Search Results for author: Bram van Ginneken

Found 59 papers, 19 papers with code

Transfer learning from a sparsely annotated dataset of 3D medical images

1 code implementation8 Nov 2023 Gabriel Efrain Humpire-Mamani, Colin Jacobs, Mathias Prokop, Bram van Ginneken, Nikolas Lessmann

A base segmentation model (3D U-Net) was trained on a large and sparsely annotated dataset; its weights were used for transfer learning on four new down-stream segmentation tasks for which a fully annotated dataset was available.

Organ Segmentation Segmentation +1

Kidney abnormality segmentation in thorax-abdomen CT scans

no code implementations6 Sep 2023 Gabriel Efrain Humpire Mamani, Nikolas Lessmann, Ernst Th. Scholten, Mathias Prokop, Colin Jacobs, Bram van Ginneken

Our end-to-end segmentation method was trained on 215 contrast-enhanced thoracic-abdominal CT scans, with half of these scans containing one or more abnormalities.

Segmentation

Lumbar spine segmentation in MR images: a dataset and a public benchmark

1 code implementation21 Jun 2023 Jasper W. van der Graaf, Miranda L. van Hooff, Constantinus F. M. Buckens, Matthieu Rutten, Job L. C. van Susante, Robert Jan Kroeze, Marinus de Kleuver, Bram van Ginneken, Nikolas Lessmann

This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal.

Segmentation

Understanding metric-related pitfalls in image analysis validation

no code implementations3 Feb 2023 Annika Reinke, Minu D. Tizabi, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, A. Emre Kavur, Tim Rädsch, Carole H. Sudre, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Veronika Cheplygina, Jianxu Chen, Evangelia Christodoulou, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Jens Kleesiek, Florian Kofler, Thijs Kooi, Annette Kopp-Schneider, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Michael A. Riegler, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul F. Jäger, Lena Maier-Hein

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.

Domain adaptation strategies for cancer-independent detection of lymph node metastases

no code implementations13 Jul 2022 Péter Bándi, Maschenka Balkenhol, Marcory van Dijk, Bram van Ginneken, Jeroen van der Laak, Geert Litjens

Furthermore, we show the effectiveness of repeated adaptation of networks from one cancer type to another to obtain multi-task metastasis detection networks.

Cancer Metastasis Detection Domain Adaptation

Metrics reloaded: Recommendations for image analysis validation

1 code implementation3 Jun 2022 Lena Maier-Hein, Annika Reinke, Patrick Godau, Minu D. Tizabi, Florian Buettner, Evangelia Christodoulou, Ben Glocker, Fabian Isensee, Jens Kleesiek, Michal Kozubek, Mauricio Reyes, Michael A. Riegler, Manuel Wiesenfarth, A. Emre Kavur, Carole H. Sudre, Michael Baumgartner, Matthias Eisenmann, Doreen Heckmann-Nötzel, Tim Rädsch, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, Matthew Blaschko, M. Jorge Cardoso, Veronika Cheplygina, Beth A. Cimini, Gary S. Collins, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Robert Haase, Daniel A. Hashimoto, Michael M. Hoffman, Merel Huisman, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, Hannes Kenngott, Florian Kofler, Annette Kopp-Schneider, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, Jens Petersen, Nasir Rajpoot, Nicola Rieke, Julio Saez-Rodriguez, Clara I. Sánchez, Shravya Shetty, Maarten van Smeden, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Paul F. Jäger

The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.

Instance Segmentation object-detection +2

Structure and position-aware graph neural network for airway labeling

1 code implementation12 Jan 2022 Weiyi Xie, Colin Jacobs, Jean-Paul Charbonnier, Bram van Ginneken

The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch features are extracted using convolutional neural networks (CNN) and enriched using graph neural networks.

Position

Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow

no code implementations30 Sep 2021 Grzegorz Chlebus, Andrea Schenk, Horst K. Hahn, Bram van Ginneken, Hans Meine

In this work, we propose an uncertainty slice sampling (USS) strategy for semantic segmentation of 3D medical volumes that selects 2D image slices for annotation and compare it with various other strategies.

