1 code implementation • 14 Jun 2022 • Moritz Roman Hernandez Petzsche, Ezequiel de la Rosa, Uta Hanning, Roland Wiest, Waldo Enrique Valenzuela Pinilla, Mauricio Reyes, Maria Ines Meyer, Sook-Lei Liew, Florian Kofler, Ivan Ezhov, David Robben, Alexander Hutton, Tassilo Friedrich, Teresa Zarth, Johannes Bürkle, The Anh Baran, Bjoern Menze, Gabriel Broocks, Lukas Meyer, Claus Zimmer, Tobias Boeckh-Behrens, Maria Berndt, Benno Ikenberg, Benedikt Wiestler, Jan S. Kirschke
The test dataset will be used for model validation only and will not be released to the public.
no code implementations • 16 May 2022 • Bailiang Jian, Mohammad Farid Azampour, Francesca De Benetti, Johannes Oberreuter, Christina Bukas, Alexandra S. Gersing, Sarah C. Foreman, Anna-Sophia Dietrich, Jon Rischewski, Jan S. Kirschke, Nassir Navab, Thomas Wendler
We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI.
1 code implementation • 30 Mar 2022 • Paul Engstler, Matthias Keicher, David Schinz, Kristina Mach, Alexandra S. Gersing, Sarah C. Foreman, Sophia S. Goller, Juergen Weissinger, Jon Rischewski, Anna-Sophia Dietrich, Benedikt Wiestler, Jan S. Kirschke, Ashkan Khakzar, Nassir Navab
Do black-box neural network models learn clinically relevant features for fracture diagnosis?
We show that our approach is able to generate AIFs without any manual annotation, and hence avoiding manual rater's influences.
1 code implementation • 12 Mar 2021 • Christina Bukas, Bailiang Jian, Luis F. Rodriguez Venegas, Francesca De Benetti, Sebastian Ruehling, Anjany Sekuboyina, Jens Gempt, Jan S. Kirschke, Marie Piraud, Johannes Oberreuter, Nassir Navab, Thomas Wendler
The framework uses the patient CT scan and the fractured vertebra label to build a virtual healthy spine using a high-level approach.
1 code implementation • 10 Mar 2021 • Hans Liebl, David Schinz, Anjany Sekuboyina, Luca Malagutti, Maximilian T. Löffler, Amirhossein Bayat, Malek El Husseini, Giles Tetteh, Katharina Grau, Eva Niederreiter, Thomas Baum, Benedikt Wiestler, Bjoern Menze, Rickmer Braren, Claus Zimmer, Jan S. Kirschke
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach.
Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions.
This paper presents a novel yet efficient defense framework for segmentation models against adversarial attacks in medical imaging.
The first subnetwork is designed to complete the shape of the downsampled defective skull.
Building on state-of-art metric losses, we present a novel Grading Loss for learning representations that respect Genant's fracture grading scheme.
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D.
2 code implementations • 24 Jan 2020 • Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat, Maximilian Löffler, Hans Liebl, Hongwei Li, Giles Tetteh, Jan Kukačka, Christian Payer, Darko Štern, Martin Urschler, Maodong Chen, Dalong Cheng, Nikolas Lessmann, Yujin Hu, Tianfu Wang, Dong Yang, Daguang Xu, Felix Ambellan, Tamaz Amiranashvili, Moritz Ehlke, Hans Lamecker, Sebastian Lehnert, Marilia Lirio, Nicolás Pérez de Olaguer, Heiko Ramm, Manish Sahu, Alexander Tack, Stefan Zachow, Tao Jiang, Xinjun Ma, Christoph Angerman, Xin Wang, Kevin Brown, Alexandre Kirszenberg, Élodie Puybareau, Di Chen, Yiwei Bai, Brandon H. Rapazzo, Timyoas Yeah, Amber Zhang, Shangliang Xu, Feng Hou, Zhiqiang He, Chan Zeng, Zheng Xiangshang, Xu Liming, Tucker J. Netherton, Raymond P. Mumme, Laurence E. Court, Zixun Huang, Chenhang He, Li-Wen Wang, Sai Ho Ling, Lê Duy Huynh, Nicolas Boutry, Roman Jakubicek, Jiri Chmelik, Supriti Mulay, Mohanasankar Sivaprakasam, Johannes C. Paetzold, Suprosanna Shit, Ivan Ezhov, Benedikt Wiestler, Ben Glocker, Alexander Valentinitsch, Markus Rempfler, Björn H. Menze, Jan S. Kirschke
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
We propose an auto-encoding network architecture for point clouds (PC) capable of extracting shape signatures without supervision.
Synthesizing MR imaging sequences is highly relevant in clinical practice, as single sequences are often missing or are of poor quality (e. g. due to motion).
Furthermore, we explored two variants of adversarial training schemes that incorporated the anatomical a priori knowledge into the Btrfly Net.
Relevant shape, intensity and texture biomarkers characterizing the different lesions are isolated and a lesion predictive model is built by using Partial Least Squares.
Robust localisation and identification of vertebrae is essential for automated spine analysis.
The first stage employs a multi-layered perceptron performing non-linear regression for locating the lumbar region using the global context.