Publications

Jonas Teuwen

2022

  1. Deep MRI reconstruction with radial subsampling
    Medical Imaging 2022: Physics of Medical Imaging
    G. Yiasemis, C. Zhang, C. I. Sánchez, J. Sonke, J. Teuwen, 2022
    doi preprints
    Abstract
  2. Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features
    Radiology
    R. Hou, L. J. Grimm, M. A. Mazurowski, J. R. Marks, L. M. King, C. C. Maley, T. Lynch, M. Van Oirsouw, K. Rogers, N. Stone, M. Wallis, J. Teuwen, J. Wesseling, E. S. Hwang, J. Y. Lo, 2022, 303;(1):54-62
    doi paper (open access)
    Abstract
  3. Exploiting the Dixon Method for a Robust Breast and Fibro-Glandular Tissue Segmentation in Breast MRI
    Diagnostics
    R. Samperna, N. Moriakov, N. Karssemeijer, J. Teuwen, R. M. Mann, 2022, 12;(7):1690
    doi paper (open access)
    Abstract
  4. Deep learning-based breast tissue segmentation in digital mammography: generalization across views and vendors
    Medical Imaging 2022: Image Processing
    S. D. Verboom, M. Caballo, M. J. M. Broeders, J. Teuwen, I. Sechopoulos, 2022
    doi
    Abstract
  5. Tumor tracking in 4D CT images for adaptive radiotherapy
    Medical Imaging 2022: Image Processing
    P. S. Kronemeijer, E. Gavves, J. Sonke, J. Teuwen, 2022
    doi
    Abstract
  6. Mammary duct detection using self-supervised encoders
    Medical Imaging 2022: Computer-Aided Diagnosis
    S. Doyle, F. Dal Canton, J. Wesseling, C. I. Sánchez, J. Teuwen, 2022
    doi
    Abstract
  7. Breast imaging and deep learning: past, present, and future
    Advances in Magnetic Resonance Technology and Applications
    S. Eskreis-Winkler, J. Teuwen, S. Benson, 2022
    doi
    Abstract
  8. Recurrent variational network: A deep learning inverse problem solver applied to the task of accelerated mri reconstruction
    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    G. Yiasemis, J. Sonke, C. Sánchez, J. Teuwen, 2022
    url preprints
    Abstract
  9. Federated learning enables big data for rare cancer boundary detection.
    Nature communications
    S. Pati, U. Baid, B. Edwards, M. Sheller, S. Wang, G. A. Reina, P. Foley, A. Gruzdev, D. Karkada, C. Davatzikos, C. Sako, S. Ghodasara, M. Bilello, S. Mohan, P. Vollmuth, G. Brugnara, C. J. Preetha, F. Sahm, K. Maier-Hein, M. Zenk, M. Bendszus, W. Wick, E. Calabrese, J. Rudie, J. Villanueva-Meyer, S. Cha, M. Ingalhalikar, M. Jadhav, U. Pandey, J. Saini, J. Garrett, M. Larson, R. Jeraj, S. Currie, R. Frood, K. Fatania, R. Y. Huang, K. Chang, C. Balaña, J. Capellades, J. Puig, J. Trenkler, J. Pichler, G. Necker, A. Haunschmidt, S. Meckel, G. Shukla, S. Liem, G. S. Alexander, J. Lombardo, J. D. Palmer, A. E. Flanders, A. P. Dicker, H. I. Sair, C. K. Jones, A. Venkataraman, M. Jiang, T. Y. So, C. Chen, P. A. Heng, Q. Dou, M. Kozubek, F. Lux, J. Michálek, P. Matula, M. Keřkovský, T. Kopřivová, M. Dostál, V. Vybíhal, M. A. Vogelbaum, J. R. Mitchell, J. Farinhas, J. A. Maldjian, C. G. B. Yogananda, M. C. Pinho, D. Reddy, J. Holcomb, B. C. Wagner, B. M. Ellingson, T. F. Cloughesy, C. Raymond, T. Oughourlian, A. Hagiwara, C. Wang, M. To, S. Bhardwaj, C. Chong, M. Agzarian, A. X. Falcão, S. B. Martins, B. C. A. Teixeira, F. Sprenger, D. Menotti, D. R. Lucio, P. Lamontagne, D. Marcus, B. Wiestler, F. Kofler, I. Ezhov, M. Metz, R. Jain, M. Lee, Y. W. Lui, R. Mckinley, J. Slotboom, P. Radojewski, R. Meier, R. Wiest, D. Murcia, E. Fu, R. Haas, J. Thompson, D. R. Ormond, C. Badve, A. E. Sloan, V. Vadmal, K. Waite, R. R. Colen, L. Pei, M. Ak, A. Srinivasan, J. R. Bapuraj, A. Rao, N. Wang, O. Yoshiaki, T. Moritani, S. Turk, J. Lee, S. Prabhudesai, F. Morón, J. Mandel, K. Kamnitsas, B. Glocker, L. V. M. Dixon, M. Williams, P. Zampakis, V. Panagiotopoulos, P. Tsiganos, S. Alexiou, I. Haliassos, E. I. Zacharaki, K. Moustakas, C. Kalogeropoulou, D. M. Kardamakis, Y. S. Choi, S. Lee, J. H. Chang, S. S. Ahn, B. Luo, L. Poisson, N. Wen, P. Tiwari, R. Verma, R. Bareja, I. Yadav, J. Chen, N. Kumar, M. Smits, S. R. Van Der Voort, A. Alafandi, F. Incekara, M. M. J. Wijnenga, G. Kapsas, R. Gahrmann, J. W. Schouten, H. J. Dubbink, A. J. P. E. Vincent, M. J. Van Den Bent, P. J. French, S. Klein, Y. Yuan, S. Sharma, T. Tseng, S. Adabi, S. P. Niclou, O. Keunen, A. Hau, M. Vallières, D. Fortin, M. Lepage, B. Landman, K. Ramadass, K. Xu, S. Chotai, L. B. Chambless, A. Mistry, R. C. Thompson, Y. Gusev, K. Bhuvaneshwar, A. Sayah, C. Bencheqroun, A. Belouali, S. Madhavan, T. C. Booth, A. Chelliah, M. Modat, H. Shuaib, C. Dragos, A. Abayazeed, K. Kolodziej, M. Hill, A. Abbassy, S. Gamal, M. Mekhaimar, M. Qayati, M. Reyes, J. E. Park, J. Yun, H. S. Kim, A. Mahajan, M. Muzi, S. Benson, R. G. H. Beets-Tan, J. Teuwen, A. Herrera-Trujillo, M. Trujillo, W. Escobar, A. Abello, J. Bernal, J. Gómez, J. Choi, S. Baek, Y. Kim, H. Ismael, B. Allen, J. M. Buatti, A. Kotrotsou, H. Li, T. Weiss, M. Weller, A. Bink, B. Pouymayou, H. F. Shaykh, J. Saltz, P. Prasanna, S. Shrestha, K. M. Mani, D. Payne, T. Kurc, E. Pelaez, H. Franco-Maldonado, F. Loayza, S. Quevedo, P. Guevara, E. Torche, C. Mendoza, F. Vera, E. Ríos, E. López, S. A. Velastin, G. Ogbole, M. Soneye, D. Oyekunle, O. Odafe-Oyibotha, B. Osobu, M. Shu'Aibu, A. Dorcas, F. Dako, A. L. Simpson, M. Hamghalam, J. J. Peoples, R. Hu, A. Tran, D. Cutler, F. Y. Moraes, M. A. Boss, J. Gimpel, D. K. Veettil, K. Schmidt, B. Bialecki, S. Marella, C. Price, L. Cimino, C. Apgar, P. Shah, B. Menze, J. S. Barnholtz-Sloan, J. Martin, S. Bakas, 2022, 13;(1):7346
    url
    Abstract
  10. WeakSTIL: weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need
    Medical Imaging 2022: Digital and Computational Pathology
    Y. Schirris, M. Engelaer, A. Panteli, H. M. Horlings, E. Gavves, J. Teuwen, 2022
    doi preprints
    Abstract
  11. Artificial Intelligence for Image Registration in Radiation Oncology.
    Seminars in radiation oncology
    J. Teuwen, Z. A. R. Gouw, J. Sonke, 2022, 32;(4):330-342
    PubMed
    Abstract
  12. Prediction of histological grade and molecular subtypes of invasive breast cancer using mammographic growth rate in screening
    European Journal of Cancer
    J. Peters, N. Moriakov, J. Van Dijck, S. Elias, E. Lips, J. Wesseling, R. Mann, J. Teuwen, M. Caballo, M. Broeders, 2022, 175
    doi paper (open access)
    Abstract
  13. 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
    doi
    Abstract

