Jonas Teuwen


  1. Auto-Segmentation of Oropharyngeal Cancer Primary Tumors Using Multiparametric MRI-Based Deep Learning
    International Journal of Radiation Oncology*Biology*Physics
    K. Wahid, S. Ahmed, R. He, L. Van Dijk, J. Teuwen, B. Mcdonald, V. Salama, A. Mohamed, T. Salzillo, C. Dede, N. Taku, S. Lai, C. Fuller, M. Naser, 2022, 112;(5):e31


  1. Subpixel object segmentation using wavelets and multi resolution analysis
    R. Sheombarsing, N. Moriakov, J. Sonke, J. Teuwen, 2021
  2. Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study
    European Radiology
    S. L. Van Winkel, A. Rodríguez-Ruiz, L. Appelman, A. Gubern-Mérida, N. Karssemeijer, J. Teuwen, A. J. T. Wanders, I. Sechopoulos, R. M. Mann, 2021, 31;(11):8682-8691
  3. Deep learning model for automatic contouring of cardiovascular substructures on radiotherapy planning CT images: Dosimetric validation and reader study based clinical acceptability testing
    Radiotherapy and Oncology
    M. Garrett Fernandes, J. Bussink, B. Stam, R. Wijsman, D. A. Schinagl, R. Monshouwer, J. Teuwen, 2021, 165
  4. Diffusion weighted imaging for evaluation of breast lesions: Comparison between high b-value single-shot and routine readout-segmented sequences at 3 T
    Magnetic Resonance Imaging
    W. B. Sanderink, J. Teuwen, L. Appelman, L. Moy, L. Heacock, E. Weiland, I. Sechopoulos, R. M. Mann, 2021, 84
  5. Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge
    Machine Learning for Medical Image Reconstruction
    P. M. Johnson, G. Jeong, K. Hammernik, J. Schlemper, C. Qin, J. Duan, D. Rueckert, J. Lee, N. Pezzotti, E. De Weerdt, S. Yousefi, M. S. Elmahdy, J. H. F. Van Gemert, C. Schülke, M. Doneva, T. Nielsen, S. Kastryulin, B. P. F. Lelieveldt, M. J. P. Van Osch, M. Staring, E. Z. Chen, P. Wang, X. Chen, T. Chen, V. M. Patel, S. Sun, H. Shin, Y. Jun, T. Eo, S. Kim, T. Kim, D. Hwang, P. Putzky, D. Karkalousos, J. Teuwen, N. Moriakov, B. Bakker, M. Caan, M. Welling, M. J. Muckley, F. Knoll, 2021
  6. Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art
    Seminars in Cancer Biology
    I. Sechopoulos, J. Teuwen, R. Mann, 2021, 72
  7. Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence
    N. Lessmann, C. I. Sánchez, L. Beenen, L. H. Boulogne, M. Brink, E. Calli, J. Charbonnier, T. Dofferhoff, W. M. Van Everdingen, P. K. Gerke, B. Geurts, H. A. Gietema, M. Groeneveld, L. Van Harten, N. Hendrix, W. Hendrix, H. J. Huisman, I. Išgum, C. Jacobs, R. Kluge, M. Kok, J. Krdzalic, B. Lassen-Schmidt, K. Van Leeuwen, J. Meakin, M. Overkamp, T. Van Rees Vellinga, E. M. Van Rikxoort, R. Samperna, C. Schaefer-Prokop, S. Schalekamp, E. T. Scholten, C. Sital, J. L. Stöger, J. Teuwen, K. V. Venkadesh, C. De Vente, M. Vermaat, W. Xie, B. De Wilde, M. Prokop, B. Van Ginneken, 2021, 298;(1):E18-E28
  8. Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation
    Medical Image Analysis
    J. Teuwen, N. Moriakov, C. Fedon, M. Caballo, I. Reiser, P. Bakic, E. García, O. Diaz, K. Michielsen, I. Sechopoulos, 2021, 71
  9. Comparison of simultaneous multi-slice single-shot DWI to readout-segmented DWI for evaluation of breast lesions at 3T MRI
    European Journal of Radiology
    W. B. Sanderink, J. Teuwen, L. Appelman, L. Moy, L. Heacock, E. Weiland, N. Karssemeijer, P. A. Baltzer, I. Sechopoulos, R. M. Mann, 2021, 138
  10. Computer-aided diagnosis of masses in breast computed tomography imaging: deep learning model with combined handcrafted and convolutional radiomic features
    Journal of Medical Imaging
    M. Caballo, A. M. Hernandez, S. H. Lyu, J. Teuwen, R. M. Mann, B. Van Ginneken, J. M. Boone, I. Sechopoulos, 2021, 8;(02)
  11. Oropharyngeal Tumour Segmentation Using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge
    Head and Neck Tumor Segmentation
    C. Rao, S. Pai, I. Hadzic, I. Zhovannik, D. Bontempi, A. Dekker, J. Teuwen, A. Traverso, 2021
  12. Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
    IEEE Transactions on Medical Imaging
    M. J. Muckley, B. Riemenschneider, A. Radmanesh, S. Kim, G. Jeong, J. Ko, Y. Jun, H. Shin, D. Hwang, M. Mostapha, S. Arberet, D. Nickel, Z. Ramzi, P. Ciuciu, J. Starck, J. Teuwen, D. Karkalousos, C. Zhang, A. Sriram, Z. Huang, N. Yakubova, Y. W. Lui, F. Knoll, 2021, 40;(9):2306-2317
  13. Automatic breast lesion detection in ultrafast DCE‐MRI using deep learning
    Medical Physics
    F. Ayatollahi, S. B. Shokouhi, R. M. Mann, J. Teuwen, 2021, 48;(10):5897-5907
  14. Sparse-shot learning with exclusive cross-entropy for extremely many localisations
    Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)
    A. Panteli, J. Teuwen, H. Horlings, E. Gavves, 2021


