1 code implementation • 21 Mar 2024 • Alicia Durrer, Julia Wolleb, Florentin Bieder, Paul Friedrich, Lester Melie-Garcia, Mario Ocampo-Pineda, Cosmin I. Bercea, Ibrahim E. Hamamci, Benedikt Wiestler, Marie Piraud, Özgür Yaldizli, Cristina Granziera, Bjoern H. Menze, Philippe C. Cattin, Florian Kofler
Monitoring diseases that affect the brain's structural integrity requires automated analysis of magnetic resonance (MR) images, e. g., for the evaluation of volumetric changes.
no code implementations • 31 Jul 2023 • Diana Waldmannstetter, Benedikt Wiestler, Julian Schwarting, Ivan Ezhov, Marie Metz, Spyridon Bakas, Bhakti Baheti, Satrajit Chakrabarty, Jan S. Kirschke, Rolf A. Heckemann, Marie Piraud, Florian Kofler, Bjoern H. Menze
Nowadays, registration methods are typically evaluated based on sub-resolution tracking error differences.
1 code implementation • 19 Jun 2023 • Linus Kreitner, Johannes C. Paetzold, Nikolaus Rauch, Chen Chen, Ahmed M. Hagag, Alaa E. Fayed, Sobha Sivaprasad, Sebastian Rausch, Julian Weichsel, Bjoern H. Menze, Matthias Harders, Benjamin Knier, Daniel Rueckert, Martin J. Menten
To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data.
no code implementations • 4 Apr 2023 • Diana Waldmannstetter, Benedikt Wiestler, Julian Schwarting, Ivan Ezhov, Marie Metz, Spyridon Bakas, Bhakti Baheti, Satrajit Chakrabarty, Daniel Rueckert, Jan S. Kirschke, Rolf A. Heckemann, Marie Piraud, Bjoern H. Menze, Florian Kofler
Even though simultaneous optimization of similarity metrics is a standard procedure in the field of semantic segmentation, surprisingly, this is much less established for image registration.
1 code implementation • 22 Jul 2022 • Martin J. Menten, Johannes C. Paetzold, Alina Dima, Bjoern H. Menze, Benjamin Knier, Daniel Rueckert
Encouraged by our method's competitive quantitative and superior qualitative performance, we believe that it constitutes a versatile tool to advance the quantitative analysis of OCTA images.
no code implementations • 1 Mar 2022 • Fernando Navarro, Guido Sasahara, Suprosanna Shit, Ivan Ezhov, Jan C. Peeken, Stephanie E. Combs, Bjoern H. Menze
Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning.
no code implementations • 24 Oct 2021 • Giles Tetteh, Fernando Navarro, Johannes Paetzold, Jan Kirschke, Claus Zimmer, Bjoern H. Menze
First, it is time-consuming - the clinician needs to scan through several slices of images to ascertain the region of interest before deciding on what severity grade to assign to a patient.
no code implementations • 3 Sep 2021 • Suprosanna Shit, Ivan Ezhov, Leon Mächler, Abinav R., Jana Lipkova, Johannes C. Paetzold, Florian Kofler, Marie Piraud, Bjoern H. Menze
In this paper, we propose a neural solver to learn an optimal iterative scheme in a data-driven fashion for any class of PDEs.
1 code implementation • 30 Aug 2021 • Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar, Mihail I. Todorov, Anjany Sekuboyina, Georgios Kaissis, Ali Ertürk, Stephan Günnemann, Bjoern H. Menze
Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset.
1 code implementation • CVPR 2021 • Suprosanna Shit, Johannes C. Paetzold, Anjany Sekuboyina, Ivan Ezhov, Alexander Unger, Andrey Zhylka, Josien P. W. Pluim, Ulrich Bauer, Bjoern H. Menze
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
no code implementations • 14 May 2021 • Fernando Navarro, Christopher Watanabe, Suprosanna Shit, Anjany Sekuboyina, Jan C. Peeken, Stephanie E. Combs, Bjoern H. Menze
Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets.
no code implementations • 22 Apr 2021 • Izabela Horvath, Johannes C. Paetzold, Oliver Schoppe, Rami Al-Maskari, Ivan Ezhov, Suprosanna Shit, Hongwei Li, Ali Ertuerk, Bjoern H. Menze
Novel multimodal imaging methods are capable of generating extensive, super high resolution datasets for preclinical research.
