Dr. Jonas Teuwen Profile

Dr. Jonas Teuwen

Group Leader at Netherlands Cancer Institute

SPIE Involvement:

Conference Program Committee | Author

Publications (15)

Proceedings Article | 2 April 2024 Presentation + Paper

Presentation + Paper

Optimizing CycleGAN design for CBCT-to-CT translation: insights into 2D vs 3D modeling, patch size, and the need for tailored evaluation metrics

Ibrahim Hadzic, Suraj Pai, Vicki Trier Taasti, Dennis Bontempi, Ivan Zhovannik, Richard Canters, Jan Jakob Sonke, Andre Dekker, Jonas Teuwen, Alberto Traverso

Proceedings Volume 12926, 1292608 (2024) https://doi.org/10.1117/12.3007031

KEYWORDS: Cone beam computed tomography, 3D modeling, Computed tomography, Education and training, Data modeling, Anatomy, Visualization, Medical imaging, Visual inspection, Performance modeling

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SPIE Journal Paper | 28 December 2023 Open Access

Open Access

Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors

Sarah D. Verboom, Marco Caballo, Jim Peters, Jessie Gommers, Daan van den Oever, Mireille J. M. Broeders, Jonas Teuwen, Ioannis Sechopoulos

JMI, Vol. 11, Issue 01, 014001, (December 2023) https://doi.org/10.1117/12.10.1117/1.JMI.11.1.014001

KEYWORDS: Image segmentation, Education and training, Muscles, Image processing, Breast, Mammography, Data modeling, Atomic force microscopy, Deep learning, Digital mammography

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Poster + Presentation + Paper

Deep MRI reconstruction with radial subsampling

George Yiasemis, Chaoping Zhang, Clara Sánchez, Jan-Jakob Sonke, Jonas Teuwen

Proceedings Volume 12031, 1203136 (2022) https://doi.org/10.1117/12.2609876

KEYWORDS: Magnetic resonance imaging, Data acquisition, Brain, Visualization, Neuroimaging, Image restoration, Medical image reconstruction, Inverse problem on medical image

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Proceedings Article | 4 April 2022 Poster + Paper

Poster + Paper

Development and validation of a clinical CT system simulator

S. A. Tunissen, L. Oostveen, N. Moriakov, J. Teuwen, K. Michielsen, E. Smit, I. Sechopoulos

Proceedings Volume 12031, 120312G (2022) https://doi.org/10.1117/12.2611323

KEYWORDS: Computed tomography, Computer simulations

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Proceedings Article | 4 April 2022 Presentation + Paper

Presentation + Paper

WeakSTIL: weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need

Yoni Schirris, Mendel Engelaer, Andreas Panteli, Hugo Mark Horlings, Efstratios Gavves, Jonas Teuwen

Proceedings Volume 12039, 120390B (2022) https://doi.org/10.1117/12.2611528

KEYWORDS: Machine learning, Machine vision, Tumors, Tissues, Breast cancer, Visualization, Medical imaging, Neural networks, Feature extraction

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Proceedings Article | 4 April 2022 Presentation + Paper

Presentation + Paper

Tumor tracking in 4D CT images for adaptive radiotherapy

Pieter Kronemeijer, Efstratios Gavves, Jan-Jakob Sonke, Jonas Teuwen

Proceedings Volume 12032, 1203209 (2022) https://doi.org/10.1117/12.2612954

KEYWORDS: Tumors, Image segmentation, Radiotherapy, 3D modeling, Computed tomography, 4D CT imaging, Computer programming, Performance modeling, Magnetic resonance imaging, Lung cancer

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Proceedings Article | 4 April 2022 Presentation + Paper

Presentation + Paper

Mammary duct detection using self-supervised encoders

Shannon Doyle, Francesco Dal Canton, Jelle Wesseling, Clara Sánchez, Jonas Teuwen

Proceedings Volume 12033, 120330U (2022) https://doi.org/10.1117/12.2612838

KEYWORDS: Computer programming, Image classification, Tissues, Breast, Scanners, Medical imaging

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Ductal Carcinoma in Situ (DCIS) constitutes 20–25% of all diagnosed breast cancers and is a well known potential precursor for invasive breast cancer.1 The gold standard method for diagnosing DCIS involves the detection of calcifications and abnormal cell proliferation in mammary ducts in Hematoxylin and Eosin (H&E) stained whole-slide images (WSIs). Automatic duct detection may facilitate this task as well as downstream applications that currently require tedious, manual annotation of ducts. Examples of these are grading of DCIS lesions2 and prediction of local recurrence of DCIS.3 Several methods have been developed for object detection in the field of deep learning. Such models are typically initialised using ImageNet transfer-learning features, as the limited availability of annotated medical images has hindered the creation of domain-specific encoders. Novel techniques such as self-supervised learning (SSL) promise to overcome this problem by utilising unlabelled data to learn feature encoders. SSL encoders trained on unlabelled ImageNet have demonstrated SSL’s capacity to produce meaningful representations, scoring higher than supervised features on the ImageNet 1% classification task.4 In the domain of histopathology, feature encoders (Histo encoders) have been developed.5, 6 In classification experiments with linear regression, frozen features of these encoders outperformed those of ImageNet encoders. However, when models initialised with histopathology and ImageNet encoders were fine-tuned on the same classification tasks, there were no differences in performance between the encoders.5, 6 Furthermore, the transferability of SSL encodings to object detection is poorly understood.4 These findings show that more research is needed to develop training strategies for SSL encoders that can enhance performance in relevant downstream tasks. In our study, we investigated whether current state-of-the-art SSL methods can provide model initialisations that outperform ImageNet pre-training on the task of duct detection in WSIs of breast tissue resections. We compared the performance of these SSL-based histopathology encodings (Histo-SSL) with ImageNet pre-training (supervised and self-supervised) and training from scratch. Additionally, we compared the performance of our Histo-SSL encodings with published Histo encoders by Ciga5 and Mormont6 on the same task.

