However, interscan variability of CAC quantification is high, especially in scans made without ECG synchronization. We propose a method for automatic detection of CACs that are severely affected by cardiac motion. Subsequently, we evaluate the impact of such CACs on CAC quantification and CVD risk determination. This study includes 1000 baseline and 585 one year follow-up low-dose chest CTs from the National Lung Screening Trial. 415 baseline scans are used to train and evaluate a convolutional neural network that identifies observer determined CACs affected by severe motion artifacts. Thereafter, 585 paired scans acquired at baseline and follow-up were used to evaluate the impact of severe motion artifacts on CAC quantification and risk categorization. Based on the CAC amount the scans were categorized into four standard CVD risk categories. The method identified CACs affected by severe motion artifacts with 85.2% accuracy. Moreover, reproducibility of CAC scores in scan pairs is higher in scans containing mostly CACs not affected by severe cardiac motion. Hence, the proposed method enables identification of scans affected by severe cardiac motion where CAC quantification may not be reproducible. |
| 5. | A. Vos, G. Kranenburg, P.A. de Jong, W.P.T.M. Mali, W. van Hecke, R.L.A.W. Bleys, I. Išgum, A. Vink, W. Spiering Insights Imaging, 9 (4), pp. 493-498, 2018. @article{Vos2018, title = {The amount of calcifications in pseudoxanthoma elasticum patients is underestimated in computed tomographic imaging; a post-mortem correlation of histological and computed tomographic findings in two cases}, author = {A. Vos, G. Kranenburg, P.A. de Jong, W.P.T.M. Mali, W. van Hecke, R.L.A.W. Bleys, I. Išgum, A. Vink, W. Spiering}, url = {https://link.springer.com/article/10.1007%2Fs13244-018-0621-6}, year = {2018}, date = {2018-06-01}, journal = {Insights Imaging}, volume = {9}, number = {4}, pages = {493-498}, abstract = {OBJECTIVES: Pseudoxanthoma elasticum (PXE) is a rare genetic disorder, characterised by elastic fibre degeneration and calcifications in multiple organ systems. Computed tomography (CT) imaging is a potential method to monitor disease progression in PXE patients; however, this method has not been validated. The aim of this study was to correlate histological and computed tomographic findings in PXE patients to investigate the ability of CT scanning to detect these alterations. METHODS: Post mortem total body CT scans were obtained from two PXE patients (a 69-year-old male and 77-year-old female). Autopsy was performed, and 38 tissue samples of the first and 45 tissue samples of the second patient were extensively investigated histologically. The findings were compared with the CT scans. RESULTS: Degenerated and calcified elastic fibres and calcifications were histologically found in the skin, subcutaneous fat, heart, arteries and pleura and around the oesophagus. On CT imaging only the intradermal alterations of the skin and the larger vascular calcifications were detected. The smaller PXE-related abnormalities were not visible on CT. CONCLUSIONS: With CT imaging vascular calcifications and skin alterations can be monitored in PXE patients. However, many of the subtle PXE-related abnormalities found in other organ systems during the autopsy were not visualised by CT scans. Furthermore, we extended the current knowledge on the disease location of PXE with subcutaneous, oesophageal and pleural lesions. TEACHING POINTS: • CT can be used to monitor gross vascular calcifications in PXE patients. • Many subtle PXE-related abnormalities are not visualised by CT scans. • PXE-related alterations can also be found in oesophagus, pleura and subcutaneous fat.}, keywords = {}, pubstate = {published}, tppubtype = {article} } OBJECTIVES: Pseudoxanthoma elasticum (PXE) is a rare genetic disorder, characterised by elastic fibre degeneration and calcifications in multiple organ systems. Computed tomography (CT) imaging is a potential method to monitor disease progression in PXE patients; however, this method has not been validated. The aim of this study was to correlate histological and computed tomographic findings in PXE patients to investigate the ability of CT scanning to detect these alterations. METHODS: Post mortem total body CT scans were obtained from two PXE patients (a 69-year-old male and 77-year-old female). Autopsy was performed, and 38 tissue samples of the first and 45 tissue samples of the second patient were extensively investigated histologically. The findings were compared with the CT scans. RESULTS: Degenerated and calcified elastic fibres and calcifications were histologically found in the skin, subcutaneous fat, heart, arteries and pleura and around the oesophagus. On CT imaging only the intradermal alterations of the skin and the larger vascular calcifications were detected. The smaller PXE-related abnormalities were not visible on CT. CONCLUSIONS: With CT imaging vascular calcifications and skin alterations can be monitored in PXE patients. However, many of the subtle PXE-related abnormalities found in other organ systems during the autopsy were not visualised by CT scans. Furthermore, we extended the current knowledge on the disease location of PXE with subcutaneous, oesophageal and pleural lesions. TEACHING POINTS: • CT can be used to monitor gross vascular calcifications in PXE patients. • Many subtle PXE-related abnormalities are not visualised by CT scans. • PXE-related alterations can also be found in oesophagus, pleura and subcutaneous fat. |
