Marleen de Bruijne
Professor
Image Analysis, Computational Modelling and Geometry
Universitetsparken 1
2100 København Ø
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An end-to-end approach to segmentation in medical images with CNN and posterior-CRF
Chen, S., Sedghi Gamechi, Z., Dubost, F., van Tulder, G. & de Bruijne, Marleen, Feb 2022, In: Medical Image Analysis. 76, p. 1-12 102311.Research output: Contribution to journal › Journal article › Research › peer-review
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Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation
Chen, S., Bortsova, G., Juárez, A. G. U., van Tulder, G. & de Bruijne, Marleen, 1 Jan 2019, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Shen, D., Yap, P-T., Liu, T., Peters, T. M., Khan, A., Staib, L. H., Essert, C. & Zhou, S. (eds.). Springer VS, p. 457-465 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11766 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Three-dimensional ultrasound evaluation of the effects of pomegranate therapy on carotid plaque texture using locality preserving projection
Chen, X., Lin, M., Cui, H., Chen, Y., van Engelen, A., de Bruijne, Marleen, Azarpazhooh, M. R., Sohrevardi, S. M., Chow, T. W. S., Spence, J. D. & Chiu, B., 2020, In: Computer Methods and Programs in Biomedicine. 184, 10 p., 105276.Research output: Contribution to journal › Journal article › Research › peer-review
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Asymmetric similarity-weighted ensembles for image segmentation
Cheplygina, V., Van Opbroek, A., Ikram, M. A., Vernooij, M. W. & de Bruijne, Marleen, 2016, 2016 IEEE International Symposium on Biomedical Imaging: from Nano to Macro. IEEE, p. 273-277 5 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Classification of COPD with multiple instance learning
Cheplygina, V., Sørensen, L. E. B. L., Tax, D., Pedersen, J. J. H., Loog, M. & de Bruijne, Marleen, 2014, 22nd International Conference on Pattern Recognition (ICPR) 2014. IEEE, p. 1508-1513 6 p. (International Conference on Pattern Recognition).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Crowdsourcing airway annotations in chest computed tomography images
Cheplygina, V., Perez-Rovira, A., Kuo, W., Tiddens, H. A. W. M. & de Bruijne, Marleen, 2021, In: PLoS ONE. 16, e0249580.Research output: Contribution to journal › Journal article › Research › peer-review
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Transfer learning for multicenter classification of chronic obstructive pulmonary disease
Cheplygina, V., Peña, I. P., Pedersen, J. J. H., Lynch, D. A., Sørensen, L. & de Bruijne, Marleen, Sep 2018, In: IEEE Journal of Biomedical and Health Informatics. 22, 5, p. 1486 - 1496Research output: Contribution to journal › Journal article › Research › peer-review
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Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Cheplygina, V., de Bruijne, Marleen & Pluim, J. P. W., 29 Mar 2019, In: Medical Image Analysis. 54, p. 280-296 17 p.Research output: Contribution to journal › Journal article › Research › peer-review
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Label stability in multiple instance learning
Cheplygina, V., Sørensen, L., Tax, D. M. J., de Bruijne, Marleen & Loog, M., 2015, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I. Navab, N., Hornegger, J., Wells, W. M. & Frangi, A. F. (eds.). Springer, p. 539-546 8 p. (Lecture notes in computer science).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners
Cheplygina, V., Opbroek, A. V., Ikram, M. A., Vernooij, M. W. & de Bruijne, Marleen, 2020, In: arXiv.org. 17 p.Research output: Contribution to journal › Journal article › Research
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Do you trust your multiple instance learning classifier?
