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| Steffen Bruns Amsterdam UMC s.bruns@amsterdamumc.nl |
PhD Candidate
E-mail: s.bruns@amsterdamumc.nl
Phone: +31 20 56 65206
LinkedIn; Google Scholar
Steffen studied Medical Engineering Sciences at the University of Lübeck, Germany. Within this program, he got the chance to be an intern at the University of California, Berkeley. He joined the Berkeley Imaging Systems Laboratory where he worked on comparing the sensitivity of MRI to Magnetic Particle Imaging. For his Master thesis, he completed a project on the accelerated reconstruction for small-animal pinhole PET at MILabs in Utrecht, Netherlands.
In October 2017, Steffen started his PhD at the Image Sciences Institute of the UMC Utrecht and joined the Quantitative Medical Image Analysis Group. He is part of the program
Deep learning in medical image analysis (DLMedIA) where he is currently working on the development of novel deep learning algorithms for the automatic analysis of high-dimensional medical images, mainly spectral CT.
2020 |
S. Bruns, J.M. Wolterink, R.A.P. Takx, R.W. van Hamersvelt, D. Suchá, M.A. Viergever, T. Leiner, I. Išgum Deep learning from dual-energy information for whole-heart segmentation in dual-energy and single-energy non-contrast-enhanced cardiac CT Journal Article Medical Physics (in press), 2020. @article{Bruns2020b, title = {Deep learning from dual-energy information for whole-heart segmentation in dual-energy and single-energy non-contrast-enhanced cardiac CT}, author = {S. Bruns, J.M. Wolterink, R.A.P. Takx, R.W. van Hamersvelt, D. Suchá, M.A. Viergever, T. Leiner, I. Išgum }, url = {https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.14451}, doi = {10.1002/mp.14451}, year = {2020}, date = {2020-08-05}, journal = {Medical Physics (in press)}, abstract = {Purpose Deep learning-based whole-heart segmentation in coronary CT angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic extraction of these measures in patients undergoing only non-contrast-enhanced CT (NCCT) scanning would be valuable, but defining a manual reference standard that would allow training a deep learning-based method for whole-heart segmentation in NCCT is challenging, if not impossible. In this work, we leverage dual-energy information provided by a dual-layer detector CT scanner to obtain a reference standard in virtual non-contrast (VNC) CT images mimicking NCCT images, and train a 3D convolutional neural network (CNN) for the segmentation of VNC as well as NCCT images. Methods Eighteen patients were scanned with and without contrast enhancement on a dual-layer detector CT scanner. Contrast-enhanced acquisitions were reconstructed into a CCTA and a perfectly aligned VNC image. In each CCTA image, manual reference segmentations of the left ventricular (LV) myocardium, LV cavity, right ventricle, left atrium, right atrium, ascending aorta, and pulmonary artery trunk were obtained and propagated to the corresponding VNC image. These VNC images and reference segmentations were used to train 3D CNNs in a six-fold cross-validation for automatic segmentation in either VNC images or NCCT images reconstructed from the non-contrast-enhanced acquisition. Automatic segmentation in VNC images was evaluated using the Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD). Automatically determined volumes of the cardiac chambers and LV myocardium in NCCT were compared to reference volumes of the same patient in CCTA by Bland-Altman analysis. An additional independent multi-vendor multi-center set of single-energy NCCT images from 290 patients was used for qualitative analysis, in which two observers graded segmentations on a five-point scale. Results Automatic segmentations in VNC images showed good agreement with reference segmentations, with an average DSC of 0.897 ± 0.034 and an average ASSD of 1.42 ± 0.45 mm. Volume differences [95% confidence interval] between automatic NCCT and reference CCTA segmentations were -19 [-67; 30] mL for LV myocardium, -25 [-78; 29] mL for LV cavity, -29 [-73; 14] mL for right ventricle, -20 [-62; 21] mL for left atrium, and -19 [-73; 34] mL for right atrium, respectively. In 214 (74%) NCCT images from the independent multi-vendor multi-center set, both observers agreed that the automatic segmentation was mostly accurate (grade 3) or better. Conclusion Our automatic method produced accurate whole-heart segmentations in NCCT images using a CNN trained with VNC images from a dual-layer detector CT scanner. This method might enable quantification of additional cardiac measures from NCCT images for improved cardiovascular risk prediction. }, keywords = {}, pubstate = {published}, tppubtype = {article} } Purpose Deep learning-based