| 6. | B.D. de Vos, J.M. Wolterink, P.A. de Jong, T. Leiner, M.A. Viergever, I. Isgum ConvNet-based localization of anatomical structures in 3D medical images Journal Article IEEE Transactions on Medical Imaging, 36 (7), pp. 1470-1481, 2017. @article{devos2017, title = {ConvNet-based localization of anatomical structures in 3D medical images}, author = {B.D. de Vos, J.M. Wolterink, P.A. de Jong, T. Leiner, M.A. Viergever, I. Isgum}, year = {2017}, date = {2017-02-17}, journal = {IEEE Transactions on Medical Imaging}, volume = {36}, number = {7}, pages = {1470-1481}, abstract = {Localization of anatomical structures is a prerequisite for many tasks in medical image analysis. We propose a method for automatic localization of one or more anatomical structures in 3D medical images through detection of their presence in 2D image slices using a convolutional neural network (ConvNet). A single ConvNet is trained to detect presence of the anatomical structure of interest in axial, coronal, and sagittal slices extracted from a 3D image. To allow the ConvNet to analyze slices of different sizes, spatial pyramid pooling is applied. After detection, 3D bounding boxes are created by combining the output of the ConvNet in all slices. In the experiments 200 chest CT, 100 cardiac CT angiography (CTA), and 100 abdomen CT scans were used. The heart, ascending aorta, aortic arch, and descending aorta were localized in chest CT scans, the left cardiac ventricle in cardiac CTA scans, and the liver in abdomen CT scans. Localization was evaluated using the distances between automatically and manually defined reference bounding box centroids and walls. The best results were achieved in localization of structures with clearly defined boundaries (e.g. aortic arch) and the worst when the structure boundary was not clearly visible (e.g. liver). The method was more robust and accurate in localization multiple structures.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Localization of anatomical structures is a prerequisite for many tasks in medical image analysis. We propose a method for automatic localization of one or more anatomical structures in 3D medical images through detection of their presence in 2D image slices using a convolutional neural network (ConvNet). A single ConvNet is trained to detect presence of the anatomical structure of interest in axial, coronal, and sagittal slices extracted from a 3D image. To allow the ConvNet to analyze slices of different sizes, spatial pyramid pooling is applied. After detection, 3D bounding boxes are created by combining the output of the ConvNet in all slices. In the experiments 200 chest CT, 100 cardiac CT angiography (CTA), and 100 abdomen CT scans were used. The heart, ascending aorta, aortic arch, and descending aorta were localized in chest CT scans, the left cardiac ventricle in cardiac CTA scans, and the liver in abdomen CT scans. Localization was evaluated using the distances between automatically and manually defined reference bounding box centroids and walls. The best results were achieved in localization of structures with clearly defined boundaries (e.g. aortic arch) and the worst when the structure boundary was not clearly visible (e.g. liver). The method was more robust and accurate in localization multiple structures. |
| 7. | P. Natarajan; J. C. Bis; L. F. Bielak; A. J. Cox; M. Dorr; M. F. Feitosa; N. Franceschini; X. Guo; S-J. Hwang; A. Isaacs; M. A. Jhun; et al Multiethnic Exome-Wide Association Study of Subclinical Atherosclerosis Journal Article Circulation. Cardiovascular genetics, 9 (6), pp. 511-520, 2016. @article{Natarajan2016, title = {Multiethnic Exome-Wide Association Study of Subclinical Atherosclerosis}, author = {P. Natarajan and J. C. Bis and L. F. Bielak and A. J. Cox and M. Dorr and M. F. Feitosa and N. Franceschini and X. Guo and S-J. Hwang and A. Isaacs and M. A. Jhun and et al}, year = {2016}, date = {2016-12-02}, journal = {Circulation. Cardiovascular genetics}, volume = {9}, number = {6}, pages = {511-520}, address = {United States}, abstract = {BACKGROUND: -The burden of subclinical atherosclerosis in asymptomatic individuals is heritable and associated with elevated risk of developing clinical coronary heart disease (CHD). We sought to identify genetic variants in protein-coding regions associated with subclinical atherosclerosis and the risk of subsequent CHD. METHODS AND RESULTS: -We studied a total of 25,109 European ancestry and African-American participants with coronary artery calcification (CAC) measured by cardiac computed tomography and 52,869 with common carotid intima media thickness (CIMT) measured by ultrasonography within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Participants were genotyped for 247,870 DNA sequence variants (231,539 in exons) across the genome. A meta-analysis of exome-wide association studies was performed across cohorts for CAC and CIMT. APOB p.Arg3527Gln was associated with four-fold excess CAC (P = 3x10-10). The APOE epsilon2 allele (p.Arg176Cys) was associated with both 22.3% reduced CAC (P = 1x10-12) and 1.4% reduced CIMT (P = 4x10-14) in carriers compared with non-carriers. In secondary analyses conditioning on LDL cholesterol concentration, the epsilon2 protective association with CAC, although attenuated, remained strongly significant. Additionally, the presence of epsilon2 was associated with reduced risk for CHD (OR 0.77; P = 1x10-11). CONCLUSIONS: -Exome-wide association meta-analysis demonstrates that protein-coding variants in APOB and APOE associate with subclinical atherosclerosis. APOE epsilon2 represents the first significant association for multiple subclinical atherosclerosis traits across multiple ethnicities as well as clinical CHD.