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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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Beyond 2D: Fusion of Monocular 3D Pose, Motion and Appearance for Human Action Recognition

Beyond 2D: Fusion of Monocular 3D Pose, Motion and Appearanc...

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International Conference on Information Fusion

作者： Wei Lin Jie Yu Institute of Computer Graphics and Vision Graz Technical University Graz Austria Computer Vision Research Lab Robert Bosch GmbH Hildesheim Germany

ISBN: (数字)9780996452786

ISBN: (纸本)9781728118406

Human pose has proved to be an effective representation for action recognition in video. However, traditional 2D features extracted from videos suffer from the high variations caused by viewpoint changes and projection. In this paper, we investigate the recent monocular 3D pose estimation technology for action recognition and perform a cross-modality analysis by comparing 2D, monocular 3D and Kinect 3D poses in terms of action recognition, especially under cross-viewpoint settings. We show that our proposed monocular 3D pose action recognition pipeline achieves superior results even without real depth information as input. Our proposed three-stream fusion of 3D pose, motion and appearance outperforms state-of-the-art methods on sub-JHMDB, Penn Action and NTU RGB+D datasets.

关键词： Three-dimensional displays Tensile stress Pose estimation Pipelines Two dimensional displays Skeleton Feature extraction

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Reduct-based ranking of attributes

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Procedia computer Science 2020年 176卷 2576-2585页

作者： Beata Zielosko Urszula Stańczyk Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting factor was defined, based on relative reducts. A reduct constitutes an embedded mechanism of feature selection, specific to rough set theory. The proposed factor takes into account the number of reducts in which a given attribute exists, as well as lengths of reducts. Two approaches for reduct generation were employed and compared, with search executed by a genetic algorithm. To validate the usefulness of the reduct-based rankings in the process of feature reduction, for gradually decreasing subsets of attributes, selected through rankings, sets of decision rules were induced in classical rough set approach. The performance of all rule classifiers was evaluated, and experimental results showed that the proposed rankings led to at least the same, or even increased classification accuracy for reduced sets of features than in the case of operating on the entire set of condition attributes. The experiments were performed on datasets from stylometry domain, with treating authorship attribution as a classification task, and stylometric descriptors as characteristic features defining writing styles.

关键词： feature reduction reduct ranking of attributes rough sets decision rules classification stylometry

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Assessing quality of decision reducts

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Procedia computer Science 2020年 176卷 3273-3282页

作者： Urszula Stańczyk Beata Zielosko Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 41-200 Sosnowiec Poland

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution.

关键词： reduct rough sets decision rules classification stylometry feature reduction

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AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER

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Annals of the Rheumatic Diseases 2023年 82卷 2012-2012页

作者： P. Mielnik A. Myhre Hjelle A. Traseth K. Tokarz B. Pollen M. Fojcik Helse Forde Section for Rheumatology； Department for Neurology Rheumatology and Physical Medicine Forde Norway Institute of Informatics Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland Western Norway University of Applied Sciences Faculty of Engineering and Science Forde Norway

Background Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AIIRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AIIRD are lacking. Objectives This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AIIRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. Methods Subjects with AIIRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AIIRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AIIRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant anal

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Evaluating Spatial Configuration Constrained CNNs for Localizing Facial and Body Pose Landmarks

Evaluating Spatial Configuration Constrained CNNs for Locali...

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International Conference on Image and vision Computing New Zealand, IVCNZ

作者： Christian Payer Darko Štern Martin Urschler Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz Austria School of Computer Science The University of Auckland Auckland New Zealand

Landmark localization is a widely used task required in medical image analysis and computer vision applications. Formulated in a heatmap regression framework, we have recently proposed a CNN architecture that learns on its own to split the localization task into two simpler sub-problems, dedicating one component to locally accurate but ambiguous predictions, while the other component improves robustness by incorporating the spatial configuration of landmarks to remove ambiguities. We learn this simplification in our SpatialConfiguration-Net (SCN) by multiplying the heatmap predictions of its two components and by training the network in and end-to-end manner, thus achieving regularization similar to e.g. a hand-crafted Markov Random Field model. While we have previously shown localization results solely on data from 2D and 3D medical imaging modalities, in this work our aim is to study the generalization capabilities of our SpatialConfiguration-Net to computer vision problems. Therefore, we evaluate our performance both in terms of accuracy and robustness on a facial alignment task, where we improve upon the state-of-the-art methods, as well as on a human body pose estimation task, where we demonstrate results in line with the recent state-of-the-art.