whole-heart segmentation in coronary CT angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic extraction of these measures in patients undergoing only non-contrast-enhanced CT (NCCT) scanning would be valuable, but defining a manual reference standard that would allow training a deep learning-based method for whole-heart segmentation in NCCT is challenging, if not impossible. In this work, we leverage dual-energy information provided by a dual-layer detector CT scanner to obtain a reference standard in virtual non-contrast (VNC) CT images mimicking NCCT images, and train a 3D convolutional neural network (CNN) for the segmentation of VNC as well as NCCT images. Methods Eighteen patients were scanned with and without contrast enhancement on a dual-layer detector CT scanner. Contrast-enhanced acquisitions were reconstructed into a CCTA and a perfectly aligned VNC image. In each CCTA image, manual reference segmentations of the left ventricular (LV) myocardium, LV cavity, right ventricle, left atrium, right atrium, ascending aorta, and pulmonary artery trunk were obtained and propagated to the corresponding VNC image. These VNC images and reference segmentations were used to train 3D CNNs in a six-fold cross-validation for automatic segmentation in either VNC images or NCCT images reconstructed from the non-contrast-enhanced acquisition. Automatic segmentation in VNC images was evaluated using the Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD). Automatically determined volumes of the cardiac chambers and LV myocardium in NCCT were compared to reference volumes of the same patient in CCTA by Bland-Altman analysis. An additional independent multi-vendor multi-center set of single-energy NCCT images from 290 patients was used for qualitative analysis, in which two observers graded segmentations on a five-point scale. Results Automatic segmentations in VNC images showed good agreement with reference segmentations, with an average DSC of 0.897 ± 0.034 and an average ASSD of 1.42 ± 0.45 mm. Volume differences [95% confidence interval] between automatic NCCT and reference CCTA segmentations were -19 [-67; 30] mL for LV myocardium, -25 [-78; 29] mL for LV cavity, -29 [-73; 14] mL for right ventricle, -20 [-62; 21] mL for left atrium, and -19 [-73; 34] mL for right atrium, respectively. In 214 (74%) NCCT images from the independent multi-vendor multi-center set, both observers agreed that the automatic segmentation was mostly accurate (grade 3) or better. Conclusion Our automatic method produced accurate whole-heart segmentations in NCCT images using a CNN trained with VNC images from a dual-layer detector CT scanner. This method might enable quantification of additional cardiac measures from NCCT images for improved cardiovascular risk prediction. |
2020 |
S. Bruns, J.M. Wolterink, T.P.W. van den Boogert, J.P. Henriques, J. Baan, R.N. Planken, I. Išgum Automatic whole-heart segmentation in 4D TAVI treatment planning CT Inproceedings SPIE Medical Imaging (in press), 2020. @inproceedings{Bruns2020, title = {Automatic whole-heart segmentation in 4D TAVI treatment planning CT }, author = {S. Bruns, J.M. Wolterink, T.P.W. van den Boogert, J.P. Henriques, J. Baan, R.N. Planken, I. Išgum}, year = {2020}, date = {2020-10-14}, booktitle = {SPIE Medical Imaging (in press)}, abstract = {4D cardiac CT angiography (CCTA) images acquired for transcatheter aortic valve implantation (TAVI) planning provide a wealth of information about the morphology of the heart throughout the cardiac cycle. We propose a deep learning method to automatically segment the cardiac chambers and myocardium in 4D CCTA. We obtain automatic segmentations in 472 patients and use these to automatically identify end-systolic (ES) and end-diastolic (ED) phases, and to determine the left ventricular ejection fraction (LVEF). Our results show that automatic segmentation of cardiac structures through the cardiac cycle is feasible (median Dice similarity coefficient 0.908, median average symmetric surface distance 1.59 mm). Moreover, we demonstrate that these segmentations can be used to accurately identify ES and ED phases (bias [limits of agreement] of 1.81 [-11.0; 14.7]% and -0.02 [-14.1; 14.1]%). Finally, we show that there is correspondence between LVEF values determined from CCTA and echocardiography (-1.71 [-25.0; 21.6]%). Our automatic deep learning approach to segmentation has the potential to routinely extract functional information from 4D CCTA.