Proceedings Article | 4 April 2022 Poster + Paper

Poster + Paper

Deep learning-based breast tissue segmentation in digital mammography: generalization across views and vendors

Sarah Verboom, Marco Caballo, Mireille J. Broeders, Jonas Teuwen, Ioannis Sechopoulos

Proceedings Volume 12032, 1203236 (2022) https://doi.org/10.1117/12.2611437

KEYWORDS: Image segmentation, Breast, Mammography, Image processing, Digital mammography, Convolutional neural networks

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SPIE Journal Paper | 29 March 2021

Computer-aided diagnosis of masses in breast computed tomography imaging: deep learning model with combined handcrafted and convolutional radiomic features

Marco Caballo, Andrew Hernandez, Su Hyun Lyu, Jonas Teuwen, Ritse Mann, Bram van Ginneken, John Boone, Ioannis Sechopoulos

JMI, Vol. 8, Issue 02, 024501, (March 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.2.024501

KEYWORDS: Computer aided diagnosis and therapy, Breast, Image segmentation, Feature extraction, Mammography, Computed tomography, Computing systems, 3D modeling, Solid modeling, 3D image processing

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Proceedings Article | 13 March 2019 Presentation + Paper

Presentation + Paper

Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation

Joris van Vugt, Elena Marchiori, Ritse Mann, Albert Gubern-Mérida, Nikita Moriakov, Jonas Teuwen

Proceedings Volume 10950, 1095002 (2019) https://doi.org/10.1117/12.2512940

KEYWORDS: Mammography, Data modeling, Breast, Digital mammography, Machine learning, Diagnostics, Breast cancer

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Proceedings Article | 13 March 2019 Paper

Paper

Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images

Marco Caballo, Jonas Teuwen, Ritse Mann, Ioannis Sechopoulos

Proceedings Volume 10950, 109500J (2019) https://doi.org/10.1117/12.2511718

KEYWORDS: Breast, Computed tomography, Neural networks, Feature extraction, Image classification, Statistical analysis, Pattern recognition

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Proceedings Article | 1 March 2019 Presentation + Paper

Presentation + Paper

Deep learning framework for digital breast tomosynthesis reconstruction

Nikita Moriakov, Koen Michielsen, Jonas Adler, Ritse Mann, Ioannis Sechopoulos, Jonas Teuwen

Proceedings Volume 10948, 1094804 (2019) https://doi.org/10.1117/12.2512912

KEYWORDS: Reconstruction algorithms, Breast, Digital breast tomosynthesis, Neural networks

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Proceedings Article | 6 July 2018 Paper

Paper

Automated lesion detection and segmentation in digital mammography using a u-net deep learning network

Timothy de Moor, Alejandro Rodriguez-Ruiz, Albert Gubern Mérida, Ritse Mann, Jonas Teuwen

Proceedings Volume 10718, 1071805 (2018) https://doi.org/10.1117/12.2318326

KEYWORDS: Image segmentation, Digital mammography, Tumor growth modeling, Mammography, Computer-aided diagnosis, Breast cancer

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Proceedings Article | 2 March 2018 Paper

Paper

Student beats the teacher: deep neural networks for lateral ventricles segmentation in brain MR

Mohsen Ghafoorian, Jonas Teuwen, Rashindra Manniesing, Frank-Erik Leeuw, Bram van Ginneken, Nico Karssemeijer, Bram Platel

Proceedings Volume 10574, 105742U (2018) https://doi.org/10.1117/12.2293569

KEYWORDS: Image segmentation, Neural networks, Brain, Magnetic resonance imaging, Neuroimaging, Medical imaging, Image processing algorithms and systems, Network architectures, Brain diseases, Dementia

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Proceedings Article | 27 February 2018 Paper

Paper

Pectoral muscle segmentation in breast tomosynthesis with deep learning

Alejandro Rodriguez-Ruiz, Jonas Teuwen, Kaman Chung, Nico Karssemeijer, Margarita Chevalier, Albert Gubern-Merida, Ioannis Sechopoulos

Proceedings Volume 10575, 105752J (2018) https://doi.org/10.1117/12.2292920

KEYWORDS: Image segmentation, Digital breast tomosynthesis, Breast, Mammography, Systems modeling, Detection and tracking algorithms, Tissues, Convolutional neural networks, 3D modeling, Image processing algorithms and systems

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Showing 5 of 15 publications

Conference Committee Involvement (3)

Computer-Aided Diagnosis

19 February 2024 | San Diego, California, United States

Computer-Aided Diagnosis

20 February 2023 | San Diego, California, United States

Computer-Aided Diagnosis

21 February 2022 | San Diego, California, United States

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