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27th International Conference on Pattern Recognition, ICPR 2024

作者： Hirner, Dominik Fraundorfer, Friedrich Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Cologne Germany

ISBN: (纸本)9783031781919

Stereo estimation has made many advancements in recent years with the introduction of deep-learning. However the traditional supervised approach to deep-learning requires the creation of accurate and plentiful ground-truth data, which is expensive to create and not available in many situations. This is especially true for remote sensing applications, where there is an excess of available data without proper ground truth. To tackle this problem, we propose a self-supervised CNN with self-improving adaptive abilities. In the first iteration, the created disparity map is inaccurate and noisy. Leveraging the left-right consistency check, we get a sparse but more accurate disparity map which is used as an initial pseudo ground-truth. This pseudo ground-truth is then adapted and updated after every epoch in the training step of the network. We use the sum of inconsistent points in order to track the network convergence. The code for our method will be made available after acceptance at https://***/thedodo/SAda-Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Anytime Optimal Trajectory Repairing for Autonomous Vehicles

IEEE Open Journal of Intelligent Transportation Systems

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IEEE Open Journal of Intelligent Transportation Systems 2025年 6卷 537-553页

作者： Tong, Kailin Steinberger, Martin Horn, Martin Solmaz, Selim Watzenig, Daniel Virtual Vehicle Research GmbH Department of Electrics Electronics and Software Graz8010 Austria Institute of Automation and Control Graz University of Technology Graz8010 Austria Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria

Adapting to dynamically changing situations remains a pivotal challenge for automated driving systems, which demand robust and efficient solutions. Occasional perception errors inherent in artificial intelligence further complicate the task. Whereas traditional motion planning algorithms address this challenge by replanning the entire trajectory, a significantly more efficient strategy is to repair only the flawed segments. Our paper introduces a groundbreaking approach by formulating an optimal trajectory repairing problem and proposing an innovative and efficient framework for critical timing detection and trajectory repairing. This trajectory repairing specifically employs Bernstein basis polynomials in both 2D distance-time and 3D spatiotemporal spaces. A distinctive feature of our method is the use of an anytime grid search to determine a sub-optimal time-to-repair, which contrasts with previous methods that relied on manually tuned or fixed repair times, limiting both flexibility and robustness. A statistical analysis of 100 scenarios demonstrates that our trajectory-repairing framework outperforms the path-speed decoupled repairing framework in terms of scenario success rate. Furthermore, we introduce a novel algorithm for driving corridor generation that more accurately approximates the collision-free space than state-of-the-art work. The proposed approach has broad potential for application in embedded systems across various autonomous platforms. © 2020 IEEE.

关键词： Accidents

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Bigger Isn’t Always Better: Towards a General Prior for Medical Image Reconstruction 46th

Bigger Isn’t Always Better: Towards a General Prior for M...

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46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024

作者： Glaszner, Lukas Zach, Martin Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/II Graz8010 Austria

ISBN: (纸本)9783031851803

Diffusion models have been successfully applied to many inverse problems, including MRI and CT reconstruction. Researchers typically re-purpose models originally designed for unconditional sampling without modifications. Using two different posterior sampling algorithms, we show empirically that such large networks are not necessary. Our smallest model, effectively a ResNet, performs almost as good as an attention U-Net on in-distribution reconstruction, while being significantly more robust towards distribution shifts. Furthermore, we introduce models trained on natural images and demonstrate that they can be used in both MRI and CT reconstruction, out-performing model trained on medical images in out-of-distribution cases. As a result of our findings, we strongly caution against simply re-using very large networks and encourage researchers to adapt the model complexity to the respective task. Moreover, we argue that a key step towards a general diffusion-based prior is training on natural images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Inverse problems

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LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar Segmentation 1st

LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar ...

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1st MICCAI Challenge Comprehensive Analysis and Computing of Real-World Medical Images, CARE 2024 Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Thaler, Franz Štern, Darko Plank, Gernot Urschler, Martin Gottfried Schatz Research Center: Medical Physics and Biophysics Medical University of Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria AVL List GmbH Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria

ISBN: (纸本)9783031870088

Atrial fibrillation (AF) represents the most prevalent type of cardiac arrhythmia for which treatment may require patients to undergo ablation therapy. In this surgery cardiac tissues are locally scarred on purpose to prevent electrical signals from causing arrhythmia. Patient-specific cardiac digital twin models show great potential for personalized ablation therapy, however, they demand accurate semantic segmentation of healthy and scarred tissue typically obtained from late gadolinium enhanced (LGE) magnetic resonance (MR) scans. In this work we propose the Left Atrial Cascading Refinement CNN (LA-CaRe-CNN), which aims to accurately segment the left atrium as well as left atrial scar tissue from LGE MR scans. LA-CaRe-CNN is a 2-stage CNN cascade that is trained end-to-end in 3D, where Stage 1 generates a prediction for the left atrium, which is then refined in Stage 2 in conjunction with the original image information to obtain a prediction for the left atrial scar tissue. To account for domain shift towards domains unknown during training, we employ strong intensity and spatial augmentation to increase the diversity of the training dataset. Our proposed method based on a 5-fold ensemble achieves great segmentation results, namely, 89.21% DSC and 1.6969 mm ASSD for the left atrium, as well as 64.59% DSC and 91.80% G-DSC for the more challenging left atrial scar tissue. Thus, segmentations obtained through LA-CaRe-CNN show great potential for the generation of patient-specific cardiac digital twin models and downstream tasks like personalized targeted ablation therapy to treat AF. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ablation