2021

  1. Subpixel object segmentation using wavelets and multi resolution analysis
    R. Sheombarsing, N. Moriakov, J. Sonke, J. Teuwen, 2021
    ArXiv
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    Radiology
    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
    doi paper (open access)
    Abstract
  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
    doi preprints paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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)
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi preprints paper (open access)
    Abstract
  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
    doi preprints
    Abstract
  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
    url preprints
    Abstract

2020

  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
    ArXiv
    Abstract
  2. Convolutional neural networks
    Handbook of Medical Image Computing and Computer Assisted Intervention
    J. Teuwen, N. Moriakov, 2020
    doi
    Abstract
  3. 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
    doi paper (open access)
    Abstract
  4. Tussen data en theorie
    Tijdschrift voor Toezicht
    J. Goudsmit, J. Teuwen, 2020, 11;(1):48-53
    doi
    Abstract
  5. Kernel of cyclegan as a principal homogeneous space
    International Conference on Learning Representations
    N. Moriakov, J. Adler, J. Teuwen, 2020
    url preprints
    Abstract
  6. 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
    PubMed
    Abstract

2019

  1. i-RIM applied to the fastMRI challenge
    P. Putzky, D. Karkalousos, J. Teuwen, N. Moriakov, B. Bakker, M. Caan, M. Welling, 2019
    ArXiv
    Abstract
  2. Learned SIRT for Cone Beam Computed Tomography Reconstruction
    R. J. Dilz, L. Schröder, N. Moriakov, J. Sonke, J. Teuwen, 2019
    ArXiv
    Abstract
  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
    doi
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi paper (open access)
    Abstract
  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
    doi
    Abstract
  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
    doi
    Abstract
  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
    doi preprints paper (open access)
    Abstract
  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
    doi preprints
    Abstract

2018

  1. On Maximum Focused Electric Energy in Bounded Regions
    J. Teuwen, P. Urbach, 2018
    ArXiv
    Abstract
  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
    doi
    Abstract
  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
    doi preprints
    Abstract
  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
    doi preprints
    Abstract
  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
    doi preprints
    Abstract

2017

  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
    doi preprints
    Abstract
  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
    doi paper (open access)
    Abstract