no code implementations • 23 Sep 2020 • Hanwool Park, Amirhossein Bayat, Mohammad Sabokrou, Jan S. Kirschke, Bjoern H. Menze
This paper presents a novel yet efficient defense framework for segmentation models against adversarial attacks in medical imaging.
no code implementations • 22 Sep 2020 • Amirhossein Bayat, Suprosanna Shit, Adrian Kilian, Jürgen T. Liechtenstein, Jan S. Kirschke, Bjoern H. Menze
The first subnetwork is designed to complete the shape of the downsampled defective skull.
no code implementations • 18 Aug 2020 • Malek Husseini, Anjany Sekuboyina, Maximilian Loeffler, Fernando Navarro, Bjoern H. Menze, Jan S. Kirschke
Building on state-of-art metric losses, we present a novel Grading Loss for learning representations that respect Genant's fracture grading scheme.
no code implementations • 13 Jul 2020 • Amirhossein Bayat, Anjany Sekuboyina, Johannes C. Paetzold, Christian Payer, Darko Stern, Martin Urschler, Jan S. Kirschke, Bjoern H. Menze
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D.
no code implementations • MIDL 2019 • Fernando Navarro, Anjany Sekuboyina, Diana Waldmannstetter, Jan C. Peeken, Stephanie E. Combs, Bjoern H. Menze
Robust localization of organs in computed tomography scans is a constant pre-processing requirement for organ-specific image retrieval, radiotherapy planning, and interventional image analysis.
3 code implementations • 16 Mar 2020 • Suprosanna Shit, Johannes C. Paetzold, Anjany Sekuboyina, Ivan Ezhov, Alexander Unger, Andrey Zhylka, Josien P. W. Pluim, Ulrich Bauer, Bjoern H. Menze
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
no code implementations • MIDL 2019 • Carolin M. Pirkl, Pedro A. Gómez, Ilona Lipp, Guido Buonincontri, Miguel Molina-Romero, Anjany Sekuboyina, Diana Waldmannstetter, Jonathan Dannenberg, Sebastian Endt, Alberto Merola, Joseph R. Whittaker, Valentina Tomassini, Michela Tosetti, Derek K. Jones, Bjoern H. Menze, Marion I. Menzel
Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues.
no code implementations • 22 Jul 2019 • Anjany Sekuboyina, Markus Rempfler, Alexander Valentinitsch, Maximilian Loeffler, Jan S. Kirschke, Bjoern H. Menze
We propose an auto-encoding network architecture for point clouds (PC) capable of extracting shape signatures without supervision.
no code implementations • 1 Jul 2019 • Jiechao Ma, Sen Liang, Xiang Li, Hongwei Li, Bjoern H. Menze, Rongguo Zhang, Wei-Shi Zheng
Mammogram is the most effective imaging modality for the mass lesion detection of breast cancer at the early stage.
no code implementations • 6 Feb 2019 • Anjany Sekuboyina, Markus Rempfler, Alexander Valentinitsch, Bjoern H. Menze, Jan S. Kirschke
Furthermore, we explored two variants of adversarial training schemes that incorporated the anatomical a priori knowledge into the Btrfly Net.
no code implementations • 18 Dec 2018 • Jiechao Ma, Xiang Li, Hongwei Li, Bjoern H. Menze, Sen Liang, Rongguo Zhang, Wei-Shi Zheng
In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group convolution on 3D single-shot detector (SSD) called group-attention SSD (GA-SSD).
Computed Tomography (CT) Finding Pulmonary Nodules In Large-Scale Ct Images
no code implementations • 22 Oct 2018 • Xiaobin Hu, Hongwei Li, Yu Zhao, Chao Dong, Bjoern H. Menze, Marie Piraud
Based on the same start-of-the-art network architecture, the accuracy of nested-class (enhancing tumor) is reasonably improved from 69% to 72% compared with the traditional Softmax-based method which blind to topological prior.