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: -The burden of subclinical atherosclerosis in asymptomatic individuals is heritable and associated with elevated risk of developing clinical coronary heart disease (CHD). We sought to identify genetic variants in protein-coding regions associated with subclinical atherosclerosis and the risk of subsequent CHD. METHODS AND RESULTS: -We studied a total of 25,109 European ancestry and African-American participants with coronary artery calcification (CAC) measured by cardiac computed tomography and 52,869 with common carotid intima media thickness (CIMT) measured by ultrasonography within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Participants were genotyped for 247,870 DNA sequence variants (231,539 in exons) across the genome. A meta-analysis of exome-wide association studies was performed across cohorts for CAC and CIMT. APOB p.Arg3527Gln was associated with four-fold excess CAC (P = 3x10-10). The APOE epsilon2 allele (p.Arg176Cys) was associated with both 22.3% reduced CAC (P = 1x10-12) and 1.4% reduced CIMT (P = 4x10-14) in carriers compared with non-carriers. In secondary analyses conditioning on LDL cholesterol concentration, the epsilon2 protective association with CAC, although attenuated, remained strongly significant. Additionally, the presence of epsilon2 was associated with reduced risk for CHD (OR 0.77; P = 1x10-11). CONCLUSIONS: -Exome-wide association meta-analysis demonstrates that protein-coding variants in APOB and APOE associate with subclinical atherosclerosis. APOE epsilon2 represents the first significant association for multiple subclinical atherosclerosis traits across multiple ethnicities as well as clinical CHD. |
| 8. | A. Vos, W. van Hecke, W.G.M. Spliet, R. Goldschmeding, I. Isgum, R. Kockelkoren, R.L.A.W. Bleys, W.P.T.M. Mali, P.A. de Jong, A. Vink Predominance of nonatherosclerotic internal elastic lamina calcification in the intracranial internal carotid artery Journal Article Stroke, 47 (1), pp. 221-3, 2016. @article{Vos15, title = {Predominance of nonatherosclerotic internal elastic lamina calcification in the intracranial internal carotid artery}, author = {A. Vos, W. van Hecke, W.G.M. Spliet, R. Goldschmeding, I. Isgum, R. Kockelkoren, R.L.A.W. Bleys, W.P.T.M. Mali, P.A. de Jong, A. Vink}, year = {2016}, date = {2016-01-04}, journal = {Stroke}, volume = {47}, number = {1}, pages = {221-3}, abstract = {Background and Purpose—Calcification of the intracranial internal carotid artery (iICA) is an independent risk factor for stroke. These calcifications are generally seen as manifestation of atherosclerosis, but histological investigations are limited. The aim of this study is to determine whether calcifications in the iICA are present in atherosclerotic plaques, or in other parts of the arterial wall. Methods—Thirty-nine iICAs were histologically assessed, using digital microscopy to quantify the amount of calcification in the different layers of the arterial wall. Results—Calcifications were found in the intima, around the internal elastic lamina and in the medial layer of the arterial wall. In 71% of the arteries, internal elastic lamina calcification contributed most to the total calcified cross-sectional surface area. Internal elastic lamina calcification was unrelated to the occurrence of atherosclerotic intimal lesions. Intimal calcifications were most often associated with atherosclerotic lesions, but also many noncalcified atherosclerotic lesions were found. Conclusions—In the iICA, calcifications are predominantly present around the internal elastic lamina, suggesting that this nonatherosclerotic type of calcification contributes to the previously observed increased risk of stroke in patients with iICA calcifications.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background and Purpose—Calcification of the intracranial internal carotid artery (iICA) is an independent risk factor for stroke. These calcifications are generally seen as manifestation of atherosclerosis, but histological investigations are limited. The aim of this study is to determine whether calcifications in the iICA are present in atherosclerotic plaques, or in other parts of the arterial wall. Methods—Thirty-nine iICAs were histologically assessed, using digital microscopy to quantify the amount of calcification in the different layers of the arterial wall. Results—Calcifications were found in the intima, around the internal elastic lamina and in the medial layer of the arterial wall. In 71% of the arteries, internal elastic lamina calcification contributed most to the total calcified cross-sectional surface area. Internal elastic lamina calcification was unrelated to the occurrence of atherosclerotic intimal lesions. Intimal calcifications were most often associated with atherosclerotic lesions, but also many noncalcified atherosclerotic lesions were found. Conclusions—In the iICA, calcifications are predominantly present around the internal elastic lamina, suggesting that this nonatherosclerotic type of calcification contributes to the previously observed increased risk of stroke in patients with iICA calcifications. |
Inproceedings |
|
| 1. | J.M.H. Noothout, B.D. de Vos, J.M. Wolterink, I. Išgum Automatic segmentation of thoracic aorta segments in low-dose chest CT Inproceedings In: SPIE Medical Imaging, pp. 105741S, 2018. @inproceedings{Noothout2018, title = {Automatic segmentation of thoracic aorta segments in low-dose chest CT}, author = {J.M.H. Noothout, B.D. de Vos, J.M. Wolterink, I. Išgum}, url = {https://arxiv.org/abs/1810.05727 https://doi.org/10.1117/12.2293114}, year = {2018}, date = {2018-10-03}, booktitle = {SPIE Medical Imaging}, volume = {10574}, pages = {105741S}, abstract = {Morphological analysis and identi fication of pathologies in the aorta are important for cardiovascular diagnosis and risk assessment in patients. Manual annotation is time-consuming and cumbersome in CT scans acquired without contrast enhancement and with low radiation dose. Hence, we propose an automatic method to segment the ascending aorta, the aortic arch and the thoracic descending aorta in low-dose chest CT without contrast enhancement. Segmentation was performed using a dilated convolutional neural network (CNN), with a receptive field of 131X131 voxels, that classifi ed voxels in axial, coronal and sagittal image slices. To obtain a final segmentation, the obtained probabilities of the three planes were averaged per class, and voxels were subsequently assigned to the class with the highest class probability. Two-fold cross-validation experiments were performed where ten scans were used to train the network and another ten to evaluate the performance. Dice coefficients of 0.83, 0.86 and 0.88, and Average Symmetrical Surface Distances (ASSDs) of 2.44, 1.56 and 1.87 mm were obtained for the ascending aorta, the aortic arch, and the descending aorta, respectively. The results indicate that the proposed method could be used in large-scale studies analyzing the anatomical location of pathology and morphology of the thoracic aorta.