Cheplygina, V., Sørensen, L., Tax, D. M. J., de Bruijne, Marleen & Loog, M., 2017, Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning. Duivesteijn, W., Pechenizkiy, M., Fletcher, G., Menkovski, V., Postma, E., Vanschoren, J. & van der Putten, P. (eds.). p. 72-73 2 p.Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › Research
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Early experiences with crowdsourcing airway annotations in chest CT
Cheplygina, V., Perez-Rovira, A., Kuo, W., Tiddens, H. A. W. M. & de Bruijne, Marleen, 2016, Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings. Carneiro, G., Mateus, D., Peter, L., Bradley, A., Tavares, J. M. R. S., Belagiannis, V., Papa, J. P., Nascimento, J. C., Loog, M., Lu, Z., Cardoso, J. S. & Cornebise, J. (eds.). Springer, p. 209-218 10 p. (Lecture notes in computer science, Vol. 10008).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Automated Quantification of the Morphological Atherosclerotic Calcification Distribution on X-Rays Is a Strong Predictor of Mortality in Postmenopausal Women
Christiansen, C., Karsdal, M., Lauze, Francois Bernard, Dam, E., Ganz, Melanie, de Bruijne, Marleen, Sørensen, M., Barascuk, N. & Nielsen, Mads, 2008, In: Arteriosclerosis, Thrombosis, and Vascular Biology. 6, p. e48-e48 1 p.Research output: Contribution to journal › Conference abstract in journal › Research › peer-review
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Relation between wall shear stress and carotid artery wall thickening MRI versus CFD
Cibis, M., Potters, W. V., Selwaness, M., Gijsen, F. J., Franco, O. H., Arias Lorza, A. M., de Bruijne, Marleen, Hofman, A., van der Lugt, A., Nederveen, A. J. & Wentzel, J. J., 2016, In: Journal of Biomechanics. 49, 5, p. 735-741 7 p.Research output: Contribution to journal › Journal article › Research › peer-review
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MRI tracheomalacia (TM) assessment in pediatric patients: feasibility study
Ciet, P., Wielopolski, P., Lever, S., Manniesing, R., de Bruijne, Marleen, Lequin, M. H. & Tiddens, H. A. W. M., 2011.Research output: Contribution to conference › Conference abstract for conference › Research › peer-review
Chest-CT protocol standardization for multicentre trial in cystic fibrosis (CF) infants
Ciet, P., Graniel, K. G., Stick, S., de Bruijne, Marleen, Tiddens, H. & Straten, M. V., 2013, In: Insights into Imaging. 4, Supplement 1, 1 p., B-0737.Research output: Contribution to journal › Conference abstract in journal › Research › peer-review
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Respiratory muscle movement in Pompe disease using cine Magnetic Resonance Imaging
Ciet, P., Wens, S., Perez-Rovira, A., Logie, K., Wielopolski, P., de Bruijne, Marleen, Ploeg, A. V. D., Doorn, P. V. & Tiddens, H., 2013.Research output: Contribution to conference › Conference abstract for conference › Research › peer-review
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Tracheomalacia assessment in pediatric patients with MRI: preliminary report
Ciet, P., Wielopolski, P. A., Lever, S., de Bruijne, Marleen, Lequin, M. H. & Tiddens, H. A., 2010.Research output: Contribution to conference › Conference abstract for conference › Research › peer-review
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Spirometer-controlled cine magnetic resonance imaging to diagnose tracheobronchomalacia in pediatric patients
Ciet, P., Wielopolski, P., Manniesing, R., Lever, S., de Bruijne, Marleen, Morana, G., Muzzio, P. C., Lequin, M. H. & Tiddens, H. A. W. M., 2014, In: European Respiratory Journal. 43, 1, p. 115-124 10 p.Research output: Contribution to journal › Journal article › Research › peer-review
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Standardization of a chest-CT protocol for multi-center trial in cystic fibrosis (CF) infants
Ciet, P., Graniel, K. G., Stick, S., de Bruijne, Marleen, Tiddens, H. & van Straten, M., 2012, In: Journal of Thoracic Imaging. 27, 5, p. W127 1 p., O47.Research output: Contribution to journal › Conference abstract in journal › Research › peer-review
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Lung tissue classification in severe advanced cystic fibrosis from CT scans
Ciompi, F., Palaioroutas, A., Loeve, M., Pujol, O., Radeva, P., Tiddens, H. & de Bruijne, Marleen, 2011, The Fourth International Workshop on Pulmonary Image Analysis. Beichel, R., de Bruine, M., van Ginneken, B., Kabus, S., Kiraly, A., Kuhnigk, J-M., McClelland, J., Mori, K., van Rikxoort, E. & Rit, S. (eds.). CreateSpace Independent Publishing Platform , p. 57-68 12 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Iterated stacked classifiers for lung segmentation in computed tomography