Active Learning Liver Segmentation +1

Deep Clustering Activation Maps for Emphysema Subtyping

no code implementations1 Jun 2021 Weiyi Xie, Colin Jacobs, Bram van Ginneken

We propose a deep learning clustering method that exploits dense features from a segmentation network for emphysema subtyping from computed tomography (CT) scans.

Clustering Computed Tomography (CT) +1

Common Limitations of Image Processing Metrics: A Picture Story

1 code implementation12 Apr 2021 Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Jianxu Chen, Veronika Cheplygina, Evangelia Christodoulou, Beth Cimini, Gary S. Collins, Sandy Engelhardt, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Peter Hirsch, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, A. Emre Kavur, Hannes Kenngott, Jens Kleesiek, Andreas Kleppe, Sven Kohler, Florian Kofler, Annette Kopp-Schneider, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, M. Alican Noyan, Jens Petersen, Gorkem Polat, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clara I. Sánchez, Julien Schroeter, Anindo Saha, M. Alper Selver, Lalith Sharan, Shravya Shetty, Maarten van Smeden, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul Jäger, Lena Maier-Hein

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.

Instance Segmentation object-detection +2

Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI

1 code implementation23 Sep 2020 Anneke Meyer, Grzegorz Chlebus, Marko Rak, Daniel Schindele, Martin Schostak, Bram van Ginneken, Andrea Schenk, Hans Meine, Horst K. Hahn, Andreas Schreiber, Christian Hansen

Background and Objective: Accurate and reliable segmentation of the prostate gland in MR images can support the clinical assessment of prostate cancer, as well as the planning and monitoring of focal and loco-regional therapeutic interventions.

Hyperparameter Optimization Segmentation

A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

no code implementations2 Aug 2020 S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers

In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.

Uncertainty Quantification

Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans

no code implementations16 Apr 2020 Weiyi Xie, Colin Jacobs, Jean-Paul Charbonnier, Bram van Ginneken

We argue that such structural relationships play a critical role in the accurate delineation of pulmonary lobes when the lungs are affected by diseases such as COVID-19 or COPD.

Transfer Learning

Random smooth gray value transformations for cross modality learning with gray value invariant networks

1 code implementation MIDL 2019 Nikolas Lessmann, Bram van Ginneken

Random transformations are commonly used for augmentation of the training data with the goal of reducing the uniformity of the training samples.

Streaming convolutional neural networks for end-to-end learning with multi-megapixel images

3 code implementations11 Nov 2019 Hans Pinckaers, Bram van Ginneken, Geert Litjens

This method exploits the locality of most operations in modern convolutional neural networks by performing the forward and backward pass on smaller tiles of the image.

Iterative Augmentation of Visual Evidence for Weakly-Supervised Lesion Localization in Deep Interpretability Frameworks: Application to Color Fundus Images

no code implementations16 Oct 2019 Cristina González-Gonzalo, Bart Liefers, Bram van Ginneken, Clara I. Sánchez

We show that the augmented visual evidence of the predictions highlights the biomarkers considered by experts for diagnosis and improves the final localization performance.

mlVIRNET: Multilevel Variational Image Registration Network

no code implementations22 Sep 2019 Alessa Hering, Bram van Ginneken, Stefan Heldmann

We show that the use of a deep learning multilevel approach leads to significantly better registration results.

Image Registration

A deep learning model for segmentation of geographic atrophy to study its long-term natural history

no code implementations15 Aug 2019 Bart Liefers, Johanna M. Colijn, Cristina González-Gonzalo, Timo Verzijden, Paul Mitchell, Carel B. Hoyng, Bram van Ginneken, Caroline C. W. Klaver, Clara I. Sánchez

Participants: 409 CFIs of 238 eyes with GA from the Rotterdam Study (RS) and the Blue Mountain Eye Study (BMES) for model development, and 5, 379 CFIs of 625 eyes from the Age-Related Eye Disease Study (AREDS) for analysis of GA growth rate.

