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Design and Development of a Web-based Tool for Inpainting of Dissected Aortae in Angiography Images

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arXiv 2020年

作者： Prutsch, Alexander Pepe, Antonio Egger, Jan Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Computer Algorithms for Medicine Lab Graz Austria

Medical imaging is an important tool for the diagnosis and the evaluation of an aortic dissection (AD);a serious condition of the aorta, which could lead to a life-threatening aortic rupture. AD patients need life-long medical monitoring of the aortic enlargement and of the disease progression, subsequent to the diagnosis of the aortic dissection. Since there is a lack of healthy-dissected image pairs from medical studies, the application of inpainting techniques offers an alternative source for generating them by doing a virtual regression from dissected aortae to healthy aortae;an indirect way to study the origin of the disease. The proposed inpainting tool combines a neural network, which was trained on the task of inpainting aortic dissections, with an easy-to-use user interface. To achieve this goal, the inpainting tool has been integrated within the 3D medical image viewer of StudierFenster (***). By designing the tool as a web application, we simplify the usage of the neural network and reduce the initial learning curve. Copyright © 2020, The Authors. All rights reserved.

关键词： Blood vessels

Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation

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arXiv 2024年

作者： Ferreira, André Jesus, Tiago Puladi, Behrus Kleesiek, Jens Alves, Victor Egger, Jan Center Algoritmi / LASI University of Minho Braga4710-057 Portugal Computer Algorithms for Medicine Laboratory Graz Austria University Medicine Essen Girardetstraße 2 Essen45131 Germany School of Medicine University of Minho Braga Portugal ICVS/3B’s – PT Government Associate Laboratory Guimarães Braga Portugal University Medicine Essen Hufelandstraße 55 Essen45147 Germany Partner Site Essen Hufelandstraße 55 Essen45147 Germany Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz8010 Austria Institute of Medical Informatics University Hospital RWTH Aachen Aachen Germany Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Aachen Germany Department of Physics TU Dortmund University Dortmund Germany

This paper presents the winning solution of task 1 and the third-placed solution of task 3 of the BraTS challenge. The use of automated tools in clinical practice has increased due to the development of more and more sophisticated and reliable algorithms. However, achieving clinical standards and developing tools for real-life scenarios is a major challenge. To this end, BraTS has organised tasks to find the most advanced solutions for specific purposes. In this paper, we propose the use of synthetic data to train state-of-the-art frameworks in order to improve the segmentation of adult gliomas in a post-treatment scenario, and the segmentation of meningioma for radiotherapy planning. Our results suggest that the use of synthetic data leads to more robust algorithms, although the synthetic data generation pipeline is not directly suited to the meningioma task. In task 1, we achieved a DSC of 0.7900, 0.8076, 0.7760, 0.8926, 0.7874, 0.8938 and a HD95 of 35.63, 30.35, 44.58, 16.87, 38.19, 17.95 for ET, NETC, RC, SNFH, TC and WT, respectively and, in task 3, we achieved a DSC of 0.801 and HD95 of 38.26, in the testing phase. The code for these tasks is available at https://***/ShadowTwin41/BraTS_2023_2024_solutions. Copyright © 2024, The Authors. All rights reserved.

关键词： Radiotherapy

Significance of Single Interval Discrete Attributes: Case Study on 2-Level Discretisation

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SSRN

SSRN 2022年

作者： Stańczyk, Urszula Zielosko, Beata Baron, Grzegorz Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 Sosnowiec41-200 Poland

Supervised discretisation is widely considered as far more advantageous form of transformation than unsupervised processing of attributes. When evaluating candidate cut-points for splitting the ranges of values of discretised features, the supervised methods help to preserve informative content of a variable, which is useful for classification. After a discretisation process, based on employed criteria, some attributes can be found as irrelevant, with all their values represented in discrete domain by a single interval. In consequence, such variables are removed from considerations, and no knowledge is mined from them. The paper presents research works focused on extended 2-level transformations of attributes values, with combining supervised with unsupervised discretisation strategies. For all variables with single intervals returned from supervised algorithms, the ranges of values were divided by unsupervised methods with varying numbers of bins. Resulting variants of data were subjected to selected data mining techniques and performance of chosen classifiers evaluated and compared. The experiments were performed on stylometric task of authorship attribution. © 2022, The Authors. All rights reserved.