no code implementations • 6 Jun 2018 • Lena Maier-Hein, Matthias Eisenmann, Annika Reinke, Sinan Onogur, Marko Stankovic, Patrick Scholz, Tal Arbel, Hrvoje Bogunovic, Andrew P. Bradley, Aaron Carass, Carolin Feldmann, Alejandro F. Frangi, Peter M. Full, Bram van Ginneken, Allan Hanbury, Katrin Honauer, Michal Kozubek, Bennett A. Landman, Keno März, Oskar Maier, Klaus Maier-Hein, Bjoern H. Menze, Henning Müller, Peter F. Neher, Wiro Niessen, Nasir Rajpoot, Gregory C. Sharp, Korsuk Sirinukunwattana, Stefanie Speidel, Christian Stock, Danail Stoyanov, Abdel Aziz Taha, Fons van der Sommen, Ching-Wei Wang, Marc-André Weber, Guoyan Zheng, Pierre Jannin, Annette Kopp-Schneider
International challenges have become the standard for validation of biomedical image analysis methods.
no code implementations • 25 May 2018 • Dhritiman Das, Eduardo Coello, Rolf F Schulte, Bjoern H. Menze
The goal of our proposed framework is to learn the spectral features from a training set comprising of different variations of both simulated and in-vivo brain spectra and then use this learning for the subsequent metabolite quantification.
no code implementations • 8 Apr 2018 • Cagdas Ulas, Giles Tetteh, Michael J. Thrippleton, Paul A. Armitage, Stephen D. Makin, Joanna M. Wardlaw, Mike E. Davies, Bjoern H. Menze
Dynamic contrast-enhanced (DCE) MRI is an evolving imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters in body tissues, in which series of T1-weighted images are collected following the administration of a paramagnetic contrast agent.
1 code implementation • 8 Apr 2018 • Cagdas Ulas, Giles Tetteh, Stephan Kaczmarz, Christine Preibisch, Bjoern H. Menze
Arterial spin labeling (ASL) allows to quantify the cerebral blood flow (CBF) by magnetic labeling of the arterial blood water.
no code implementations • 5 Apr 2018 • Marie Piraud, Anjany Sekuboyina, Bjoern H. Menze
For many biological image segmentation tasks, including topological knowledge, such as the nesting of classes, can greatly improve results.
no code implementations • 4 Apr 2018 • Anjany Sekuboyina, Markus Rempfler, Jan Kukačka, Giles Tetteh, Alexander Valentinitsch, Jan S. Kirschke, Bjoern H. Menze
Robust localisation and identification of vertebrae is essential for automated spine analysis.
no code implementations • 25 Mar 2018 • Giles Tetteh, Velizar Efremov, Nils D. Forkert, Matthias Schneider, Jan Kirschke, Bruno Weber, Claus Zimmer, Marie Piraud, Bjoern H. Menze
Our experiments show that, by replacing 3-D filters with cross-hair filters in our network, we achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy (with a Cox-Wilcoxon paired sample significance test p-value of 0. 07 when compared to full 3-D filters).
no code implementations • 12 Apr 2017 • Giles Tetteh, Markus Rempfler, Bjoern H. Menze, Claus Zimmer
Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling.
no code implementations • 7 Apr 2017 • Jana Lipková, Markus Rempfler, Patrick Christ, John Lowengrub, Bjoern H. Menze
The segmentation of liver lesions is crucial for detection, diagnosis and monitoring progression of liver cancer.
no code implementations • 13 Mar 2017 • Anjany Sekuboyina, Alexander Valentinitsch, Jan S. Kirschke, Bjoern H. Menze
The first stage employs a multi-layered perceptron performing non-linear regression for locating the lumbar region using the global context.
no code implementations • ICCV 2017 • Markus Rempfler, Jan-Hendrik Lange, Florian Jug, Corinna Blasse, Eugene W. Myers, Bjoern H. Menze, Bjoern Andres
Lineage tracing, the joint segmentation and tracking of living cells as they move and divide in a sequence of light microscopy images, is a challenging task.
3 code implementations • 7 Oct 2016 • Patrick Ferdinand Christ, Mohamed Ezzeldin A. Elshaer, Florian Ettlinger, Sunil Tatavarty, Marc Bickel, Patrick Bilic, Markus Rempfler, Marco Armbruster, Felix Hofmann, Melvin D'Anastasi, Wieland H. Sommer, Seyed-Ahmad Ahmadi, Bjoern H. Menze
Automatic segmentation of the liver and its lesion is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems.
no code implementations • 20 Jun 2016 • Markus Rempfler, Bjoern Andres, Bjoern H. Menze
Several important tasks in medical image analysis can be stated in the form of an optimization problem whose feasible solutions are connected subgraphs.