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

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

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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Multi-Source and Multi-Sequence Myocardial Pathology Segmentation Using a Cascading Refinement CNN

arXiv

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arXiv 2024年

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

Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases and consequently, a major cause for mortality and morbidity worldwide. Accurate assessment of myocardial tissue viability for post-MI patients is critical for diagnosis and treatment planning, e.g. allowing surgical revascularization, or to determine the risk of adverse cardiovascular events in the future. Fine-grained analysis of the myocardium and its surrounding anatomical structures can be performed by combining the information obtained from complementary medical imaging techniques. In this work, we use late gadolinium enhanced (LGE) magnetic resonance (MR), T2-weighted (T2) MR and balanced steady-state free precession (bSSFP) cine MR in order to semantically segment the left and right ventricle, healthy and scarred myocardial tissue, as well as edema. To this end, we propose the Multi-Sequence Cascading Refinement CNN (MS-CaRe-CNN), a 2-stage CNN cascade that receives multi-sequence data and generates predictions of the anatomical structures of interest without considering tissue viability at Stage 1. The prediction of Stage 1 is then further refined in Stage 2, where the model additionally distinguishes myocardial tissue based on viability, i.e. healthy, scarred and edema regions. Our proposed method is set up as a 5-fold ensemble and semantically segments scar tissue achieving 62.31% DSC and 82.65% precision, as well as 63.78% DSC and 87.69% precision for the combined scar and edema region. These promising results for such small and challenging structures confirm that MS-CaRe-CNN is well-suited to generate semantic segmentations to assess the viability of myocardial tissue, enabling downstream tasks like personalized therapy planning. © 2024, CC BY-SA.

关键词： Medical imaging

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Learning Gradually Non-convex Image Priors Using Score Matching

arXiv

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arXiv 2023年

作者： Kobler, Erich Pock, Thomas Department of Neuroradiology University Hospital Bonn Bonn Germany Institue of Computer Graphics and Vision Graz University of Technology Graz Austria

In this paper, we propose a unified framework of denoising score-based models in the context of graduated non-convex energy minimization. We show that for sufficiently large noise variance, the associated negative log density - the energy - becomes convex. Consequently, denoising score-based models essentially follow a graduated non-convexity heuristic. We apply this framework to learning generalized Fields of Experts image priors that approximate the joint density of noisy images and their associated variances. These priors can be easily incorporated into existing optimization algorithms for solving inverse problems and naturally implement a fast and robust graduated non-convexity mechanism. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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GraMMaCAD: INTERACTIVELY DEFINING SPATIALLY VARYING FGMS ON BREP CAD MODELS

GraMMaCAD: INTERACTIVELY DEFINING SPATIALLY VARYING FGMS ON ...

引用

ASME 2022 International Design Engineering Technical Conferences and computers and Information in Engineering Conference, IDETC-CIE 2022

作者： Luu, Thu Huong Altenhofen, Christian Stork, André Fellner, Dieter TU Darmstadt Interactive Graphics Systems Group Darmstadt64283 Germany Fraunhofer IGD Darmstadt64283 Germany Graz University of Technology Institute of Computer Graphics and Knowledge Visualization Graz8010 Austria

ISBN: (纸本)9780791886212

Additive manufacturing (AM) has enabled control over heterogeneous materials and structures in ways that were not previously possible, including functionally graded materials (FGM). However, typical computer aided design (CAD) systems – mainly based on boundary representations (B-Rep) – are not built for modeling and representing spatially varying material information, which are the key features for designing FGM parts. While volumetric representations (V-Rep) such as tetrahedral or hexahedral meshes, trivariate splines or subdivision solids can hold the necessary FGM information, they are by default not compatible with existing CAD models. Thus, the capabilities of AM motivate enhancing CAD systems to support volumetric material allocation and distribution. We present an interactive, intuitive and efficient interaction concept based on grading features and transition functions for augmenting CAD models with smooth volumetric material gradients for multi-material fabrication using our system called GraMMaCAD (Graded Multi-Material CAD). This concept has the advantage of being extremely versatile, since the material property functions are left to the arbitrariness of the user, who is free to modify their definition as needed. By providing interactive preview modes, the system allows for efficient exploration of the volumetric gradation of the designed FGM part. We fabricated two functional examples that demonstrate the usability of the system and show the ability to quickly augment models with functionally graded material transitions. Copyright © 2022 by ASME.