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Morphological analysis and identi fication of pathologies in the aorta are important for cardiovascular diagnosis and risk assessment in patients. Manual annotation is time-consuming and cumbersome in CT scans acquired without contrast enhancement and with low radiation dose. Hence, we propose an automatic method to segment the ascending aorta, the aortic arch and the thoracic descending aorta in low-dose chest CT without contrast enhancement. Segmentation was performed using a dilated convolutional neural network (CNN), with a receptive field of 131X131 voxels, that classifi ed voxels in axial, coronal and sagittal image slices. To obtain a final segmentation, the obtained probabilities of the three planes were averaged per class, and voxels were subsequently assigned to the class with the highest class probability. Two-fold cross-validation experiments were performed where ten scans were used to train the network and another ten to evaluate the performance. Dice coefficients of 0.83, 0.86 and 0.88, and Average Symmetrical Surface Distances (ASSDs) of 2.44, 1.56 and 1.87 mm were obtained for the ascending aorta, the aortic arch, and the descending aorta, respectively. The results indicate that the proposed method could be used in large-scale studies analyzing the anatomical location of pathology and morphology of the thoracic aorta. |
| 2. | B.D. de Vos, F.F. Berendsen, M.A. Viergever, M. Staring, I. Isgum End-to-end unsupervised deformable image registration with a convolutional neural network Inproceedings In: ML-CDS 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, Proceedings (Ed.): Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, pp. 204–212, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, Proceedings 2017. @inproceedings{deVos2017bb, title = {End-to-end unsupervised deformable image registration with a convolutional neural network}, author = {B.D. de Vos, F.F. Berendsen, M.A. Viergever, M. Staring, I. Isgum}, editor = {ML-CDS 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, Proceedings}, url = {https://arxiv.org/abs/1704.06065}, year = {2017}, date = {2017-10-27}, booktitle = {Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017}, issuetitle = {ML-CDS 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, Proceedings}, journal = {ML-CDS 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, Proceedings}, pages = {204--212}, organization = {ML-CDS 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, Proceedings}, abstract = {In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial transformer, which generates the displacement vector field that enables the resampler to warp the moving image to the fixed image. The DIRNet is trained end-to-end by unsupervised optimization of a similarity metric between input image pairs. A trained DIRNet can be applied to perform registration on unseen image pairs in one pass, thus non-iteratively. Evaluation was performed with registration of images of handwritten digits (MNIST) and cardiac cine MR scans (Sunnybrook Cardiac Data). The results demonstrate that registration with DIRNet is as accurate as a conventional deformable image registration method with short execution times.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial transformer, which generates the displacement vector field that enables the resampler to warp the moving image to the fixed image. The DIRNet is trained end-to-end by unsupervised optimization of a similarity metric between input image pairs. A trained DIRNet can be applied to perform registration on unseen image pairs in one pass, thus non-iteratively. Evaluation was performed with registration of images of handwritten digits (MNIST) and cardiac cine MR scans (Sunnybrook Cardiac Data). The results demonstrate that registration with DIRNet is as accurate as a conventional deformable image registration method with short execution times. |
| 3. | H. Sokooti, B.D. de Vos, F. Berendsen, B.P.F. Lelieveldt, I. Isgum, M. Staring Nonrigid image registration using multi-scale 3D convolutional neural networks Inproceedings In: Medical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part 1, pp. 232–239, 2017. @inproceedings{Sokoti17, title = {Nonrigid image registration using multi-scale 3D convolutional neural networks}, author = {H. Sokooti, B.D. de Vos, F. Berendsen, B.P.F. Lelieveldt, I. Isgum, M. Staring}, url = {https://link.springer.com/content/pdf/10.1007%2F978-3-319-66182-7_27.pdf}, year = {2017}, date = {2017-09-10}, booktitle = {Medical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part 1}, volume = {10433}, pages = {232--239}, series = {Lecture Notes in Computer Science}, abstract = {In this paper we propose a method to solve nonrigid image registration through a learning approach, instead of via iterative optimization of a predefined dissimilarity metric. We design a Convolutional Neural Network (CNN) architecture that, in contrast to all other work, directly estimates the displacement vector field (DVF) from a pair of input images. The proposed RegNet is trained using a large set of artificially generated DVFs, does not explicitly define a dissimilarity metric, and integrates image content at multiple scales to equip the network with contextual information. At testing time nonrigid registration is performed in a single shot, in contrast to current iterative methods. We tested RegNet on 3D chest CT follow-up data. The results show that the accuracy of RegNet is on par with a conventional B-spline registration, for anatomy within the capture range. Training RegNet with artificially generated DVFs is therefore a promising approach for obtaining good results on real clinical data, thereby greatly simplifying the training problem. Deformable image registration can therefore be successfully casted as a learning problem.