Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Mauricio Reyes

Mauricio Reyes

Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland

Common Limitations of Image Processing Metrics: A Picture Story


Apr 13, 2021
Annika Reinke, Matthias Eisenmann, Minu D. Tizabi, Carole H. Sudre, Tim Rädsch, Michela Antonelli, Tal Arbel, Spyridon Bakas, M. Jorge Cardoso, Veronika Cheplygina, Keyvan Farahani, Ben Glocker, Doreen Heckmann-Nötzel, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Jens Kleesiek, Tahsin Kurc, Michal Kozubek, Bennett A. Landman, Geert Litjens, Klaus Maier-Hein, Bjoern Menze, Henning Müller, Jens Petersen, Mauricio Reyes, Nicola Rieke, Bram Stieltjes, Ronald M. Summers, Sotirios A. Tsaftaris, Bram van Ginneken, Annette Kopp-Schneider, Paul Jäger, Lena Maier-Hein

Figure 1 for Common Limitations of Image Processing Metrics: A Picture Story
Figure 2 for Common Limitations of Image Processing Metrics: A Picture Story
Figure 3 for Common Limitations of Image Processing Metrics: A Picture Story
Figure 4 for Common Limitations of Image Processing Metrics: A Picture Story
* This is a dynamic paper on limitations of commonly used metrics. The current version discusses segmentation metrics only, while future versions will also include metrics for classification and detection. For missing use cases, comments or questions, please contact [email protected] or [email protected]. Substantial contributions to this document will be acknowledged with a co-authorship 
  Access Paper or Ask Questions

Combining unsupervised and supervised learning for predicting the final stroke lesion


Jan 02, 2021
Adriano Pinto, Sérgio Pereira, Raphael Meier, Roland Wiest, Victor Alves, Mauricio Reyes, Carlos A. Silva

Figure 1 for Combining unsupervised and supervised learning for predicting the final stroke lesion
Figure 2 for Combining unsupervised and supervised learning for predicting the final stroke lesion
Figure 3 for Combining unsupervised and supervised learning for predicting the final stroke lesion
Figure 4 for Combining unsupervised and supervised learning for predicting the final stroke lesion
* Accepted at Medical Image Analysis (MedIA) 
  Access Paper or Ask Questions

pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis


Oct 07, 2020
Alain Jungo, Olivier Scheidegger, Mauricio Reyes, Fabian Balsiger

Figure 1 for pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis
Figure 2 for pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis
Figure 3 for pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis
Figure 4 for pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis
* first and last author contributed equally 
  Access Paper or Ask Questions

Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks


Aug 10, 2020
Fabian Balsiger, Alain Jungo, Olivier Scheidegger, Benjamin Marty, Mauricio Reyes

Figure 1 for Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks
Figure 2 for Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks
Figure 3 for Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks
Figure 4 for Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks
* Accepted at MICCAI MLMIR 2020 
  Access Paper or Ask Questions

Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting


Nov 09, 2019
Fabian Balsiger, Alain Jungo, Olivier Scheidegger, Pierre G. Carlier, Mauricio Reyes, Benjamin Marty

Figure 1 for Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting
Figure 2 for Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting
Figure 3 for Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting
Figure 4 for Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting

  Access Paper or Ask Questions

Dual-Stream Pyramid Registration Network


Sep 26, 2019
Xiaojun Hu, Miao Kang, Weilin Huang, Matthew R. Scott, Roland Wiest, Mauricio Reyes

Figure 1 for Dual-Stream Pyramid Registration Network
Figure 2 for Dual-Stream Pyramid Registration Network
Figure 3 for Dual-Stream Pyramid Registration Network
Figure 4 for Dual-Stream Pyramid Registration Network
* To appear in MICCAI 2019 (Oral) 
  Access Paper or Ask Questions

Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation


Jul 07, 2019
Alain Jungo, Mauricio Reyes

Figure 1 for Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation
Figure 2 for Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation
* Appears in Medical Image Computing and Computer Assisted Interventions (MICCAI), 2019 
  Access Paper or Ask Questions

Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation


Jun 05, 2019
Fabian Balsiger, Yannick Soom, Olivier Scheidegger, Mauricio Reyes

Figure 1 for Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation
Figure 2 for Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation
Figure 3 for Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation
Figure 4 for Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation
* Accepted at MICCAI 2019 
  Access Paper or Ask Questions

Informative sample generation using class aware generative adversarial networks for classification of chest Xrays


Apr 30, 2019
Behzad Bozorgtabar, Dwarikanath Mahapatra, Hendrik von Teng, Alexander Pollinger, Lukas Ebner, Jean-Phillipe Thiran, Mauricio Reyes

Figure 1 for Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Figure 2 for Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Figure 3 for Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Figure 4 for Informative sample generation using class aware generative adversarial networks for classification of chest Xrays

