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Medical deep learning-A systematic meta-review

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arXiv 2020年

作者： Egger, Jan Gsaxner, Christina Pepe, Antonio Pomykala, Kelsey L. Jonske, Frederic Kurz, Manuel Li, Jianning Kleesiek, Jens Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Inffeldgasse 16 Styria Graz8010 Austria Department of Oral & Maxillofacial Surgery Medical University of Graz Auenbruggerplatz 5/1 Styria Graz8036 Austria Computer Algorithms for Medicine Laboratory Styria Graz Austria University Medicine Essen Girardetstraße 2 Essen45131 Germany University Medicine Essen Hufelandstraße 55 Essen45147 Germany Partner Site Essen Hufelandstraße 55 Essen45147 Germany

Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edge methods. Additionally, deep learning has delivered state-of-the-art results in tasks like autonomous driving, outclassing previous attempts. There are even instances where deep learning outperformed humans, for example with object recognition and gaming. Deep learning is also showing vast potential in the medical domain. With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information. Patient data is not only collected in clinical centers, like hospitals and private practices, but also by mobile healthcare apps or online websites. The abundance of collected patient data and the recent growth in the deep learning field has resulted in a large increase in research effort s. In Q2/2020, the search engine PubMed returned already over 11,000 results for the search term 'deep learning', and around 90% of these publications are from the last three years. However, even though PubMed represents the largest search engine in the medical field, it does not cover all medical-related publications. Hence, a complete overview of the field of 'medical deep learning' is almost impossible to obtain and acquiring a full overview of medical sub-fields is becoming increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been published within the last few years. They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies. With these surveys as a foundation, the aim of this article is to provide the first high-level, systematic meta-review of medical deep learning surveys. Copyright © 2020, The Authors. All rights res

关键词： Generative adversarial networks

Ten simple rules for measuring the impact of workshops

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PLoS Computational Biology 2018年第8期14卷 e1006191-e1006191页

作者： Sufi, Shoaib Nenadic, Aleksandra Silva, Raniere Duckles, Beth Simera, Iveta de Beyer, Jennifer A. Struthers, Caroline Nurmikko-Fuller, Terhi Bellis, Louisa Miah, Wadud Wilde, Adriana Emsley, Iain Philippe, Olivier Balzano, Melissa Coelho, Sara Ford, Heather Jones, Catherine Higgins, Vanessa School of Computer Science University of Manchester Manchester United Kingdom Portland State University PortlandOR United States UK EQUATOR Centre Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology Musculoskeletal Sciences University of Oxford Oxford United Kingdom College of Arts and Social Sciences Australian National University Canberra Australia Cancer Research UK Cambridge Centre University of Cambridge Cambridge United Kingdom Numerical Algorithms Group Oxford United Kingdom School of Computer Science University of St. Andrews St. Andrews United Kingdom Oxford e-Research Centre University of Oxford Oxford United Kingdom Electronics and Computer Science University of Southampton Southampton United Kingdom ELIXIR Hub Wellcome Genome Campus Cambridge United Kingdom EGI Foundation Amsterdam Netherlands School of Media and Communication University of Leeds Leeds United Kingdom Scientific Computing Department Science and Technology Facilities Council Rutherford Appleton Laboratory Didcot United Kingdom School of Social Sciences University of Manchester Manchester United Kingdom

Workshops are used to explore a specific topic, to transfer knowledge, to solve identified problems, or to create something new. In funded research projects and other research endeavours, workshops are the mechanism used to gather the wider project, community, or interested people together around a particular topic. However, natural questions arise: how do we measure the impact of these workshops? Do we know whether they are meeting the goals and objectives we set for them? What indicators should we use? In response to these questions, this paper will outline rules that will improve the measurement of the impact of workshops. © 2018 Sufi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： computer programming

How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation

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arXiv 2024年

作者： Ferreira, André Solak, Naida Li, Jianning Dammann, Philipp 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 Department of Neurosurgery and Spine Surgery University Hospital Essen Essen Germany

Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are used to massively increase the amount of available samples for training three different deep learning models for brain tumour segmentation, the first task of the BraTS2023 challenge. The first model is the standard nnU-Net, the second is the Swin UNETR and the third is the winning solution of the BraTS 2021 Challenge. The entire pipeline is built on the nnU-Net implementation, except for the generation of the synthetic data. The use of convolutional algorithms and transformers is able to fill each other's knowledge gaps. Using the new metric, our best solution achieves the dice results 0.9005, 0.8673, 0.8509 and HD95 14.940, 14.467, 17.699 (whole tumour, tumour core and enhancing tumour) in the validation set. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier

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arXiv 2022年

作者： Heiliger, Lars Marinov, Zdravko Hasin, Max Ferreira, André Fragemann, Jana Pomykala, Kelsey Murray, Jacob Kersting, David Alves, Victor Stiefelhagen, Rainer Egger, Jan Kleesiek, Jens Institute for Ai in Medicine University Hospital Essen Essen Germany University Medicine Essen Essen Germany Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Center Algoritmi University of Minho Braga Portugal Computer Algorithms for Medicine Laboratory Graz Austria Department of Nuclear Medicine University Hospital Essen Essen Germany Essen Germany West German Cancer Center Essen Germany HIDSS4Health - Helmholtz Information Data Science School for Health Heidelberg Karlsruhe Germany

Tumor volume and changes in tumor characteristics over time are important biomarkers for cancer therapy. In this context, FDGPET/CT scans are routinely used for staging and re-staging of cancer, as the radiolabeled fluorodeoxyglucose is taken up in regions of high metabolism. Unfortunately, these regions with high metabolism are not specific to tumors and can also represent physiological uptake by normal functioning organs, inflammation, or infection, making detailed and reliable tumor segmentation in these scans a demanding task. This gap in research is addressed by the AutoPET challenge, which provides a public data set with FDG-PET/CT scans from 900 patients to encourage further improvement in this field. Our contribution to this challenge is an ensemble of two state-of-the-art segmentation models, the nn-Unet and the Swin UNETR, augmented by a maximum intensity projection classifier that acts like a gating mechanism. If it predicts the existence of lesions, both segmentations are combined by a late fusion approach. Our solution achieves an average Cross Validation Dice score of 72.12% on patients diagnosed with lung cancer, melanoma, and lymphoma in our cross-validation. Code: https://***/heiligerl/AutoPET-Challenge-Submission. © 2022, CC BY.

关键词： Tumors

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

Deep learning - A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

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arXiv 2020年

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Institute of 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 Oral and Maxillofacial Surgery Medical University of Graz Graz Austria University Medicine Essen Essen Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou China Research Unit Experimental Neurotraumatology Department of Neurosurgery Medical University of Graz Graz Austria Knowledge Discovery Know-Center Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,

关键词： Surveys

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

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

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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

AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo

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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