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arXiv 2021年

作者： Jin, Yuan Pepe, Antonio Li, Jianning Gsaxner, Christina Zhao, Fen-Hua Pomykala, Kelsey L. Kleesiek, Jens Frangi, Alejandro F. Egger, Jan Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz8010 Austria Computer Algorithms for Medicine Laboratory Graz Austria Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China Girardetstraße 2 Essen45131 Germany Department of Radiology Affiliated Dongyang Hospital of Wenzhou Medical University Zhejiang Dongyang322100 China Hufelandstraße 55 Essen45147 Germany Partner Site Essen Hufelandstraße 55 Essen45147 Germany School of Computing University of Leeds Leeds United Kingdom School of Medicine University of Leeds Leeds United Kingdom

The aortic vessel tree is composed of the aorta and its branching arteries, and plays a key role in supplying the whole body with blood. Aortic diseases, like aneurysms or dissections, can lead to an aortic rupture, whose treatment with open surgery is highly risky. Therefore, patients commonly undergo drug treatment under constant monitoring, which requires regular inspections of the vessels through imaging. The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if completed with a contrast agent, called CT angiography (CTA). Optimally, the whole aortic vessel tree geometry from consecutive CTAs is overlaid and compared. This allows not only detection of changes in the aorta, but also of its branches, caused by the primary pathology or newly developed. When performed manually, this reconstruction requires slice by slice contouring, which could easily take a whole day for a single aortic vessel tree, and is therefore not feasible in clinical practice. Automatic or semi-automatic vessel tree segmentation algorithms, however, can complete this task in a fraction of the manual execution time and run in parallel to the clinical routine of the clinicians. In this paper, we systematically review computing techniques for the automatic and semi-automatic segmentation of the aortic vessel tree. The review concludes with an in-depth discussion on how close these state-of-the-art approaches are to an application in clinical practice and how active this research field is, taking into account the number of publications, datasets and challenges. Copyright © 2021, The Authors. All rights reserved.

关键词： Diseases

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FakeNews: GAN-based generation of realistic 3D volumetric data A systematic review and taxonomy

arXiv

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arXiv 2022年

作者： Ferreira, André Li, Jianning Alves, Victor Pomykala, Kelsey L. Kleesiek, Jens Egger, Jan Center Algoritmi University of Minho Braga Portugal Computer Algorithms for Medicine Laboratory Graz Austria University Medicine Essen Girardetstraße 2 Essen45131 Germany 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 TU Dortmund University Department of Physics Otto-Hahn-Straße 4 Dortmund44227 Germany Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Aachen52074 Germany Institute of Medical Informatics University Hospital RWTH Aachen Aachen52074 Germany

With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is very important in medicine, as it ranges from disease diagnoses to therapy monitoring. When the dataset is sufficient, models can be trained to help doctors with these tasks. Unfortunately, there are scenarios where large amounts of data is unavailable. For example, rare diseases and privacy issues can lead to restricted data availability. In non-medical fields, the high cost of obtaining enough high-quality data can also be a concern. A solution to these problems can be the generation of realistic synthetic data using Generative Adversarial Networks (GANs). The existence of these mechanisms is a good asset, especially in healthcare, as the data must be of good quality, realistic, and without privacy issues. Therefore, most of the publications on volumetric GANs are within the medical domain. In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs. We therefore outline GAN-based methods in these areas with common architectures, loss functions and evaluation metrics, including their advantages and disadvantages. We present a novel taxonomy, evaluations, challenges, and research opportunities to provide a holistic overview of the current state of volumetric GANs. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Generalisation of Segmentation Using Generative Adversarial Networks

Generalisation of Segmentation Using Generative Adversarial ...