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this paper we propose a method to solve nonrigid image registration through a learning approach, instead of via iterative optimization of a predefined dissimilarity metric. We design a Convolutional Neural Network (CNN) architecture that, in contrast to all other work, directly estimates the displacement vector field (DVF) from a pair of input images. The proposed RegNet is trained using a large set of artificially generated DVFs, does not explicitly define a dissimilarity metric, and integrates image content at multiple scales to equip the network with contextual information. At testing time nonrigid registration is performed in a single shot, in contrast to current iterative methods. We tested RegNet on 3D chest CT follow-up data. The results show that the accuracy of RegNet is on par with a conventional B-spline registration, for anatomy within the capture range. Training RegNet with artificially generated DVFs is therefore a promising approach for obtaining good results on real clinical data, thereby greatly simplifying the training problem. Deformable image registration can therefore be successfully casted as a learning problem. |
| 4. | J. Šprem; B.D. de Vos; P.A. de Jong; M.A. Viergever; I. Isgum In: SPIE Medical Imaging, 2017. @inproceedings{Šprem2017-3103, title = {Classification of coronary artery calcifications according to motion artifacts in chest CT using a convolutional neural network}, author = {J. Šprem and B.D. de Vos and P.A. de Jong and M.A. Viergever and I. Isgum}, url = {https://doi.org/10.1117/12.2253669}, year = {2017}, date = {2017-02-13}, booktitle = {SPIE Medical Imaging}, series = {10133-27}, abstract = {Coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events (CVEs). CAC can be quantified in chest CT scans acquired in lung screening. However, in these images the reproducibility of CAC quantification is compromised by cardiac motion artifacts that occur during scanning, which limits the reproducibility of CVE risk assessment. We present a system for detection of severe cardiac motion artifacts affecting CACs by using a convolutional neural network (CNN). This study included 125 chest CT scans from the National Lung Screening Trial (NLST). The images were acquired with CT scanners from four major CT scanner vendors (GE, Siemens, Philips, Toshiba) with varying tube voltage and slice thickness settings, and without ECG synchronization. An observer manually identified CAC lesions and labelled each CAC according to presence of cardiac motion (strongly affected, not affected). A CNN was designed to automatically label the identified CAC lesions according to the presence of cardiac motion by analyzing a patch from the axial CT slice around each CAC lesion. From 125 CT scans, 9201 CAC lesions were analyzed. 8001 lesions were used for training (19% positive) and the remaining 1200 (50% positive) were used for testing. The CNN achieved a classification accuracy of 85% (86% sensitivity, 84% specificity).}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events (CVEs). CAC can be quantified in chest CT scans acquired in lung screening. However, in these images the reproducibility of CAC quantification is compromised by cardiac motion artifacts that occur during scanning, which limits the reproducibility of CVE risk assessment. We present a system for detection of severe cardiac motion artifacts affecting CACs by using a convolutional neural network (CNN). This study included 125 chest CT scans from the National Lung Screening Trial (NLST). The images were acquired with CT scanners from four major CT scanner vendors (GE, Siemens, Philips, Toshiba) with varying tube voltage and slice thickness settings, and without ECG synchronization. An observer manually identified CAC lesions and labelled each CAC according to presence of cardiac motion (strongly affected, not affected). A CNN was designed to automatically label the identified CAC lesions according to the presence of cardiac motion by analyzing a patch from the axial CT slice around each CAC lesion. From 125 CT scans, 9201 CAC lesions were analyzed. 8001 lesions were used for training (19% positive) and the remaining 1200 (50% positive) were used for testing. The CNN achieved a classification accuracy of 85% (86% sensitivity, 84% specificity). |
| 5. | B.D. de Vos, M.A. Viergever, P.A. de Jong, I. Isgum Automatic Slice Identification in 3D Medical Images with a ConvNet Regressor Inproceedings In: Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, MICCAI 2016, Athens, Greece, pp. 161–169, 2016. @inproceedings{devos-slice2016, title = {Automatic Slice Identification in 3D Medical Images with a ConvNet Regressor}, author = {B.D. de Vos, M.A. Viergever, P.A. de Jong, I. Isgum}, year = {2016}, date = {2016-09-27}, booktitle = {Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, MICCAI 2016, Athens, Greece}, pages = {161--169}, abstract = {Identi fication of anatomical regions of interest is a prerequisite in many medical image analysis tasks. We propose a method that automatically identifi es a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor. In 150 chest CT scans two reference slices were manually identi ed: one containing the aortic root and another superior to the aortic arch. In two independent experiments, the ConvNet regressor was trained with 100 CTs to determine the distance between each slice and the SOI in a CT. To identify the SOI, a first order polynomial was fitted through the obtained distances. In 50 test scans, the mean distances between the reference and the automatically identifi ed slices were 5.7mm (4.0 slices) for the aortic root and 5.6mm (3.7 slices) for the aortic arch. The method shows similar results for both tasks and could be used for automatic slice identi fication.