关键词： Pattern recognition

General Concepts in Swarm of Drones Control: Analysis and Implementation

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General Concepts in Swarm of Drones Control: Analysis and Im...

IEEE International Conference on Big Data

作者： Dariusz Marek Marcin Paszkuta Jakub Szyguła Piotr Biernacki Adam Domański Marta Szczygieł Marcel Król Konrad Wojciechowski Dept. of Distributed Systems and Informatic Devices Silesian University of Technology Poland Polish-Japanese Academy of Information Technology Warsaw Poland Dept. of Computer Graphics Vision and Digital Systems Silesian University of Technology Warsaw Poland Dept. of Distributed Systems and Informatic Devices Silesian University of Technology Warsaw Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Warsaw Poland Silesian University of Technology Poland

In the presented paper, detailed schematics and descriptions concerning communication in the context of swarm drone control are introduced. Methods and technologies behind them are discussed. The implementation of the presented concept was verified through a series of tests. Simulation results which confirm the effectiveness and efficiency of the proposed solutions are presented. The obtained results prove the correctness implementation of the presented methods and also demonstrate the benefits derived from the proposed approach. The presented concept of controlling a swarm of drones represents the current state of knowledge and technology in the field of drones and their control. By utilizing advanced communication technologies, positioning, and analysis of communication structures, this work makes a significant contribution to the UAV (Unmanned Aerial Vehicle) field.

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k-strip: A novel segmentation algorithm in k-space for the application of skull stripping

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arXiv 2022年

作者： Rempe, Moritz Mentzel, Florian Pomykala, Kelsey L. Haubold, Johannes Nensa, Felix Kröninger, Kevin Egger, Jan Kleesiek, Jens University Hospital Essen Girardetstraße 2 Essen45131 Germany The Department of Physics of the Technical University Dortmund Otto-Hahn-Straße 4a Dortmund44227 Germany The Computer Algorithms for Medicine Laboratory Graz8010 Austria The Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz8010 Austria University Medicine Essen Hufelandstraße 55 Essen45147 Germany Partner Site Essen Hufelandstraße 55 Essen45147 Germany

We present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich complex valued k-space. Using three datasets from different institutions with a total of around 140000 MRI slices, we show that our network can perform skull-stripping on the raw data of MRIs while preserving the phase information which no other skull stripping algorithm is able to work with. For two of the datasets, skull stripping performed by HD-BET (Brain Extraction Tool) in the image domain is used as the ground truth, whereas the third dataset comes with per-hand annotated brain segmentations. All three datasets were very similar to the ground truth (DICE scores of 92%-99% and Hausdorff distances of under 5.5 pixel). Results on slices above the eye-region reach DICE scores of up to 99%, whereas the accuracy drops in regions around the eyes and below, with partially blurred output. The output of k-strip often has smoothed edges at the demarcation to the skull. Binary masks are created with an appropriate threshold. With this proof-of-concept study, we were able to show the feasibility of working in the k-space frequency domain, preserving phase information, with consistent results. Besides preserving valuable information for further diagnostics, this approach makes an immediate anonymization of patient data possible, already before being transformed into the image domain. Future research should be dedicated to discovering additional ways the k-space can be used for innovative image analysis and further workflows. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Bayesian uncertainty estimation of learned variational MRI reconstruction

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arXiv 2021年

作者： Narnhofer, Dominik Effland, Alexander Kobler, Erich Hammernik, Kerstin Knoll, Florian Pock, Thomas Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Graz Austria Institute of Applied Mathematics University of Bonn Bonn Germany Computer Science Department Johannes Kepler University Linz Austria Technical University of Munich Munich Germany Imperial College London London United Kingdom Nyu School of Medicine NY United States

Recent deep learning approaches focus on improving quantitative scores of dedicated benchmarks, and therefore only reduce the observation-related (aleatoric) uncertainty. However, the model-immanent (epistemic) uncertainty is less frequently systematically analyzed. In this work, we introduce a Bayesian variational framework to quantify the epistemic uncertainty. To this end, we solve the linear inverse problem of undersampled MRI reconstruction in a variational setting. The associated energy functional is composed of a data fidelity term and the total deep variation (TDV) as a learned parametric regularizer. To estimate the epistemic uncertainty we draw the parameters of the TDV regularizer from a multivariate Gaussian distribution, whose mean and covariance matrix are learned in a stochastic optimal control problem. In several numerical experiments, we demonstrate that our approach yields competitive results for undersampled MRI reconstruction. Moreover, we can accurately quantify the pixelwise epistemic uncertainty, which can serve radiologists as an additional resource to visualize reconstruction *** Codes 68T45, 65K10, 65D19 Copyright © 2021, The Authors. All rights reserved.

