Search Results for author: Jens Petersen

Found 39 papers, 19 papers with code

Enhancing predictive imaging biomarker discovery through treatment effect analysis

no code implementations4 Jun 2024 Shuhan Xiao, Lukas Klein, Jens Petersen, Philipp Vollmuth, Paul F. Jaeger, Klaus H. Maier-Hein

In response, we present a new task of discovering predictive imaging biomarkers directly from the pre-treatment images to learn relevant image features.

Decision Making

Prediction of post-radiotherapy recurrence volumes in head and neck squamous cell carcinoma using 3D U-Net segmentation

no code implementations16 Aug 2023 Denis Kutnár, Ivan R Vogelius, Katrin Elisabet Håkansson, Jens Petersen, Jeppe Friborg, Lena Specht, Mogens Bernsdorf, Anita Gothelf, Claus Kristensen, Abraham George Smith

We investigated the extent to which a Convolutional neural network (CNN) is able to predict LRR volumes based on pre-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET)/computed tomography (CT) scans in HNSCC patients and thus the potential to identify biological high risk volumes using CNNs.

Computed Tomography (CT) POS

Localise to segment: crop to improve organ at risk segmentation accuracy

1 code implementation10 Apr 2023 Abraham George Smith, Denis Kutnár, Ivan Richter Vogelius, Sune Darkner, Jens Petersen

Some deep learning methods for the segmentation of organs at risk use a two stage process where a localisation network first crops an image to the relevant region and then a locally specialised network segments the cropped organ of interest.

Organ Segmentation Segmentation

MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation

1 code implementation17 Mar 2023 Saikat Roy, Gregor Koehler, Constantin Ulrich, Michael Baumgartner, Jens Petersen, Fabian Isensee, Paul F. Jaeger, Klaus Maier-Hein

This leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation.

Decoder Image Segmentation +3

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.

Explicit Temporal Embedding in Deep Generative Latent Models for Longitudinal Medical Image Synthesis

1 code implementation13 Jan 2023 Julian Schön, Raghavendra Selvan, Lotte Nygård, Ivan Richter Vogelius, Jens Petersen

This, in contrast to previous methods, allows us to synthesize continuous, smooth, and high-quality longitudinal volumetric data with limited supervision.

Disentanglement Image Generation

CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization

no code implementations5 Jan 2023 Carsten T. Lüth, David Zimmerer, Gregor Koehler, Paul F. Jaeger, Fabian Isensee, Jens Petersen, Klaus H. Maier-Hein

By utilizing the representations of contrastive learning, we aim to fix the over-fixation on low-level features and learn more semantic-rich representations.

Contrastive Learning Unsupervised Anomaly Detection

Interpreting Latent Spaces of Generative Models for Medical Images using Unsupervised Methods

1 code implementation20 Jul 2022 Julian Schön, Raghavendra Selvan, Jens Petersen

The results show that unsupervised methods to discover interpretable directions in GANs generalize to VAEs and can be applied to medical images.

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

Continuous-Time Deep Glioma Growth Models

2 code implementations23 Jun 2021 Jens Petersen, Fabian Isensee, Gregor Köhler, Paul F. Jäger, David Zimmerer, Ulf Neuberger, Wolfgang Wick, Jürgen Debus, Sabine Heiland, Martin Bendszus, Philipp Vollmuth, Klaus H. Maier-Hein

The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy.

Time Series Time Series Analysis +1

RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy

1 code implementation22 Jun 2021 Abraham George Smith, Jens Petersen, Cynthia Terrones-Campos, Anne Kiil Berthelsen, Nora Jarrett Forbes, Sune Darkner, Lena Specht, Ivan Richter Vogelius

Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data.

BIG-bench Machine Learning

GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data

1 code implementation9 Jun 2021 Jens Petersen, Gregor Köhler, David Zimmerer, Fabian Isensee, Paul F. Jäger, Klaus H. Maier-Hein

Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points.

Time Series Time Series Analysis

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

Segmenting two-dimensional structures with strided tensor networks

1 code implementation13 Feb 2021 Raghavendra Selvan, Erik B Dam, Jens Petersen

We use the matrix product state (MPS) tensor network on non-overlapping patches of a given input image to predict the segmentation mask by learning a pixel-wise linear classification rule in a high dimensional space.

Image Classification Image Segmentation +4

Frequency Decomposition in Neural Processes

no code implementations1 Jan 2021 Jens Petersen, Paul F Jaeger, Gregor Koehler, David Zimmerer, Fabian Isensee, Klaus Maier-Hein

Neural Processes are a powerful tool for learning representations of function spaces purely from examples, in a way that allows them to perform predictions at test time conditioned on so-called context observations.

A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients

no code implementations28 Nov 2019 David Zimmerer, Jens Petersen, Simon A. A. Kohl, Klaus H. Maier-Hein

Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions.

Anomaly Detection

High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection

no code implementations27 Nov 2019 David Zimmerer, Jens Petersen, Klaus Maier-Hein

Variational Auto-Encoders have often been used for unsupervised pretraining, feature extraction and out-of-distribution and anomaly detection in the medical field.