}, howpublished = {Deep Learning and Data Labeling for Medical Applications - Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Identi fication of anatomical regions of interest is a prerequisite in many medical image analysis tasks. We propose a method that automatically identifi es a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor. In 150 chest CT scans two reference slices were manually identi ed: one containing the aortic root and another superior to the aortic arch. In two independent experiments, the ConvNet regressor was trained with 100 CTs to determine the distance between each slice and the SOI in a CT. To identify the SOI, a first order polynomial was fitted through the obtained distances. In 50 test scans, the mean distances between the reference and the automatically identifi ed slices were 5.7mm (4.0 slices) for the aortic root and 5.6mm (3.7 slices) for the aortic arch. The method shows similar results for both tasks and could be used for automatic slice identi fication. |
| 6. | B.D. de Vos, J. van Setten, P.A. de Jong, W.P. Mali, M. Oudkerk, M.A. Viergever, I. Isgum Genome-Wide Association Study of Coronary and Aortic Calcification in Lung Cancer Screening CT Inproceedings In: SPIE Medical Imaging, pp. 97841L-1-97841L-6, 2016. @inproceedings{devo2016b, title = {Genome-Wide Association Study of Coronary and Aortic Calcification in Lung Cancer Screening CT}, author = {B.D. de Vos, J. van Setten, P.A. de Jong, W.P. Mali, M. Oudkerk, M.A. Viergever, I. Isgum}, year = {2016}, date = {2016-03-01}, booktitle = {SPIE Medical Imaging}, volume = {9784}, pages = {97841L-1-97841L-6}, abstract = {Arterial calcification has been related to cardiovascular disease (CVD) and osteoporosis. However, little is known about the role of genetics and exact pathways leading to arterial calcification and its relation to bone density changes indicating osteoporosis. In this study, we conducted a genome-wide association study of arterial calcification burden, followed by a look-up of known single nucleotide polymorphisms (SNPs) for coronary artery disease (CAD) and myocardial infarction (MI), and bone mineral density (BMD) to test for a shared genetic basis between the traits. The study included a subcohort of Dutch-Belgian lung cancer screening trial comprised of 2,552 participants. The participants underwent baseline CT screening in one of two hospitals participating in the trial. Low-dose chest CT images were acquired without contrast enhancement and without ECG-synchronization. In these images coronary and aortic calcifications were identified automatically. Subsequently, the detected calcifications were quantified using Agatston and volume scores. Genotype data was available for these participants. A genome-wide association study was conducted on 10,220,814 SNPs using a linear regression model. To reduce multiple testing burden, known CAD/MI and BMD SNPs were specifically tested (45 SNPs from the CARDIoGRAMplusC4D consortium and 60 SNPS from the GEFOS consortium). No novel significant SNPs were found. Significant enrichment for CAD/MI SNPs were observed in testing Agatston and coronary volume scores, and moreover a significant enrichment of BMD SNPs was shown in aorta volume scores. This may indicate genetic relation of BMD SNPs and arterial calcification burden.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Arterial calcification has been related to cardiovascular disease (CVD) and osteoporosis. However, little is known about the role of genetics and exact pathways leading to arterial calcification and its relation to bone density changes indicating osteoporosis. In this study, we conducted a genome-wide association study of arterial calcification burden, followed by a look-up of known single nucleotide polymorphisms (SNPs) for coronary artery disease (CAD) and myocardial infarction (MI), and bone mineral density (BMD) to test for a shared genetic basis between the traits. The study included a subcohort of Dutch-Belgian lung cancer screening trial comprised of 2,552 participants. The participants underwent baseline CT screening in one of two hospitals participating in the trial. Low-dose chest CT images were acquired without contrast enhancement and without ECG-synchronization. In these images coronary and aortic calcifications were identified automatically. Subsequently, the detected calcifications were quantified using Agatston and volume scores. Genotype data was available for these participants. A genome-wide association study was conducted on 10,220,814 SNPs using a linear regression model. To reduce multiple testing burden, known CAD/MI and BMD SNPs were specifically tested (45 SNPs from the CARDIoGRAMplusC4D consortium and 60 SNPS from the GEFOS consortium). No novel significant SNPs were found. Significant enrichment for CAD/MI SNPs were observed in testing Agatston and coronary volume scores, and moreover a significant enrichment of BMD SNPs was shown in aorta volume scores. This may indicate genetic relation of BMD SNPs and arterial calcification burden. |