  1. Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis
    6th International Conference on Image Formation in X-Ray Computed Tomography (CT-meeting)
    K. Michielsen, N. Moriakov, J. Teuwen, I. Sechopoulos, 2020
  2. A Deep Learning-based correction to EPID dosimetry for attenuation and scatter in the Unity MR-Linac system
    Physica Medica
    I. Olaciregui-Ruiz, I. Torres-Xirau, J. Teuwen, U. A. Van Der Heide, A. Mans, 2020, 71
  3. Convolutional neural networks
    Handbook of Medical Image Computing and Computer Assisted Intervention
    J. Teuwen, N. Moriakov, 2020
  4. Machine learning-based classification of viewing behavior using a wide range of statistical oculomotor features
    Journal of Vision
    T. Kootstra, J. Teuwen, J. Goudsmit, T. Nijboer, M. Dodd, S. Van Der Stigchel, 2020, 20;(9):1
  5. Tussen data en theorie
    Tijdschrift voor Toezicht
    J. Goudsmit, J. Teuwen, 2020, 11;(1):48-53
  6. Kernel of cyclegan as a principal homogeneous space
    International Conference on Learning Representations
    N. Moriakov, J. Adler, J. Teuwen, 2020


  1. i-RIM applied to the fastMRI challenge
    P. Putzky, D. Karkalousos, J. Teuwen, N. Moriakov, B. Bakker, M. Caan, M. Welling, 2019
  2. Learned SIRT for Cone Beam Computed Tomography Reconstruction
    R. J. Dilz, L. Schröder, N. Moriakov, J. Sonke, J. Teuwen, 2019
  3. Artificial Intelligence–Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI
    Investigative Radiology
    M. U. Dalmiş, A. Gubern-Mérida, S. Vreemann, P. Bult, N. Karssemeijer, R. Mann, J. Teuwen, 2019, 54;(6):325-332
  4. State-of-the-Art Deep Learning in Cardiovascular Image Analysis
    JACC: Cardiovascular Imaging
    G. Litjens, F. Ciompi, J. M. Wolterink, B. D. De Vos, T. Leiner, J. Teuwen, I. Išgum, 2019, 12;(8):1549-1565
  5. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study
    European Radiology
    A. Rodriguez-Ruiz, K. Lång, A. Gubern-Merida, J. Teuwen, M. Broeders, G. Gennaro, P. Clauser, T. H. Helbich, M. Chevalier, T. Mertelmeier, M. G. Wallis, I. Andersson, S. Zackrisson, I. Sechopoulos, R. M. Mann, 2019, 29;(9):4825-4832
  6. MemCNN: A Python/PyTorch package for creating memory-efficient invertible neural networks
    Journal of Open Source Software
    S. C. Van De Leemput, J. Teuwen, B. Van Ginneken, R. Manniesing, 2019, 4;(39):1576
  7. Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images
    Medical Imaging 2019: Computer-Aided Diagnosis
    M. Caballo, J. Teuwen, R. Mann, I. Sechopoulos, 2019