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Augmentation-Based Domain Generalization and Joint Training from Multiple Source Domains for Whole Heart Segmentation 1st

Augmentation-Based Domain Generalization and Joint Training...

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ISBN: (纸本)9783031870088

As the leading cause of death worldwide, cardiovascular diseases motivate the development of more sophisticated methods to analyze the heart and its substructures from medical images like Computed Tomography (CT) and Magnetic Resonance (MR). Semantic segmentations of important cardiac structures that represent the whole heart are useful to assess patient-specific cardiac morphology and pathology. Furthermore, accurate semantic segmentations can be used to generate cardiac digital twin models which allows e.g. electrophysiological simulation and personalized therapy planning. Even though deep learning-based methods for medical image segmentation achieved great advancements over the last decade, retaining good performance under domain shift - i.e. when training and test data are sampled from different data distributions - remains challenging. In order to perform well on domains known at training-time, we employ a (1) balanced joint training approach that utilizes CT and MR data in equal amounts from different source domains. Further, aiming to alleviate domain shift towards domains only encountered at test-time, we rely on (2) strong intensity and spatial augmentation techniques to greatly diversify the available training data. Our proposed whole heart segmentation method, a 5-fold ensemble with our contributions, achieves the best performance for MR data overall and a performance similar to the best performance for CT data when compared to a model trained solely on CT. With 93.33% DSC and 0.8388 mm ASSD for CT and 89.30% DSC and 1.2411 mm ASSD for MR data, our method demonstrates great potential to efficiently obtain accurate semantic segmentations from which patient-specific cardiac twin models can be generated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Transparency Distortion Robustness for SOTA Image Segmentation Tasks 4th

Transparency Distortion Robustness for SOTA Image Segmentat...

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Knauthe, Volker Rak, Arne Wirth, Tristan Pöllabauer, Thomas Metzler, Simon Kuijper, Arjan Fellner, Dieter W. Technical University of Darmstadt Darmstadt Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany CGV Institute Graz University of Technology Graz Austria

ISBN: (纸本)9789819787043

Semantic Image Segmentation facilitates a multitude of real-world applications ranging from autonomous driving over industrial process supervision to vision aids for human beings. These models are usually trained in a supervised fashion using example inputs. Distribution Shifts between these examples and the inputs in operation may cause erroneous segmentations. The robustness of semantic segmentation models against distribution shifts caused by differing camera or lighting setups, lens distortions, adversarial inputs and image corruptions has been topic of recent research. However, robustness against spatially varying radial distortion effects that can be caused by uneven glass structures (e.g. windows) or the chaotic refraction in heated air has not been addressed by the research community yet. We propose a method to synthetically augment existing datasets with spatially varying distortions. Our experiments show, that these distortion effects degrade the performance of state-of-the-art segmentation models. Pretraining and enlarged model capacities proof to be suitable strategies for mitigating performance degradation to some degree, while fine-tuning on distorted images only leads to marginal performance improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantic Segmentation

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Influence of Water Droplet Contamination for Transparency Segmentation 4th

Influence of Water Droplet Contamination for Transparency ...

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Knauthe, Volker Weitz, Paul Pöllabauer, Thomas Wirth, Tristan Rak, Arne Kuijper, Arjan Fellner, Dieter W. Technical University of Darmstadt Darmstadt Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany CGV Institute Graz University of Technology Graz Austria

ISBN: (纸本)9789819787043

computer vision techniques are on the rise for industrial applications, like process supervision and autonomous agents, e.g., in the healthcare domain and dangerous environments. While the general usability of these techniques is high, there are still challenging real-world use-cases. Especially transparent structures, which can appear in the form of glass doors, protective casings or everyday objects like glasses, pose a challenge for computer vision methods. This paper evaluates the combination of transparent objects in conjunction with (naturally occurring) contamination through environmental effects like hazing. We introduce a novel publicly available dataset containing 489 images incorporating three grades of water droplet contamination on transparent structures and examine the resulting influence on transparency handling. Our findings show, that contaminated transparent objects are easier to segment and that we are able to distinguish between different severity levels of contamination with a current state-of-the art machine-learning model. This in turn opens up the possibility to enhance computer vision systems regarding resilience against, e.g., datashifts through contaminated protection casings or implement an automated cleaning alert. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI 15th

Gaussian Process Emulators for Few-Shot Segmentation in Ca...