| 7. | B.D. de Vos, J.M. Wolterink, P.A. de Jong, M.A. Viergever, I. Isgum 2D image classification for 3D anatomy localization; employing deep convolutional neural networks Inproceedings In: SPIE Medical Imaging, pp. 97841Y-1-97841Y-7, 2016. @inproceedings{devo2016, title = {2D image classification for 3D anatomy localization; employing deep convolutional neural networks}, author = {B.D. de Vos, J.M. Wolterink, P.A. de Jong, M.A. Viergever, I. Isgum}, year = {2016}, date = {2016-03-01}, booktitle = {SPIE Medical Imaging}, volume = {9784}, pages = {97841Y-1-97841Y-7}, abstract = {Localization of anatomical regions of interest (ROIs) is a preprocessing step in many medical image analysis tasks. While trivial for humans, it is complex for automatic methods. Classic machine learning approaches require the challenge of hand crafting features to describe differences between ROIs and background. Deep convolutional neural networks (CNNs) alleviate this by automatically finding hierarchical feature representations from raw images. We employ this trait to detect anatomical ROIs in 2D image slices in order to localize them in 3D. In 100 low-dose non-contrast enhanced non-ECG synchronized screening chest CT scans, a reference standard was defined by manually delineating rectangular bounding boxes around three anatomical ROIs (heart, aortic arch, and descending aorta). The scans were evenly divided into training and test sets. Every anatomical ROI was automatically identified using a combination of three CNNs, each analyzing one orthogonal image plane. While single CNNs predicted presence or absence of a specific ROI in the given plane, the combination of their results provided a 3D bounding box around it. Classification performance of all CNNs, expressed in area under the receiver operating characteristic curve, was >=0.988. Additionally, the performance of localization was evaluated. Median Dice scores for automatically determined bounding boxes around the heart, aortic arch, and descending aorta were 0.89, 0.70, and 0.85 respectively. The results demonstrate that accurate automatic 3D localization of anatomical structures by CNN-based 2D image classification is feasible.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Localization of anatomical regions of interest (ROIs) is a preprocessing step in many medical image analysis tasks. While trivial for humans, it is complex for automatic methods. Classic machine learning approaches require the challenge of hand crafting features to describe differences between ROIs and background. Deep convolutional neural networks (CNNs) alleviate this by automatically finding hierarchical feature representations from raw images. We employ this trait to detect anatomical ROIs in 2D image slices in order to localize them in 3D. In 100 low-dose non-contrast enhanced non-ECG synchronized screening chest CT scans, a reference standard was defined by manually delineating rectangular bounding boxes around three anatomical ROIs (heart, aortic arch, and descending aorta). The scans were evenly divided into training and test sets. Every anatomical ROI was automatically identified using a combination of three CNNs, each analyzing one orthogonal image plane. While single CNNs predicted presence or absence of a specific ROI in the given plane, the combination of their results provided a 3D bounding box around it. Classification performance of all CNNs, expressed in area under the receiver operating characteristic curve, was >=0.988. Additionally, the performance of localization was evaluated. Median Dice scores for automatically determined bounding boxes around the heart, aortic arch, and descending aorta were 0.89, 0.70, and 0.85 respectively. The results demonstrate that accurate automatic 3D localization of anatomical structures by CNN-based 2D image classification is feasible. |
| 8. | B.D. de Vos; P.A. de Jong; J.M. Wolterink; R. Vliegenthart; G.V.F. Wielingen; M.A. Viergever; I. Isgum Automatic machine learning based prediction of cardiovascular events in lung cancer screening data Inproceedings In: SPIE Medical Imaging, pp. 94140D, 2015. @inproceedings{deVos2015, title = {Automatic machine learning based prediction of cardiovascular events in lung cancer screening data}, author = {B.D. de Vos and P.A. de Jong and J.M. Wolterink and R. Vliegenthart and G.V.F. Wielingen and M.A. Viergever and I. Isgum}, year = {2015}, date = {2015-02-02}, booktitle = {SPIE Medical Imaging}, journal = {SPIE Medical Imaging}, volume = {9414}, pages = {94140D}, abstract = {This study investigated whether subjects at risk of a cardiovascular event (CVE) undergoing lung cancer screening can be identified using automatic image analysis and subject characterististics. Coronary and aortic calcifications were automatically identified in 3559 subjects undergoing screening. Number, size and distribution of the detected calcifications were extracted and subject’s age, smoking history and past CVEs were used. A support vector machine classifier using only image features resulted in Az of 0.69. A combination of image and subject related features resulted in an Az of 0.71. Lung cancer screening participants at risk of CVE can be identified using automatic image analysis.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This study investigated whether subjects at risk of a cardiovascular event (CVE) undergoing lung cancer screening can be identified using automatic image analysis and subject characterististics. Coronary and aortic calcifications were automatically identified in 3559 subjects undergoing screening. Number, size and distribution of the detected calcifications were extracted and subject’s age, smoking history and past CVEs were used. A support vector machine classifier using only image features resulted in Az of 0.69. A combination of image and subject related features resulted in an Az of 0.71. Lung cancer screening participants at risk of CVE can be identified using automatic image analysis. |
Abstracts |
|