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } 4D cardiac CT angiography (CCTA) images acquired for transcatheter aortic valve implantation (TAVI) planning provide a wealth of information about the morphology of the heart throughout the cardiac cycle. We propose a deep learning method to automatically segment the cardiac chambers and myocardium in 4D CCTA. We obtain automatic segmentations in 472 patients and use these to automatically identify end-systolic (ES) and end-diastolic (ED) phases, and to determine the left ventricular ejection fraction (LVEF). Our results show that automatic segmentation of cardiac structures through the cardiac cycle is feasible (median Dice similarity coefficient 0.908, median average symmetric surface distance 1.59 mm). Moreover, we demonstrate that these segmentations can be used to accurately identify ES and ED phases (bias [limits of agreement] of 1.81 [-11.0; 14.7]% and -0.02 [-14.1; 14.1]%). Finally, we show that there is correspondence between LVEF values determined from CCTA and echocardiography (-1.71 [-25.0; 21.6]%). Our automatic deep learning approach to segmentation has the potential to routinely extract functional information from 4D CCTA. |
2019 |
S. Bruns, J.M. Wolterink, R.W. van Hamersvelt, M. Zreik, T. Leiner, I. Išgum Improving myocardium segmentation in cardiac CT angiography using spectral information Inproceedings SPIE Medical Imaging, 2019. @inproceedings{Bruns2019, title = {Improving myocardium segmentation in cardiac CT angiography using spectral information}, author = {S. Bruns, J.M. Wolterink, R.W. van Hamersvelt, M. Zreik, T. Leiner, I. Išgum}, url = {https://arxiv.org/abs/1810.03968}, year = {2019}, date = {2019-02-17}, booktitle = {SPIE Medical Imaging}, abstract = {Left ventricle myocardium segmentation in cardiac CT angiography (CCTA) is essential for the assessment of myocardial perfusion. Since deep-learning methods for segmentation in CCTA suffer from differences in contrast-agent attenuation, we propose training a 3D CNN with augmentation using virtual mono-energetic reconstructions from a spectral CT scanner. We compare this with augmentation by linear intensity scaling, and combine both augmentations. We train a network with 10 conventional CCTA images and corresponding virtual mono-energetic images acquired on a spectral CT scanner and evaluate on 40 conventional CCTA images. We show that data augmentation with virtual mono-energetic images significantly improves the segmentation.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Left ventricle myocardium segmentation in cardiac CT angiography (CCTA) is essential for the assessment of myocardial perfusion. Since deep-learning methods for segmentation in CCTA suffer from differences in contrast-agent attenuation, we propose training a 3D CNN with augmentation using virtual mono-energetic reconstructions from a spectral CT scanner. We compare this with augmentation by linear intensity scaling, and combine both augmentations. We train a network with 10 conventional CCTA images and corresponding virtual mono-energetic images acquired on a spectral CT scanner and evaluate on 40 conventional CCTA images. We show that data augmentation with virtual mono-energetic images significantly improves the segmentation. |
2019 |
S. Bruns, J.M. Wolterink, R.W. van Hamersvelt, T. Leiner, I. Išgum CNN-based segmentation of the cardiac chambers and great vessels in non-contrast-enhanced cardiac CT Conference Medical Imaging with Deep Learning. MIDL London, 2019. @conference{Bruns2019b, title = {CNN-based segmentation of the cardiac chambers and great vessels in non-contrast-enhanced cardiac CT}, author = {S. Bruns, J.M. Wolterink, R.W. van Hamersvelt, T. Leiner, I. Išgum}, url = {https://openreview.net/forum?id=SJeqoqAaFV}, year = {2019}, date = {2019-07-08}, booktitle = {Medical Imaging with Deep Learning. MIDL London}, abstract = {Quanti cation of cardiac structures in non-contrast CT (NCCT) could improve cardiovascular risk strati cation. However, setting a manual reference to train a fully convolutional network (FCN) for automatic segmentation of NCCT images is hardly feasible, and an FCN trained on coronary CT angiography (CCTA) images would not generalize to NCCT. Therefore, we propose to train an FCN with virtual non-contrast (VNC) images from a dual-layer detector CT scanner and a reference standard obtained on perfectly aligned CCTA images.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Quanti cation of cardiac structures in non-contrast CT (NCCT) could improve cardiovascular risk strati cation. However, setting a manual reference to train a fully convolutional network (FCN) for automatic segmentation of NCCT images is hardly feasible, and an FCN trained on coronary CT angiography (CCTA) images would not generalize to NCCT. Therefore, we propose to train an FCN with virtual non-contrast (VNC) images from a dual-layer detector CT scanner and a reference standard obtained on perfectly aligned CCTA images. |