Anomaly Detection Out of Distribution (OOD) Detection

Deep Probabilistic Modeling of Glioma Growth

1 code implementation9 Jul 2019 Jens Petersen, Paul F. Jäger, Fabian Isensee, Simon A. A. Kohl, Ulf Neuberger, Wolfgang Wick, Jürgen Debus, Sabine Heiland, Martin Bendszus, Philipp Kickingereder, Klaus H. Maier-Hein

Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the associated parameters.

Representation Learning

Unsupervised Anomaly Localization using Variational Auto-Encoders

1 code implementation4 Jul 2019 David Zimmerer, Fabian Isensee, Jens Petersen, Simon Kohl, Klaus Maier-Hein

An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist.

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

5 code implementations17 Apr 2019 Fabian Isensee, Paul F. Jäger, Simon A. A. Kohl, Jens Petersen, Klaus H. Maier-Hein

Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.

Image Segmentation Medical Image Segmentation +2

A cross-center smoothness prior for variational Bayesian brain tissue segmentation

1 code implementation11 Mar 2019 Wouter M. Kouw, Silas N. Ørting, Jens Petersen, Kim S. Pedersen, Marleen de Bruijne

Here we present a smoothness prior that is fit to segmentations produced at another medical center.

Segmentation of Roots in Soil with U-Net

1 code implementation28 Feb 2019 Abraham George Smith, Jens Petersen, Raghavendra Selvan, Camilla Ruø Rasmussen

We evaluate our system using 867 images for which we have obtained line-intersect counts, attaining a Spearman rank correlation of 0. 9748 and an $r^2$ of 0. 9217.

Image Segmentation Segmentation +1

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

Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks

1 code implementation21 Nov 2018 Raghavendra Selvan, Thomas Kipf, Max Welling, Antonio Garcia-Uceda Juarez, Jesper H. Pedersen, Jens Petersen, Marleen de Bruijne

Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications.

Learning to quantify emphysema extent: What labels do we need?

no code implementations17 Oct 2018 Silas Nyboe Ørting, Jens Petersen, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne

We evaluate performance on 600 low-dose CT scans from the Danish Lung Cancer Screening Trial and find that learning from emphysema presence labels, which are much easier to obtain, gives equally good performance to learning from emphysema extent labels.

BIG-bench Machine Learning Multiple Instance Learning

Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses

no code implementations23 Jun 2018 Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen, Marleen de Bruijne

We propose to use statistical ranking of local hypotheses in constructing the MHT tree, which yields a probabilistic interpretation of scores across scales and helps alleviate the scale-dependence of MHT parameters.

Segmentation

Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans

no code implementations19 Jun 2018 Silas Nyboe Ørting, Jens Petersen, Veronika Cheplygina, Laura H. Thomsen, Mathilde M. W. Wille, Marleen de Bruijne

We evaluate the networks on 973 images, and show that the CNNs can learn disease relevant feature representations from derived similarity triplets.

Extraction of Airways using Graph Neural Networks

no code implementations12 Apr 2018 Raghavendra Selvan, Thomas Kipf, Max Welling, Jesper H. Pedersen, Jens Petersen, Marleen de Bruijne

We present extraction of tree structures, such as airways, from image data as a graph refinement task.

Decoder

Mean Field Network based Graph Refinement with application to Airway Tree Extraction

no code implementations10 Apr 2018 Raghavendra Selvan, Max Welling, Jesper H. Pedersen, Jens Petersen, Marleen de Bruijne

Performance of the method is compared with two methods: the first uses probability images from a trained voxel classifier with region growing, which is similar to one of the best performing methods at EXACT'09 airway challenge, and the second method is based on Bayesian smoothing on these probability images.

Bayesian Inference

Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing

no code implementations7 Aug 2017 Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen, Marleen de Bruijne

The evolution of individual branches is modelled using a process model and the observed data is incorporated into the update step of the Bayesian smoother using a measurement model that is based on a multi-scale blob detector.

Extraction of airway trees using multiple hypothesis tracking and template matching

no code implementations24 Nov 2016 Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen, Marleen de Bruijne

The results show improvements in performance when compared to the original method and region growing on intensity images.

Template Matching

Scalable kernels for graphs with continuous attributes

no code implementations NeurIPS 2013 Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt

While graphs with continuous node attributes arise in many applications, state-of-the-art graph kernels for comparing continuous-attributed graphs suffer from a high runtime complexity; for instance, the popular shortest path kernel scales as $\mathcal{O}(n^4)$, where $n$ is the number of nodes.

General Classification

Geometric tree kernels: Classification of COPD from airway tree geometry

no code implementations29 Mar 2013 Aasa Feragen, Jens Petersen, Dominik Grimm, Asger Dirksen, Jesper Holst Pedersen, Karsten Borgwardt, Marleen de Bruijne

Methodological contributions: This paper introduces a family of kernels for analyzing (anatomical) trees endowed with vector valued measurements made along the tree.

Classification General Classification +1

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