Ciompi, F., Gatta, C. & de Bruijne, Marleen, 2013, The Fifth International Workshop on Pulmonary Image Analysis: Nagoya, Japan - September 26, 2013. Beichel, R. R., de Bruijne, M., Kabus, S., Kiraly, A. P., Kitasaka, T., Kuhnigk, J-M., McClelland, J. R., van Rikxoort, E. & Rit, S. (eds.). CreateSpace Independent Publishing Platform , p. 63-74 12 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
A pixelwise inpainting-based refinement scheme for quantizing calcification in the lumbar aorta on 2D lateral x-ray images
Conrad-Hansen, L., de Bruijne, Marleen, Lauze, F., Tankó, L. B. & Nielsen, M., 2006, Medical Imaging: Image Processing. Pluim, J. P. W. & Reinhardt, J. M. (eds.). (Proceedings of SPIE; No. 6144).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Quantizing Calcification in the Lumbar Aorta on 2-D Lateral X-Ray Images
Conrad-Hansen, L. A., de Bruijne, Marleen, Lauze, F., Tanko, L. B. & Nielsen, M., 2005, Computer Vision for Biomedical Image Applications: ICCV workshop. <Forlag uden navn>, p. 409-418 (Lecture notes in computer science, Vol. 3765/2005).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Quantifying calcification in the lumbar aorta on X-ray images
Conrad-Hansen, L. A., de Bruijne, Marleen, Lauze, F. B., Tankó, L. B., Pettersen, P. C., He, Q., Chen, J., Christiansen, C. & Nielsen, Mads, 2007, Medical Image Computing and Computer-Assisted Intervention: 10th International conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part II. Ayache, N., Ourselin, S. & Maeder, A. (eds.). Springer, p. 352-359 (Lecture notes in computer science; No. 4792).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Semi-automatic detection of calcifications using nonlinear stretching on X-ray images
Conrad-Hansen, L. A., de Bruijne, Marleen, Nielsen, Mads & Petrou, M., 2007, Medical Imaging 2007: Computer-aided diagnosis, 20-22 February, 2007, San Diego, California, USA. Giger, M. L. & Karssemeijer, N. (eds.). SPIE, 7 p. (Proceedings of SPIE; No. 6514).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Maximum a posteriori estimation of linear shape variation with application to vertebra and cartilage modeling
Crimi, A., Lillholm, Martin, Nielsen, Mads, Ghosh, A., de Bruijne, Marleen, Dam, E. B. & Sporring, Jon, 2011, In: IEEE Transactions on Medical Imaging. 30, 8, p. 1514-1526 13 p.Research output: Contribution to journal › Journal article › Research › peer-review
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Prior knowledge regularization in statistical medical image tasks
Crimi, A., Sporring, Jon, de Bruijne, Marleen, Lillholm, Martin & Nielsen, Mads, 2009, Proceedings of the MICCAI Workshop on Probabilistic Models for Medical Image Analysis. 12 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Shape-based assessment of vertebral fracture risk in postmenopausal women using discriminative shape alignment
Crimi, A., Loog, M., de Bruijne, Marleen, Nielsen, Mads & Lillholm, Martin, 2012, In: Academic Radiology. 19, 4, p. 446-454 9 p.Research output: Contribution to journal › Journal article › Research › peer-review
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Reliability of computer-assisted periacetabular osteotomy using a minimally invasive approach
De Raedt, S., Mechlenburg, I., Stilling, M., Rømer, L., Murphy, R. J., Armand, M., Lepistö, J., de Bruijne, Marleen & Søballe, K., 2018, In: International Journal of Computer Assisted Radiology and Surgery. 13, 12, p. 2021-2028 8 p.Research output: Contribution to journal › Journal article › Research › peer-review
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GP-Unet: Lesion detection from weak labels with a 3D regression network
Dubost, F., Bortsova, G., Adams, H., Ikram, A., Niessen, W. J., Vernooij, M. & de Bruijne, Marleen, 2017, Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III. Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D. L. & Duschesne, S. (eds.). Springer, p. 214-221 8 p. (Lecture notes in computer science, Vol. 10435).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Enlarged perivascular spaces in brain MRI: Automated quantification in four regions