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IEEE International Symposium on Biomedical Imaging

作者： André Ferreira Gijs Luijten Behrus Puladi Jens Kleesiek Victor Alves Jan Egger Institute for AI in Medicine (IKIM) University Hospital Essen (AöR) Essen Germany Center Algoritmi / LASI University of Minho Braga Portugal Computer Algorithms for Medicine Laboratory Graz Austria Institute of Medical Informatics University Hospital RWTH Aachen Aachen Germany Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Aachen Germany Institute of Computer Graphics and Vision (ICG) Graz University of Technology Graz Austria Cancer Research Center Cologne Essen (CCCE) West German Cancer Center Essen (WTZ) University Hospital Essen (AöR) Essen Germany Partner Site Essen German Cancer Consortium (DKTK) Essen Germany Department of Physics TU Dortmund University Dortmund Germany Center for Virtual and Extended Reality in Medicine (ZvRM) University Hospital Essen Essen Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

State-of-the-art deep learning algorithms are easily biased and evaluated in misleading scenarios, especially in the medical context, where scenarios change rapidly and diseases develop quickly. The BraTS 2024 GoAT challenge aims to evaluate how brain tumour segmentation algorithms can adapt to different circumstances when these are not available for training. Our solution utilises state-of-the-art conditional generative adversarial networks to generate realistic new cases and train a segmentation algorithm that takes advantage of the convolutions and attention mechanisms. Our solution achieved a DSC value of 0.855, 0.863, 0.883 and an HD95 value of 24.83, 24.10 and 21.72 for the enhancing tumour, the tumour core and the whole tumour in the validation set, respectively.

关键词： Training Knowledge engineering Deep learning Generative adversarial networks Robustness Inference algorithms Tumors

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Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation

arXiv

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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

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TimeTubes: Visual Exploration of Observed Blazar Datasets

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Journal of Physics: Conference Series 2018年第1期1036卷

作者： Issei Fujishiro Naoko Sawada Masanori Nakayama Hsiang-Yun Wu Kazuho Watanabe Shigeo Takahashi Makoto Uemura Center for Information and Computer Science Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama 223-8522 Japan Current address: Institute of Computer Graphics and Algorithms TU Wien Favoritenstr. 9 / 5. Stock (5th floor) A-1040 Vienna Austria Department of Computer Science and Engineering Toyohashi University of Technology 1-1 Hibarigaoka Tempaku-cho Toyohashi 441-8580 Japan School of Computer Science and Engineering University of Aizu Aizu Wakamatsu Fukushima 965-8580 Japan Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima 739-8526 Japan

Blazars are attractive objects for astronomers to observe in order to burrow into the magnetic field in the relativistic jet. This paper presents TimeTubes as a novel visualization scheme that allows astronomers to interactively explore characteristic temporal variation patterns in observed blazar datasets. In the TimeTubes spatialization, the two Stokes parameters and their errors with a common timestamp are transformed into an ellipse. A series of such ellipses are aligned in parallel along the timeline to form a 3D volumetric tube. The resulting tube is then colorized by the observed intensities and colors of the blazar, and finally volume-rendered. A designated user interface is provided with visual exploration functions according to Shneiderman's Visual Information Seeking Mantra. In the latest version, an auxiliary mechanism, called visual data fusion, was incorporated to ameliorate data- and mapping-inherent uncertainties for more efficient and effective visual exploration.

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Preface

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2001年 2037卷 V-VI页

作者： Boers, Egbert J. W. Cagnoni, Stefano Gottlieb, Jens Hart, Emma Lanzi, Pier Luca Raidl, Günther R. Smith, Robert E. Tijink, Harald Leiden Institute of Advanced Computer Science Leiden University Niels Bohrweg 1 Leiden Netherlands Dept. of Computer Engineering University of Parma Parco Area delle Scienze 181/a Parma Italy SAP AG Neurottstrasse 16 Walldorf Germany Napier University School of Computing 219 Colinton Road Edinburgh United Kingdom Polytechnic of Milan Piazza Leonardo da Vinci 32 Milan Italy Algorithms and Data Structur es Group Institute of Computer Graphics Vienna University of Technology Favoritenstrasse 9-11/186 Vienna Austria The Intelligent Computer Systems Centre The University of The West of England Coldharbour Lane Frenchay Bristol United Kingdom Data and Knowledge Systems Department National Aerospace Laboratory NLR P.O. Box 153 Emmeloord Netherlands