关键词： Uncertainty analysis

Variational inference and bayesian cnns for uncertainty estimation in multi-factorial bone age prediction

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arXiv 2020年

作者： Eggenreich, Stefan Payer, Christian Urschler, Martin Štern, Darko Institute of Computer Graphics and Vision Graz University of Technology Austria School of Computer Science University of Auckland New Zealand

Additionally to the extensive use in clinical medicine, biological age (BA) in legal medicine is used to assess unknown chronological age (CA) in applications where identification documents are not available. Automatic methods for age estimation proposed in the literature are predicting point estimates, which can be misleading without the quantification of predictive uncertainty. In our multi-factorial age estimation method from MRI data, we used the Variational Inference approach to estimate the uncertainty of a Bayesian CNN model. Distinguishing model uncertainty from data uncertainty, we interpreted data uncertainty as biological variation, i.e. the range of possible CA of subjects having the same BA. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

Learning Atrial Fiber Orientations and Conductivity Tensors from Intracardiac Maps Using Physics-Informed Neural Networks 11th

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Learning Atrial Fiber Orientations and Conductivity Tensors ...

11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021

作者： Grandits, Thomas Pezzuto, Simone Costabal, Francisco Sahli Perdikaris, Paris Pock, Thomas Plank, Gernot Krause, Rolf Institute of Computer Graphics and Vision TU Graz Graz Austria BioTechMed-Graz Graz Austria Center for Computational Medicine in Cardiology Euler Institute Università della Svizzera italiana Lugano Switzerland Department of Mechanical and Metallurgical Engineering School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Institute for Biological and Medical Engineering Schools of Engineering Medicine and Biological Sciences Pontificia Universidad Católica de Chile Santiago Chile Millennium Nucleus for Cardiovascular Magnetic Resonance Santiago Chile Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania PhiladelphiaPA United States Gottfried Schatz Research Center - Division of Biophysics Medical University of Graz Graz Austria

ISBN: (纸本)9783030787097

Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information can be extracted from the available data. The fibers in cardiac tissue conduct the electrical wave faster, and their direction could be inferred from activation times. In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation. In particular, we train the neural network to weakly satisfy the anisotropic eikonal equation and to predict the measured activation times. We use a local basis for the anisotropic conductivity tensor, which encodes the fiber orientation. The methodology is tested both in a synthetic example and for patient data. Our approach shows good agreement in both cases, with an RMSE of 2.2 ms on the in-silico data and outperforming a state of the art method on the patient data. The results show a first step towards learning the fiber orientations from electroanatomical maps with physics-informed neural networks. © 2021, Springer Nature Switzerland AG.

关键词： Tensors

An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design

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arXiv 2020年

作者： Li, Jianning Pepe, Antonio Gsaxner, Christina Egger, Jan Institute of Computer Graphics and Vision Graz University of Technology Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz

We introduce a fully automatic system for cranial implant design, a common task in cranioplasty operations. The system is currently integrated in Studierfenster (http://***/), an online, cloud-based medical image processing platform for medical imaging applications. Enhanced by deep learning algorithms, the system automatically restores the missing part of a skull (i.e., skull shape completion) and generates the desired implant by subtracting the defective skull from the completed skull. The generated implant can be downloaded in the STereoLithography (.stl) format directly via the browser interface of the system. The implant model can then be sent to a 3D printer for in loco implant manufacturing. Furthermore, thanks to the standard format, the user can thereafter load the model into another application for post-processing whenever necessary. Such an automatic cranial implant design system can be integrated into the clinical practice to improve the current routine for surgeries related to skull defect repair (e.g., cranioplasty). Our system, although currently intended for educational and research use only, can be seen as an application of additive manufacturing for fast, patient-specific implant design. Copyright © 2020, The Authors. All rights reserved.

关键词： 3D printers