Dubost, F., Yilmaz, P., Adams, H., Bortsova, G., Ikram, M. A., Niessen, W., Vernooij, M. & de Bruijne, Marleen, 2019, In: NeuroImage. 185, p. 534-544Research output: Contribution to journal › Journal article › Research › peer-review
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Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks
Dubost, F., Collery, B., Renaudier, A., Roc, A., Posocco, N., Niessen, W. & de Bruijne, Marleen, 2020, Computational Methods and Clinical Applications for Spine Imaging: 6th International Workshop and Challenge, CSI 2019, Proceedings. Cai, Y., Wang, L., Audette, M., Zheng, G. & Li, S. (eds.). Springer VS, p. 88-94 7 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11963 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI Across Sites
Dubost, F., Dünnwald, M., Huff, D., Scheumann, V., Schreiber, F., Vernooij, M., Niessen, W., Skalej, M., Schreiber, S., Oeltze-Jafra, S. & de Bruijne, Marleen, 2019, OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging - 2nd International Workshop, OR 2.0 2019, and 2nd International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Proceedings. Zhou, L., Sarikaya, D., Kia, S. M., Speidel, S., Malpani, A., Hashimoto, D., Habes, M., Löfstedt, T., Ritter, K. & Wang, H. (eds.). Springer VS, p. 103-111 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11796 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation
Dubost, F., de Bruijne, Marleen, Nardin, M., Dalca, A. V., Donahue, K. L., Giese, A., Etherton, M. R., Wu, O., Groot, M. D., Niessen, W., Vernooij, M., Rost, N. S. & Schirmer, M. D., 2020, In: Medical Image Analysis. 63, 14 p., 101698.Research output: Contribution to journal › Journal article › Research › peer-review
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3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI
Dubost, F., Adams, H., Bortsova, G., Ikram, M. A., Niessen, W., Vernooij, M. & de Bruijne, Marleen, Jan 2019, In: Medical Image Analysis. 51, p. 89-100Research output: Contribution to journal › Journal article › Research › peer-review
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Hydranet: Data augmentation for regression neural networks
Dubost, F., Bortsova, G., Adams, H., Ikram, M. A., Niessen, W., Vernooij, M. & de Bruijne, Marleen, 1 Jan 2019, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Shen, D., Yap, P-T., Liu, T., Peters, T. M., Khan, A., Staib, L. H., Essert, C. & Zhou, S. (eds.). Springer VS, p. 438-446 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11767 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Weakly supervised object detection with 2D and 3D regression neural networks
Dubost, F., Adams, H., Yilmaz, P., Bortsova, G., Tulder, G. V., Ikram, M. A., Niessen, W., Vernooij, M. W. & de Bruijne, Marleen, Oct 2020, In: Medical Image Analysis. 65, 22 p., 101767.Research output: Contribution to journal › Journal article › Research › peer-review
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Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction
Dudurych, I., Garcia-Uceda, A., Petersen, Jens, Du, Y., Vliegenthart, R. & de Bruijne, Marleen, 2023, In: European Radiology. 33, p. 6718–6725Research output: Contribution to journal › Journal article › Research › peer-review
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Bronchial wall parameters on CT in healthy never-smoking, smoking, COPD, and asthma populations: a systematic review and meta-analysis
Dudurych, I., Muiser, S., McVeigh, N., Kerstjens, H. A. M., van den Berge, M., de Bruijne, Marleen & Vliegenthart, R., 2022, In: European Radiology. 32, p. 5308–5318Research output: Contribution to journal › Journal article › Research › peer-review
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Creating a training set for artificial intelligence from initial segmentations of airways
Dudurych, I., Garcia-Uceda, A., Saghir, Zaigham, Tiddens, H. A. W. M., Vliegenthart, R. & de Bruijne, Marleen, 2021, In: European radiology experimental. 5, 1, 7 p., 54.Research output: Contribution to journal › Journal article › Research › peer-review
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Natural Progression of Lung Density Changes in COPD Measured by MSCT: Correlation to Lung Function
Edula, G., Ashraf, H., Lo, P., de Bruijne, Marleen, Dahlbäck, M. & Pedersen, J. H., 2009, In: American Journal of Respiratory and Critical Care Medicine. 1, p. A6215 1 p.Research output: Contribution to journal › Conference abstract in journal › Research › peer-review
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Why is the winner the best?