  8. Differentiating benign and malignant mass and non-mass lesions in breast DCE-MRI using normalized frequency-based features
    International Journal of Computer Assisted Radiology and Surgery
    F. Ayatollahi, S. B. Shokouhi, J. Teuwen, 2019, 15;(2):297-307
  9. Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation
    Medical Imaging 2019: Computer-Aided Diagnosis
    J. Teuwen, N. Moriakov, R. Mann, E. Marchiori, J. Van Vugt, A. Gubern-Mérida, 2019
  10. Deep learning framework for digital breast tomosynthesis reconstruction
    Medical Imaging 2019: Physics of Medical Imaging
    N. Moriakov, K. Michielsen, J. Adler, R. Mann, I. Sechopoulos, J. Teuwen, 2019


  1. On Maximum Focused Electric Energy in Bounded Regions
    J. Teuwen, P. Urbach, 2018
  2. Pectoral muscle segmentation in breast tomosynthesis with deep learning
    Medical Imaging 2018: Computer-Aided Diagnosis
    A. Rodriguez-Ruiz, J. Teuwen, K. Chung, N. Karssemeijer, M. Chevalier, A. Gubern-Mérida, I. Sechopoulos, 2018
  3. Automated lesion detection and segmentation in digital mammography using a u-net deep learning network
    14th International Workshop on Breast Imaging (IWBI 2018)
    T. De Moor, A. Rodriguez-Ruiz, R. Mann, A. Gubern Mérida, J. Teuwen, 2018
  4. Student beats the teacher: deep neural networks for lateral ventricles segmentation in brain MR
    Medical Imaging 2018: Image Processing
    J. Teuwen, M. Ghafoorian, R. Manniesing, F. De Leeuw, N. Karssemeijer, B. Van Ginneken, B. Platel, 2018
  5. Improving Breast Cancer Detection Using Symmetry Information with Deep Learning
    Image Analysis for Moving Organ, Breast, and Thoracic Images
    Y. Brhane Hagos, A. Gubern Mérida, J. Teuwen, 2018


  1. $L^p-L^q$- off-diagonal estimates for the Ornstein-Uhlenbeck semigroup: Some positive and negative results
    Bulletin of the Australian Mathematical Society
    A. Amenta, J. Teuwen, 2017, 96;(1):154-161
  2. New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers
    Acta Radiologica
    A. Rodriguez-Ruiz, J. Teuwen, S. Vreemann, R. W. Bouwman, R. E. Van Engen, N. Karssemeijer, R. M. Mann, A. Gubern-Merida, I. Sechopoulos, 2017, 59;(9):1051-1059


  1. On the integral kernels of derivatives of the Ornstein–Uhlenbeck semigroup
    Infinite Dimensional Analysis, Quantum Probability and Related Topics
    J. Teuwen, 2016, 19;(04):1650030
  2. Shedding new light on Gaussian harmonic analysis
    J. Teuwen, 2016


  1. A note on Gaussian maximal functions
    Indagationes Mathematicae
    J. Teuwen, 2015, 26;(1):106-112