| 1. | B.D. de Vos, N. Lessmann, P.A. de Jong, M.A. Viergever, I. Isgum Direct coronary artery calcium scoring in low-dose chest CT using deep learning analysis Abstract In: 2017. @booklet{deVos2017b, title = {Direct coronary artery calcium scoring in low-dose chest CT using deep learning analysis}, author = {B.D. de Vos, N. Lessmann, P.A. de Jong, M.A. Viergever, I. Isgum}, year = {2017}, date = {2017-11-28}, booktitle = {Radiological Society of North America, 103rd Annual Meeting}, abstract = {PURPOSE Coronary artery calcium (CAC) score determined in screening with low-dose chest CT is a strong and independent predictor of cardiovascular events (CVE). However, manual CAC scoring in these images is cumbersome. Existing automatic methods detect CAC lesions and thereafter quantify them. However, precise localization of lesions may not be needed to facilitate identification of subjects at risk of CVE. Hence, we have developed a deep learning system for fully automatic, real-time and direct calcium scoring circumventing the need for intermediate detection of CAC lesions. METHOD AND MATERIALS The study included a set of 1,546 baseline CT scans from the National Lung Screening Trial. Three experts defined the reference standard by manually identifying CAC lesions that were subsequently quantified using the Agatston score. The designed convolutional neural network analyzed axial slices and predicted the corresponding Agatston score. Per-subject Agatston scores were determined as the sum of per-slice scores. Each subject was assigned to one of five cardiovascular risk categories (Agatston score: 0, 1-10, 10-100, 100-400, >400). The system was trained with 75% of the scans and tested with the remaining 25%. Correlation between manual and automatic CAC scores was determined using the intra class correlation coefficient (ICC). Agreement of CVD risk categorization was evaluated using accuracy and Cohen’s linearly weighted κ. RESULTS In the 386 test subjects, the median (Q1-Q3) reference Agatston score was 54 (1-321). By the reference, 95, 37, 86, 94 and 75 subjects were assigned to 0, 1-10, 10-100, 100-400, >400 risk categories, respectively. The ICC between the automatic and reference scores was 0.95. The method assigned 85% of subjects to the correct risk category with a κ of 0.90. The score was determined in <2 seconds per CT. CONCLUSION Unlike previous automatic CAC scoring methods, the proposed method allows for quantification of coronary calcium burden without the need for intermediate identification or segmentation of separate CAC lesions. The system is robust and performs analysis in real-time. CLINICAL RELEVANCE/APPLICATION The proposed method may allow real-time identification of subjects at risk of a CVE undergoing CT-based lung cancer screening without the need for intermediate segmentation of coronary calcifications.}, month = {11}, keywords = {}, pubstate = {published}, tppubtype = {booklet} } PURPOSE Coronary artery calcium (CAC) score determined in screening with low-dose chest CT is a strong and independent predictor of cardiovascular events (CVE). However, manual CAC scoring in these images is cumbersome. Existing automatic methods detect CAC lesions and thereafter quantify them. However, precise localization of lesions may not be needed to facilitate identification of subjects at risk of CVE. Hence, we have developed a deep learning system for fully automatic, real-time and direct calcium scoring circumventing the need for intermediate detection of CAC lesions. METHOD AND MATERIALS The study included a set of 1,546 baseline CT scans from the National Lung Screening Trial. Three experts defined the reference standard by manually identifying CAC lesions that were subsequently quantified using the Agatston score. The designed convolutional neural network analyzed axial slices and predicted the corresponding Agatston score. Per-subject Agatston scores were determined as the sum of per-slice scores. Each subject was assigned to one of five cardiovascular risk categories (Agatston score: 0, 1-10, 10-100, 100-400, >400). The system was trained with 75% of the scans and tested with the remaining 25%. Correlation between manual and automatic CAC scores was determined using the intra class correlation coefficient (ICC). Agreement of CVD risk categorization was evaluated using accuracy and Cohen’s linearly weighted κ. RESULTS In the 386 test subjects, the median (Q1-Q3) reference Agatston score was 54 (1-321). By the reference, 95, 37, 86, 94 and 75 subjects were assigned to 0, 1-10, 10-100, 100-400, >400 risk categories, respectively. The ICC between the automatic and reference scores was 0.95. The method assigned 85% of subjects to the correct risk category with a κ of 0.90. The score was determined in <2 seconds per CT. CONCLUSION Unlike previous automatic CAC scoring methods, the proposed method allows for quantification of coronary calcium burden without the need for intermediate identification or segmentation of separate CAC lesions. The system is robust and performs analysis in real-time. CLINICAL RELEVANCE/APPLICATION The proposed method may allow real-time identification of subjects at risk of a CVE undergoing CT-based lung cancer screening without the need for intermediate segmentation of coronary calcifications. |
| 2. | J. Šprem, B.D. de Vos, R. Vliegenthart, M.A. Viergever, P. A. de Jong, I. Isgum Increasing the Interscan Reproducibility of Coronary Calcium Scoring by Partial Volume Correction in Low-Dose non-ECG Synchronized CT: Phantom Study Abstract In: 2015. @booklet{Šprem2015, title = {Increasing the Interscan Reproducibility of Coronary Calcium Scoring by Partial Volume Correction in Low-Dose non-ECG Synchronized CT: Phantom Study}, author = {J. Šprem, B.D. de Vos, R. Vliegenthart, M.A. Viergever, P. A. de Jong, I. Isgum}, year = {2015}, date = {2015-11-29}, booktitle = {Radiological Society of North America}, month = {11}, keywords = {}, pubstate = {published}, tppubtype = {booklet} } |
PhD Theses |
|
| 1. | J. Šprem Enhanced cardiovascular risk prediction by machine learning PhD Thesis Utrecht University, The Netherlands, 2019, ISBN: 978-94-6323-713-0. @phdthesis{Šprem2019, title = {Enhanced cardiovascular risk prediction by machine learning}, author = {J. Šprem}, isbn = {978-94-6323-713-0}, year = {2019}, date = {2019-07-11}, school = {Utrecht University, The Netherlands}, keywords = {}, pubstate = {published}, tppubtype = {phdthesis} } |