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Medical deep learning-A systematic meta-review

arXiv

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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

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Accelerating Prostate Diffusion Weighted MRI using Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study

arXiv

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arXiv 2020年

作者： Kaye, Elena A. Aherne, Emily A. Duzgol, Cihan Häggström, Ida Kobler, Erich Mazaheri, Yousef Fung, Maggie M. Zhang, Zhigang Otazo, Ricardo Vargas, Herbert A. Akin, Oguz Department of Medical Physics Memorial Sloan Kettering Cancer Center Department of Radiology Memorial Sloan Kettering Cancer Center Institute of Computer Graphics and Vision Graz University of Technology Graz Austria MR Applications & Workflow Team GE Healthcare Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center

Purpose: To investigate feasibility of accelerating prostate diffusion-weighted imaging (DWI) by reducing the number of acquired averages and denoising the resulting image using a proposed guided denoising convolutional neural network (DnCNN). Materials and Methods: Raw data from the prostate DWI scans were retrospectively gathered (between July 2018 and July 2019) from six single-vendor MRI scanners. 118 data sets were used for training and validation (age: 64.3 ± 8 years) and 37 - for testing (age: 65.1 ± 7.3 years). High b-value diffusion-weighted (hb-DW) data were reconstructed into noisy images using two averages and reference images using all sixteen averages. A conventional DnCNN was modified into a guided DnCNN, which uses the low b-value DWI image as a guidance input. Quantitative and qualitative reader evaluations were performed on the denoised hb-DW images. A cumulative link mixed regression model was used to compare the readers’ scores. The agreement between the apparent diffusion coefficient (ADC) maps (denoised vs reference) was analyzed using Bland Altman analysis. Results: Compared to the DnCNN, the guided DnCNN produced denoised hb-DW images with higher peak signal-to-noise ratio and structural similarity index and lower normalized mean square error (p Copyright © 2020, The Authors. All rights reserved.

关键词： Urology

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Casting geometric constraints in semantic segmentation as semi-supervised learning

arXiv

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arXiv 2019年

作者： Stekovic, Sinisa Fraundorfer, Friedrich Lepetit, Vincent Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Université Paris-Est École des Ponts ParisTech Paris France

We propose a simple yet effective method to learn to segment new indoor scenes from video frames: State-ofthe- art methods trained on one dataset, even as large as the SUNRGB-D dataset, can perform poorly when applied to images that are not part of the dataset, because of the dataset bias, a common phenomenon in computer vision. To make semantic segmentation more useful in practice, one can exploit geometric constraints. Our main contribution is to show that these constraints can be cast conveniently as semi-supervised terms, which enforce the fact that the same class should be predicted for the projections of the same 3D location in different images. This is interesting as we can exploit general existing techniques developed for semi-supervised learning to efficiently incorporate the constraints. We show that this approach can efficiently and accurately learn to segment target sequences of ScanNet and our own target sequences using only annotations from SUNRGB-D, and geometric relations between the video frames of target sequences. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Location field descriptors: Single image 3D model retrieval in the wild

arXiv

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arXiv 2019年

作者： Grabner, Alexander Roth, Peter M. Lepetit, Vincent Institute of Computer Graphics and Vision Graz University of Technology Austria Laboratoire Bordelais de Recherche en Informatique University of Bordeaux France

We present Location Field Descriptors, a novel approach for single image 3D model retrieval in the wild. In contrast to previous methods that directly map 3D models and RGB images to an embedding space, we establish a common low-level representation in the form of location fields from which we compute pose invariant 3D shape descriptors. Location fields encode correspondences between 2D pixels and 3D surface coordinates and, thus, explicitly capture 3D shape and 3D pose information without appearance variations which are irrelevant for the task. This early fusion of 3D models and RGB images results in three main advantages: First, the bottleneck location field prediction acts as a regularizer during training. Second, major parts of the system benefit from training on a virtually infinite amount of synthetic data. Finally, the predicted location fields are visually interpretable and unblackbox the system. We evaluate our proposed approach on three challenging real-world datasets (Pix3D, Comp, and Stanford) with different object categories and significantly outperform the state-of-the-art by up to 20% absolute in multiple 3D retrieval metrics. Copyright © 2019, The Authors. All rights reserved.

关键词： Location

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