  Access Paper or Ask Questions

Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence


Apr 05, 2019
Richard McKinley, Lorenz Grunder, Rik Wepfer, Fabian Aschwanden, Tim Fischer, Christoph Friedli, Raphaela Muri, Christian Rummel, Rajeev Verma, Christian Weisstanner, Mauricio Reyes, Anke Salmen, Andrew Chan, Roland Wiest, Franca Wagner

Figure 1 for Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence
Figure 2 for Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence
Figure 3 for Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence
Figure 4 for Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence

  Access Paper or Ask Questions

Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks


Apr 04, 2019
Richard McKinley, Michael Rebsamen, Raphael Meier, Mauricio Reyes, Christian Rummel, Roland Wiest

Figure 1 for Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks
Figure 2 for Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks
Figure 3 for Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks
Figure 4 for Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks

  Access Paper or Ask Questions

Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks


Jan 22, 2019
Richard McKinley, Rik Wepfer, Fabian Aschwanden, Lorenz Grunder, Raphaela Muri, Christian Rummel, Rajeev Verma, Christian Weisstanner, Mauricio Reyes, Anke Salmen, Andrew Chan, Franca Wagner, Roland Wiest

Figure 1 for Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks
Figure 2 for Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks
Figure 3 for Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks
Figure 4 for Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks

  Access Paper or Ask Questions

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction


Nov 12, 2018
Yannick Suter, Alain Jungo, Michael Rebsamen, Urspeter Knecht, Evelyn Herrmann, Roland Wiest, Mauricio Reyes

Figure 1 for Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Figure 2 for Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Figure 3 for Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Figure 4 for Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
* Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction task 
  Access Paper or Ask Questions

Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment


Sep 25, 2018
Sergio Pereira, Raphael Meier, Victor Alves, Mauricio Reyes, Carlos A. Silva

Figure 1 for Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment
Figure 2 for Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment
* Accepted and presented at iMIMIC - Workshop on Interpretability of Machine Intelligence in Medical Image Computing 
  Access Paper or Ask Questions

Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks


Jul 24, 2018
Fabian Balsiger, Amaresha Shridhar Konar, Shivaprasad Chikop, Vimal Chandran, Olivier Scheidegger, Sairam Geethanath, Mauricio Reyes

Figure 1 for Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks
Figure 2 for Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks
Figure 3 for Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks
Figure 4 for Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks
* Accepted for Machine Learning for Medical Image Reconstruction (MLMIR) workshop at MICCAI 2018. The revision corrects Amaresha's last name and Section 2.1 (scanner type and flip angles) 
  Access Paper or Ask Questions

Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network


Jun 22, 2018
Dwarikanath Mahapatra, Behzad Bozorgtabar, Jean-Philippe Thiran, Mauricio Reyes

Figure 1 for Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network
Figure 2 for Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network
Figure 3 for Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network
Figure 4 for Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network
* Accepted to MICCAI 2018 
  Access Paper or Ask Questions

Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction


Jun 12, 2018
Adriano Pinto, Sergio Pereira, Raphael Meier, Victor Alves, Roland Wiest, Carlos A. Silva, Mauricio Reyes

Figure 1 for Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
Figure 2 for Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
Figure 3 for Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
Figure 4 for Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
* Accepted at MICCAI 2018 
  Access Paper or Ask Questions

Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation


Jun 08, 2018
Alain Jungo, Raphael Meier, Ekin Ermis, Evelyn Herrmann, Mauricio Reyes

Figure 1 for Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation
Figure 2 for Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation
Figure 3 for Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation
Figure 4 for Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation
* Appears in Medical Imaging with Deep Learning (MIDL), 2018 
  Access Paper or Ask Questions

On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation


Jun 07, 2018
Alain Jungo, Raphael Meier, Ekin Ermis, Marcela Blatti-Moreno, Evelyn Herrmann, Roland Wiest, Mauricio Reyes

Figure 1 for On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation
Figure 2 for On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation
Figure 3 for On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation
Figure 4 for On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation
* Appears in Medical Image Computing and Computer Assisted Interventions (MICCAI), 2018 
  Access Paper or Ask Questions

Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs


Mar 02, 2017
Raphael Meier, Urspeter Knecht, Alain Jungo, Roland Wiest, Mauricio Reyes

Figure 1 for Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs
Figure 2 for Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs
Figure 3 for Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs
Figure 4 for Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs
* Deactivated review mode (line spacing) 
  Access Paper or Ask Questions

Skull-stripping for Tumor-bearing Brain Images


Apr 02, 2012
Stefan Bauer, Lutz-P. Nolte, Mauricio Reyes

Figure 1 for Skull-stripping for Tumor-bearing Brain Images
* Swiss Society of Biomedical Engineering, Annual Meeting 2011, Bern, Switzerland 
  Access Paper or Ask Questions