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Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo

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ACM Computing Surveys 1000年

作者： Yuan Jin Antonio Pepe Jianning Li Christina Gsaxner Yuxuan Chen Behrus Puladi Fen-hua Zhao Kelsey Pomykala Jens Kleesiek Alejandro Frangi Jan Egger Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Research Centre for Frontier Fundamental Studies Zhejiang Lab Hangzhou China Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Institute for Artificial Intelligence in Medicine Essen University Hospital (AöR) Essen Germany Zhejiang Laboratory of Philosophy and Social Sciences - Laboratory of Intelligent Society and Governance Zhejiang Lab Hangzhou China Institute of Medical Informatics RWTH Aachen University Aachen Germany Wenzhou Medical University Affiliated Dongyang Hospital Dongyang China Cancer Research Center Cologne Essen Essen Germany German Cancer Consortium Heidelberg Germany Christabel Pankhurst Institute The University of Manchester Manchester United Kingdom of Great Britain and Northern Ireland School of Health Sciences The University of Manchester Division of Informatics Imaging and Data Sciences Manchester United Kingdom of Great Britain and Northern Ireland The University of Manchester School of Computer Science Manchester United Kingdom of Great Britain and Northern Ireland Manchester University NHS Foundation Trust Manchester United Kingdom of Great Britain and Northern Ireland Insitute of Artificial Intelligence in Medicine Essen University Hospital (AöR) Essen Germany Computer Algorithms for Medicine Laboratory Graz Austria Cancer Research Center Cologne Essen (CCCE) Essen Germany

The aortic vessel tree, composed of the aorta and its branches, is crucial for blood supply to the body. Aortic diseases, such as aneurysms and dissections, can lead to life-threatening ruptures, often requiring open surgery. Therefore, patients commonly undergo treatment under constant monitoring, which requires regular inspections of the vessels through medical imaging techniques. Overlapping and comparing aortic vessel tree geometries from consecutive images allows for tracking changes in both the aorta and its branches. Manual reconstruction of the vessel tree is time-consuming and impractical in clinical settings. In contrast, automatic or semi-automatic segmentation algorithms can perform this task much faster, making them suitable for routine clinical use. This paper systematically reviews methods for the automatic and semi-automatic segmentation of the aortic vessel tree, concluding with a discussion on their clinical applicability, the current research landscape, and ongoing challenges.

关键词： Aorta Vessel Tree Machine Learning Deep Learning Segmentation Analysis Cardiovascular Disease Aneurysm Dissection Computed Tomography.

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Corrigendum to: GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy [Medical Image Analysis 93 (2024)]

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Medical image analysis 2024年 96卷 103174页

作者： André Ferreira Jianning Li Kelsey L Pomykala Jens Kleesiek Victor Alves Jan Egger Institute for AI in Medicine (IKIM) University Hospital Essen University Duisburg-Essen Girardetstraße 2 Essen 45131 Germany Center Algoritmi/LASI University of Minho Braga 4710-057 Portugal Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen 52074 Aachen Germany Institute of Medical Informatics University Hospital RWTH Aachen 52074 Aachen Germany. Electronic address: id10656@alunos.uminho.pt. Cancer Research Center Cologne Essen (CCCE) University Medicine Essen Hufelandstraße 55 Essen 45147 Germany. Computer Algorithms for Medicine Laboratory Graz Austria. Cancer Research Center Cologne Essen (CCCE) University Medicine Essen Hufelandstraße 55 Essen 45147 Germany German Cancer Consortium (DKTK) Partner Site Essen Hufelandstraße 55 Essen 45147 Germany TU Dortmund University Department of Physics Otto-Hahn-Straße 4 44227 Dortmund Germany. Institute for AI in Medicine (IKIM) University Hospital Essen University Duisburg-Essen Girardetstraße 2 Essen 45131 Germany. Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz 801 Austria.

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