关键词： Functionally graded materials

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vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection

arXiv

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arXiv 2024年

作者： Fruhwirth-Reisinger, Christian Lin, Wei Malić, Dušan Bischof, Horst Possegger, Horst Christian Doppler Laboratory for Embedded Machine Learning Austria Institute of Computer Graphics and Vision Graz University of Technology Austria Institute for Machine Learning Johannes Kepler University Linz Austria

Accurate 3D object detection in LiDAR point clouds is crucial for autonomous driving systems. To achieve state-of-the-art performance, the supervised training of detectors requires large amounts of human-annotated data, which is expensive to obtain and restricted to predefined object categories. To mitigate manual labeling efforts, recent unsupervised object detection approaches generate class-agnostic pseudo-labels for moving objects, subsequently serving as supervision signal to bootstrap a detector. Despite promising results, these approaches do not provide class labels or generalize well to static objects. Furthermore, they are mostly restricted to data containing multiple drives from the same scene or images from a precisely calibrated and synchronized camera setup. To overcome these limitations, we propose a vision-language-guided unsupervised 3D detection approach that operates exclusively on LiDAR point clouds. We transfer CLIP knowledge to classify point clusters of static and moving objects, which we discover by exploiting the inherent spatio-temporal information of LiDAR point clouds for clustering, tracking, as well as box and label refinement. Our approach outperforms state-of-the-art unsupervised 3D object detectors on the Waymo Open Dataset (+23 AP3D) and Argoverse 2 (+7.9 AP3D) and provides class labels not solely based on object size assumptions, marking a significant advancement in the field. Code will be available at https://***/chreisinger/ViLGOD. © 2024, CC BY-NC-SA.

关键词： Visual languages

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Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI

arXiv

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arXiv 2024年

关键词： Gaussian distribution

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Teeth Localization and Lesion Segmentation in CBCT Images using SpatialConfiguration-Net and U-Net

arXiv

引用

arXiv 2023年

作者： Hadzic, Arnela Kirnbauer, Barbara Štern, Darko Urschler, Martin Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria Department of Dental Medicine and Oral Health Medical University of Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

The localization of teeth and segmentation of periapical lesions in cone-beam computed tomography (CBCT) images are crucial tasks for clinical diagnosis and treatment planning, which are often time-consuming and require a high level of expertise. However, automating these tasks is challenging due to variations in shape, size, and orientation of lesions, as well as similar topologies among teeth. Moreover, the small volumes occupied by lesions in CBCT images pose a class imbalance problem that needs to be addressed. In this study, we propose a deep learning-based method utilizing two convolutional neural networks: the SpatialConfiguration-Net (SCN) and a modified version of the U-Net. The SCN accurately predicts the coordinates of all teeth present in an image, enabling precise cropping of teeth volumes that are then fed into the U-Net which detects lesions via segmentation. To address class imbalance, we compare the performance of three reweighting loss functions. After evaluation on 144 CBCT images, our method achieves a 97.3% accuracy for teeth localization, along with a promising sensitivity and specificity of 0.97 and 0.88, respectively, for subsequent lesion detection. © 2023, CC BY-SA.

关键词： Convolutional neural networks

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Fuzzy Querying in the Cloud-based Environment for Data Stream-driven Predictive Maintenance in AGV-enabled Smart Factories

Fuzzy Querying in the Cloud-based Environment for Data Strea...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Malysiak-Mrozek, Bozena Romanow, Dominik Grzesik, Piotr Benecki, Pawel Niyomugaba, Alexandre Habimana, Theodore Kostrzewa, Daniel Tokarz, Krzysztof Hung, Che-Lun Mrozek, Dariusz Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Ines Ruhengeri - Institute of Applied Sciences Department of Computer Science Rwanda Silesian University of Technology Department of Applied Informatics Gliwice Poland Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei Taiwan

