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2nd International Workshop on Thoracic Image Analysis, TIA 2020 Held in Conjunction with Medical Image Computing and computer-Assisted Intervention Conference, MICCAI 2020

作者： Pepe, Antonio Mistelbauer, Gabriel Gsaxner, Christina Li, Jianning Fleischmann, Dominik Schmalstieg, Dieter Egger, Jan Institute of Computer Graphics and Vision Graz University of Technology Graz Austria School of Medicine Department of Radiology Stanford University StanfordCA United States Computer Algorithms in Medicine Laboratory Graz Austria Department of Simulation and Graphics University of Magdeburg Magdeburg Germany Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria

ISBN: (纸本)9783030624682

Aortic dissection (AD) is a condition of the main artery of the human body, resulting in the formation of a new flow channel, or false lumen (FL). The disease is usually diagnosed with a computed tomography angiography (CTA) scan during the acute phase. A better understanding of the causes of AD requires knowledge of aortic geometry prior to the event, which is available only in very rare circumstances. In this work, we propose an approach to reconstruct the aorta before the formation of a dissection by performing 3D inpainting with a two-stage generative adversarial network (GAN). In the first stage of our two-stage GAN, a network is trained on the 3D edge information of the healthy aorta to reconstruct the aortic wall. The second stage infers the image information of the aorta to reconstruct the entire dataset. We train our two-stage GAN with 3D patches from 55 non-dissected aortic datasets and evaluate it on 20 more non-dissected datasets, demonstrating that our proposed 3D architecture outperforms its 2D counterpart. To obtain pre-dissection aortae, we mask the entire FL in AD datasets. Finally, we provide qualitative feedback from a renown expert on the obtained pre-dissection cases. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Brain Tumour Removing and Missing Modality Generation using 3D Wavelet Diffusion Model

arXiv

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arXiv 2024年

作者： Ferreira, André Luijten, Gijs 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 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 second-placed solution for task 8 and the participation solution for task 7 of BraTS 2024. The adoption of automated brain analysis algorithms to support clinical practice is increasing. However, many of these algorithms struggle with the presence of brain lesions or the absence of certain MRI modalities. The alterations in the brain’s morphology leads to high variability and thus poor performance of predictive models that were trained only on healthy brains. The lack of information that is usually provided by some of the missing MRI modalities also reduces the reliability of the prediction models trained with all modalities. In order to improve the performance of these models, we propose the use of conditional 3D wavelet diffusion models. The wavelet transform enabled full-resolution image training and prediction on a GPU with 48 GB VRAM, without patching or downsampling, preserving all information for prediction. The code for these tasks is available at https://***/ShadowTwin41/BraTS_2023_2024_solutions. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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

arXiv

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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

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

arXiv

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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

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Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation

arXiv

引用

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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Generalisation of Segmentation Using Generative Adversarial Networks

Generalisation of Segmentation Using Generative Adversarial ...

引用

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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Depth-Awareness in a System for Mixed-Reality Aided Surgical Procedures 15th

Depth-Awareness in a System for Mixed-Reality Aided Surgical...

引用

15th International Conference on Intelligent Computing, ICIC 2019

作者： Sylos Labini, Mauro Gsaxner, Christina Pepe, Antonio Wallner, Juergen Egger, Jan Bevilacqua, Vitoantonio Institute for Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Electrical and Information Engineering Polytechnic University of Bari Bari Italy Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria

ISBN: (纸本)9783030267650

computer-assisted surgery is a trending topic in research, with many different approaches which aim at supporting surgeons in the operating room. Existing surgical planning and navigation solutions are often considered to be distracting, unintuitive or hard to interpret. In this work, we address this issue with an approach based on mixed reality devices like Microsoft HoloLens. We assess the depth sensing capabilities of Microsoft HoloLens, and the potential benefit they could bring to computer-assisted surgery applications. © 2019, Springer Nature Switzerland AG.

关键词： Mixed reality

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A client/server based online environment for manual segmentation of medical images

arXiv

引用

arXiv 2019年

作者： Wild, Daniel Weber, Maximilian Egger, Jan Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Computer Algorithms for Medicine Lab Graz Austria

Segmentation is a key step in analyzing and processing medical images. Due to the low fault tolerance in medical imaging, manual segmentation remains the de facto standard in this domain. Besides, efforts to automate the segmentation process often rely on large amounts of manually labeled data. While existing software supporting manual segmentation is rich in features and delivers accurate results, the necessary time to set it up and get comfortable using it can pose a hurdle for the collection of large datasets. This work introduces a client/server based online environment, referred to as Studierfenster, that can be used to perform manual segmentations directly in a web browser. The aim of providing this functionality in the form of a web application is to ease the collection of ground truth segmentation datasets. Providing a tool that is quickly accessible and usable on a broad range of devices, offers the potential to accelerate this process. The manual segmentation workflow of Studierfenster consists of dragging and dropping the input file into the browser window and sliceby- slice outlining the object under consideration. The final segmentation can then be exported as a file storing its contours and as a binary segmentation mask. In order to evaluate the usability of Studierfenster, a user study was performed. The user study resulted in a mean of 6.3 out of 7.0 possible points given by users, when asked about their overall impression of the tool. The evaluation also provides insights into the results achievable with the tool in practice, by presenting two ground truth segmentations performed by physicians. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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

arXiv

引用

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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Deep multi-spectral registration using invariant descriptor learning 25

Deep multi-spectral registration using invariant descriptor ...

引用

25th IEEE International Conference on Image Processing, ICIP 2018

作者： Ofir, Nati Silberstein, Shai Levi, Hila Rozenbaum, Dani Keller, Yosi Duvdevani Bar, Sharon Elbit Systems Bar-Ilan University Computer Vision and Algorithms Ltd Weizmann Institute Israel

ISBN: (纸本)9781479970612

In this work, we propose a deep-learning approach for aligning cross-spectral images. Our approach utilizes a learned descriptor invariant to different spectra. Multi-modal images of the same scene capture different characteristics and therefore their registration is challenging. To that end, we developed a feature-based approach for registering visible (VIS) to Near-Infra-Red (NIR) images. Our scheme detects corners by Harris and matches them by a patch-metric learned on top of a network trained using the CIFAR-10 dataset. As our experiments demonstrate, we achieve accurate alignment of cross-spectral images with sub-pixel accuracy. Comparing to contemporary state-of-the-art, our approach is more accurate in the task of VIS to NIR registration. © 2018 IEEE.

关键词： Spectroscopy

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