Eisenmann, M., Reinke, A., Weru, V., Tizabi, M. D., Isensee, F., Adler, T. J., Ali, S., Andrearczyk, V., Aubreville, M., Baid, U., Bakas, S., Balu, N., Bano, S., Bernal, J., Bodenstedt, S., Casella, A., Cheplygina, V., Daum, M., Bruijne, M. D., Depeursinge, A. & 105 others, , 2023, Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023. IEEE Computer Society Press, p. 19955-19966Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Multi-feature-based plaque characterization in ex vivo MRI trained by registration to 3D histology
Engelen, A. V., Niessen, W. J., Klein, S., Groen, H. C., Verhagen, H. J. M., Wentzel, J. J., Lugt, A. V. D. & de Bruijne, Marleen, 2012, In: Physics in Medicine and Biology. 57, 1, p. 241-256 16 p.Research output: Contribution to journal › Journal article › Research › peer-review
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Supervised in-vivo plaque characterization incorporating class label uncertainty
Engelen, A. V., Niessen, W. J., Klein, S., Groen, H. C., Verhagen, H. J. M., Wentzel, J. J., van der Lugt, A. & de Bruijne, Marleen, 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, p. 246-249 4 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Preface
Ernst, F., Heinrich, M., Lellmann, J., Schlaefer, A., Dou, Q. & de Bruijne, Marleen, 2021, e Proceedings of the Fourth International Conference on Medical Imaging with Deep Learning – MIDL 2021. PMLR, p. 1-4 (Proceedings of Machine Learning Research, Vol. 143).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research
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Determinants of Perivascular Spaces in the General Population: A Pooled Cohort Analysis of Individual Participant Data
Evans, T. E., Knol, M. J., Schwingenschuh, P., Wittfeld, K., Hilal, S., Ikram, M. A., Dubost, F., Van Wijnen, K. M. H., Katschnig, P., Yilmaz, P., de Bruijne, Marleen, Habes, M., Chen, C., Langer, S., Völzke, H., Ikram, M. K., Grabe, H. J., Schmidt, R., Adams, H. H. H. & Vernooij, M. W., 2023, In: Neurology. 100, 2, p. E107-E122Research output: Contribution to journal › Journal article › Research › peer-review
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Geometric tree kernels: classification of COPD from airway tree geometry
Feragen, A., Petersen, Jens, Grimm, D., Dirksen, A., Pedersen, J. J. H., Borgwardt, K. & de Bruijne, Marleen, 2013, Information Processing in Medical Imaging: 23rd International Conference, IPMI 2013, Asilomar, CA, USA, June 28–July 3, 2013. Proceedings. Gee, J. C., Joshi, S., Pohl, K. M., Wells, W. M. & Zöllei, L. (eds.). Springer, p. 171-183 13 p. (Lecture notes in computer science, Vol. 7917).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Toward a theory of statistical tree-shape analysis
Feragen, A., Lo, P. C. P., de Bruijne, Marleen, Nielsen, Mads & Lauze, Francois Bernard, 2013, In: I E E E Transactions on Pattern Analysis and Machine Intelligence. 35, 8, p. 2008-2021 14 p.Research output: Contribution to journal › Journal article › Research › peer-review
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A hierarchical scheme for geodesic anatomical labeling of airway trees
Feragen, A., Petersen, Jens, Owen, M., Lo, P. C. P., Thomsen, L., Wille, M. M. W., Dirksen, A. & de Bruijne, Marleen, 2012, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012: 15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part III. Ayache, N., Delingette, H., Golland, P. & Mori, K. (eds.). Springer, p. 147-155 9 p. (Lecture notes in computer science, Vol. 7512).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
ID: 545222
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Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge
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Atherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty
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Distribution, size, shape, growth potential and extent of abdominal aortic calcified deposits predict mortality in postmenopausal women
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