ISBN: (纸本)9798350362480

Fuzzy data processing enables data enrichment and increases data interpretation in industrial environments. In the cloud-based IoT data ingestion pipelines, fuzzy data processing can be implemented in several locations, closer to the IoT events gateways, stream processors, or the persistence layer before the data is visualized. Since Automated Guided Vehicles (AGV)-enabled manufacturing can produce vast amounts of data, the decision on the placement of the fuzzy data processing can be important for secondary processes performed on the enriched data, like the predictive maintenance inferencing. In this paper, we analyze two locations of fuzzy data processing in the cloud-based environment built for monitoring AGVs in smart factories - by formulating fuzzy queries against data streams on stream processing units and data at rest in a database. The querying scenarios cover fuzzy filtering with simple and complex criteria, fuzzy filtering through assignment to a linguistic variable, and joining data streams by representing joining attributes as fuzzy numbers. The experimental results show that querying the data stream can be more efficient and profitable in the scalable environment of many AGVs. However, the enrichment provided for the data at rest is also beneficial when gathering data for building future predictive maintenance models. © 2024 IEEE.

关键词： Data streams

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Filtering Decision Rules Driven by Sequential Forward and Backward Selection of Attributes: An Illustrative Example in Stylometric Domain 18

Filtering Decision Rules Driven by Sequential Forward and Ba...

引用

18th Conference on computer Science and Intelligence Systems, FedCSIS 2023

作者： Zielosko, Beata Stanczyk, Urszula Jablonski, Kamil Institute of Computer Science University of Silesia in Katowice Bedzínska 39 Sosnowiec41-200 Poland Department of Graphics Silesian University of Technology Computer Vision and Digital Systems Akademicka 2A Gliwice44-100 Poland

ISBN: (纸本)9788396744784

The paper presents investigations concerning the decision rule filtering process controlled by the estimated relevance of available attributes. In the conducted study, two search directions were used, sequential forward selection and sequential backward elimination. The steps of sequential search were governed by three rankings obtained for variables, all related to characteristics of data and rules that can be induced, as follows, (i) a ranking based on the weighting factor referring to the occurrence of attributes in generated decision reducts, (ii) the OneR ranking exploiting short rule properties, and (iii) the proposed ranking defined through the operation of greedy algorithm for rule induction. The three rankings were confronted and compared from the perspective of their usefulness for the selection of rules performed in the two directions and with two strategies for rule selection. The resulting sets of rules were analysed with respect to the properties of the constituent decision rules and from the point of performance for all constructed rule-based classifiers. Substantial experiments were carried out in the stylometric domain, treating the task of authorship attribution as classification. The results obtained indicate that for all three rankings and search paths it was possible to obtain a noticeable reduction of attributes while at least maintaining the power of inducers, at the same time improving characteristics of rule sets. © 2023 Polish Information Processing Society.

关键词： Information use

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Computed Tomography Reconstruction Using Generative Energy-Based Priors

arXiv

引用

arXiv 2022年

作者： Zach, Martin Kobler, Erich Pock, Thomas Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Institute of Computer Graphics Johannes Kepler University Linz Linz4040 Austria

In the past decades, Computed Tomography (CT) has established itself as one of the most important imaging techniques in medicine. Today, the applicability of CT is only limited by the deposited radiation dose, reduction of which manifests in noisy or incomplete measurements. Thus, the need for robust reconstruction algorithms arises. In this work, we learn a parametric regularizer with a global receptive field by maximizing it’s likelihood on reference CT data. Due to this unsupervised learning strategy, our trained regularizer truly represents higher-level domain statistics, which we empirically demonstrate by synthesizing CT images. Moreover, this regularizer can easily be applied to different CT reconstruction problems by embedding it in a variational framework, which increases flexibility and interpretability compared to feed-forward learning-based approaches. In addition, the accompanying probabilistic perspective enables experts to explore the full posterior distribution and may quantify uncertainty of the reconstruction approach. We apply the regularizer to limited-angle and few-view CT reconstruction problems, where it outperforms traditional reconstruction algorithms by a large margin. © 2022, CC BY.

关键词： Image reconstruction

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