Segmentation

FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis

no code implementations2 Jul 2019 Erdi Çallı, Keelin Murphy, Ecem Sogancioglu, Bram van Ginneken

The method is tested using a held-out test dataset of 21, 176 chest x-rays (in-distribution) and a set of 14, 821 out-of-distribution x-ray images of incorrect orientation or anatomy.

Anatomy

Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration

no code implementations22 Mar 2019 Cristina González-Gonzalo, Verónica Sánchez-Gutiérrez, Paula Hernández-Martínez, Inés Contreras, Yara T. Lechanteur, Artin Domanian, Bram van Ginneken, Clara I. Sánchez

Purpose: To validate the performance of a commercially-available, CE-certified deep learning (DL) system, RetCAD v. 1. 3. 0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of diabetic retinopathy (DR) and age-related macular degeneration (AMD) in color fundus (CF) images on a dataset with mixed presence of eye diseases.

The Liver Tumor Segmentation Benchmark (LiTS)

6 code implementations13 Jan 2019 Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.

Benchmarking Computed Tomography (CT) +3

Context Encoding Chest X-rays

1 code implementation3 Dec 2018 Davide Belli, Shi Hu, Ecem Sogancioglu, Bram van Ginneken

Chest X-rays are one of the most commonly used technologies for medical diagnosis.

Anomaly Detection Image Inpainting +1

GANs for Medical Image Analysis

no code implementations13 Sep 2018 Salome Kazeminia, Christoph Baur, Arjan Kuijper, Bram van Ginneken, Nassir Navab, Shadi Albarqouni, Anirban Mukhopadhyay

Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification.

General Classification

Chest X-ray Inpainting with Deep Generative Models

no code implementations29 Aug 2018 Ecem Sogancioglu, Shi Hu, Davide Belli, Bram van Ginneken

Generative adversarial networks have been successfully applied to inpainting in natural images.

Image Inpainting

Iterative fully convolutional neural networks for automatic vertebra segmentation and identification

1 code implementation12 Apr 2018 Nikolas Lessmann, Bram van Ginneken, Pim A. de Jong, Ivana Išgum

Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities.

Instance Segmentation Segmentation +1

Student Beats the Teacher: Deep Neural Networks for Lateral Ventricles Segmentation in Brain MR

no code implementations15 Jan 2018 Mohsen Ghafoorian, Jonas Teuwen, Rashindra Manniesing, Frank-Erik de Leeuw, Bram van Ginneken, Nico Karssemeijer, Bram Platel

To show this, we use noisy segmentation labels generated by a conventional region growing algorithm to train a deep network for lateral ventricle segmentation.

Segmentation

Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions

no code implementations1 Nov 2017 Nikolas Lessmann, Bram van Ginneken, Majd Zreik, Pim A. de Jong, Bob D. de Vos, Max A. Viergever, Ivana Išgum

On soft filter reconstructions, the method achieved F1 scores of 0. 89, 0. 89, 0. 67, and 0. 55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively.

Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

no code implementations25 Feb 2017 Mohsen Ghafoorian, Alireza Mehrtash, Tina Kapur, Nico Karssemeijer, Elena Marchiori, Mehran Pesteie, Charles R. G. Guttmann, Frank-Erik de Leeuw, Clare M. Tempany, Bram van Ginneken, Andriy Fedorov, Purang Abolmaesumi, Bram Platel, William M. Wells III

In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?

Domain Adaptation Lesion Segmentation +1

The importance of stain normalization in colorectal tissue classification with convolutional networks

1 code implementation20 Feb 2017 Francesco Ciompi, Oscar Geessink, Babak Ehteshami Bejnordi, Gabriel Silva de Souza, Alexi Baidoshvili, Geert Litjens, Bram van Ginneken, Iris Nagtegaal, Jeroen van der Laak

The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image.

Classification General Classification

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