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15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024

作者： Viti, Bruno Thaler, Franz Kapper, Kathrin Lisa Urschler, Martin Holler, Martin Karabelas, Elias Department of Mathematics and Scientific Computing University of Graz Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Gottfried Schatz Research Center: Medical Physics and Biophysics Medical University of Graz Graz Austria BioTechMed-Graz Graz Austria

ISBN: (纸本)9783031877551

Segmentation of cardiac magnetic resonance images (MRI) is crucial for the analysis and assessment of cardiac function, helping to diagnose and treat various cardiovascular diseases. Most recent techniques rely on deep learning and usually require an extensive amount of labeled data. To overcome this problem, few-shot learning has the capability of reducing data dependency on labeled data. In this work, we introduce a new method that merges few-shot learning with a U-Net architecture and Gaussian Process Emulators (GPEs), enhancing data integration from a support set for improved performance. GPEs are trained to learn the relation between the support images and the corresponding masks in latent space, facilitating the segmentation of unseen query images given only a small labeled support set at inference. We test our model with the M&Ms-2 public dataset to assess its ability to segment the heart in cardiac magnetic resonance imaging from different orientations, and compare it with state-of-the-art unsupervised and few-shot methods. Our architecture shows higher DICE coefficients compared to these methods, especially in the more challenging setups where the size of the support set is considerably small. The code is available on GitLab (https://***/bruno_viti/gpe_4_cardiac_fss). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gaussian distribution

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Bigger Isn’t Always Better: Towards a General Prior for Medical Image Reconstruction

arXiv

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arXiv 2025年

作者： Glaszner, Lukas Zach, Martin Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/II Graz8010 Austria Biomedical Imaging Group École polytechnique fédérale de Lausanne Lausanne1015 Switzerland Center for Biomedical Imaging Lausanne1015 Switzerland

Diffusion model have been successfully applied to many inverse problems, including MRI and CT reconstruction. Researchers typically re-purpose models originally designed for unconditional sampling without modifications. Using two different posterior sampling algorithms, we show empirically that such large networks are not necessary. Our smallest model, effectively a ResNet, performs almost as good as an attention U-Net on in-distribution reconstruction, while being significantly more robust towards distribution shifts. Furthermore, we introduce models trained on natural images and demonstrate that they can be used in both MRI and CT reconstruction, out-performing model trained on medical images in out-of-distribution cases. As a result of our findings, we strongly caution against simply re-using very large networks and encourage researchers to adapt the model complexity to the respective task. Moreover, we argue that a key step towards a general diffusion-based prior is training on natural images. © 2025, CC BY-SA.

关键词： Medical imaging

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Machine-Learning Guided Differentiation between Photoplethysmography Waveforms of Supraventricular and Ventricular Origin

SSRN

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SSRN 2025年

作者： Manninger, Martin Lercher, Ingmar Hermans, Astrid N.L. Isaksen, Jonas L. Prassl, Anton J. Zirlik, Andreas Vernooy, Kevin Chaldoupi, Sevasti-Maria Luermans, Justin G.L.M. ter Bekke, Rachel M.A. Kanters, Jørgen K. Plank, Gernot Scherr, Daniel Pock, Thomas Linz, Dominik Division of Cardiology Department of Internal Medicine Medical University of Graz Graz Austria Maastricht University Medical Centre Maastricht Netherlands Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Department of Biomedical Sciences Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Gottfried Schatz Research Centre Division of Biophysics Medical University of Graz Graz Austria Center for Biological Research University of California San Francisco San Francisco United States

BackgroundIt is unclear, if photoplethysmography (PPG) waveforms from wearable devices can differentiate between supraventricular and ventricular *** assessed, whether a neural network-based classifier can distinguish the origin of PPG pulse *** thirty patients undergoing invasive electrophysiological (EP) studies for narrow complex tachycardia, PPG waveforms were recorded using a PPG wristband (Empatica E4) in parallel to 12-lead surface electrocardiograms (ECGs) and intracardiac bipolar electrograms. PPG waveforms were annotated to either atrial (AP) or ventricular pacing (VP) based on bipolar electrograms, ECGs and stimulation protocols. 25 221 samples were split into training, testing, and validation data sets and used to develop, optimize and validate a residual network based on convolutional layers for classifying PPG waveforms according to their origin into AP or *** were complete for 27 patients. 74% were female, median age was 53 (range 18, 78) years and median BMI was 27±5 kg/m2. The electrophysiological study revealed typical atrioventricular nodal re-entrant tachycardias in 63%, atrial tachycardias in 15% and no inducible tachyarrhythmias in 12% of patients. On an independent patient level, correct prediction was possible in ~73% for AP and ~59% for VP. With the adaptive performance built on previous annotations, the classifier correctly predicted the origins of PPG-derived pulse waves in ~97% for AP and ~95% for *** neural network trained on ground truth PPG data collected during EP studies could distinguish between supraventricular or ventricular origin from PPG waveforms alone. © 2025, The Authors. All rights reserved.

关键词： Photoplethysmography

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