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Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates

arXiv

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arXiv 2025年

作者： Van Santvliet, Lore Zappon, Elena Gsell, Matthias A.F. Thaler, Franz Blondeel, Maarten Dymarkowski, Steven Claessen, Guido Willems, Rik Urschler, Martin Vandenberk, Bert Plank, Gernot De Vos, Maarten KU Leuven Leuven Belgium Division of Medical Physics and Biophysics Gottfried Schatz Research Center Medical University of Graz Graz Austria BioTechMed-Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria Department of Cardiology University Hospitals Leuven Leuven Belgium Department of Cardiovascular Sciences KU Leuven Leuven Belgium Division of Radiology University Hospitals Leuven Leuven Belgium Division of Cardiology Hartcentrum Jessa Ziekenhuis Hasselt Belgium Department of Medicine and Life Sciences University of Hasselt Hasselt Belgium

A cardiac digital twin is a virtual replica of a patient-specific heart, mimicking its anatomy and physiology. A crucial step of building a cardiac digital twin is anatomical twinning, where the computational mesh of the digital twin is tailored to the patient-specific cardiac anatomy. In a number of studies, the effect of anatomical variation on clinically relevant functional measurements like electrocardiograms (ECGs) is investigated, using computational simulations. While such a simulation environment provides researchers with a carefully controlled ground truth, the impact of anatomical differences on functional measurements in real-world patients remains understudied. In this study, we develop a biventricular statistical shape model and use it to quantify the effect of biventricular anatomy on ECG-derived and demographic features, providing novel insights for the development of digital twins of cardiac electrophysiology. To this end, a dataset comprising high-resolution cardiac CT scans from 271 healthy individuals, including athletes, is utilized. Furthermore, a novel, universal, ventricular coordinate-based method is developed to establish lightweight shape correspondence. The performance of the shape model is rigorously established, focusing on its dimensionality reduction capabilities and the training data requirements. Additionally, a comprehensive synthetic cohort is made available, featuring ready-to-use biventricular meshes with fiber structures and anatomical region annotations. These meshes are well-suited for electrophysiological simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Mesh generation

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General 3D Room Layout from a Single View by Render-and-Compare 1

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16th European Conference on computer vision, ECCV 2020

作者： Stekovic, Sinisa Hampali, Shreyas Rad, Mahdi Sarkar, Sayan Deb 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

ISBN: (数字)9783030585174

ISBN: (纸本)9783030585167

We present a novel method to reconstruct the 3D layout of a room—walls, floors, ceilings—from a single perspective view in challenging conditions, by contrast with previous single-view methods restricted to cuboid-shaped layouts. This input view can consist of a color image only, but considering a depth map results in a more accurate reconstruction. Our approach is formalized as solving a constrained discrete optimization problem to find the set of 3D polygons that constitute the layout. In order to deal with occlusions between components of the layout, which is a problem ignored by previous works, we introduce an analysis-by-synthesis method to iteratively refine the 3D layout estimate. As no dataset was available to evaluate our method quantitatively, we created one together with several appropriate metrics. Our dataset consists of 293 images from ScanNet, which we annotated with precise 3D layouts. It offers three times more samples than the popular NYUv2 303 benchmark, and a much larger variety of layouts. © 2020, Springer Nature Switzerland AG.

关键词： Iterative methods

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Depth-aware object segmentation and grasp detection for robotic picking tasks

arXiv

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arXiv 2021年

作者： Ainetter, Stefan Böhm, Christoph Dhakate, Rohit Weiss, Stephan Fraundorfer, Friedrich Institute of Computer Graphics & Vision Graz University of Technology Graz Austria Institute of Smart System Technologies University of Klagenfurt Klagenfurt Austria

In this paper, we present a novel deep neural network architecture for joint classagnostic object segmentation and grasp detection for robotic picking tasks using a parallelplate gripper. We introduce depth-aware Coordinate Convolution (CoordConv), a method to increase accuracy for point proposal based object instance segmentation in complex scenes without adding any additional network parameters or computation complexity. Depth-aware CoordConv uses depth data to extract prior information about the location of an object to achieve highly accurate object instance segmentation. These resulting segmentation masks, combined with predicted grasp candidates, lead to a complete scene description for grasping using a parallel-plate gripper. We evaluate the accuracy of grasp detection and instance segmentation on challenging robotic picking datasets, namely Siléane and OCID-grasp, and show the benefit of joint grasp detection and segmentation on a real-world robotic picking task. Copyright © 2021, The Authors. All rights reserved.

关键词： Instance Segmentation

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In-Hand 3D Object Scanning from an RGB Sequence

arXiv

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arXiv 2022年

作者： Hampali, Shreyas Hodan, Tomas Tran, Luan Ma, Lingni Keskin, Cem Lepetit, Vincent Reality Labs Meta United States LIGM Ecole des Ponts Univ Gustave Eiffel CNRS Marne-la-Vallée France Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

We propose a method for in-hand 3D scanning of an unknown object with a monocular camera. Our method relies on a neural implicit surface representation that captures both the geometry and the appearance of the object, however, by contrast with most NeRF-based methods, we do not assume that the camera-object relative poses are known. Instead, we simultaneously optimize both the object shape and the pose trajectory. As direct optimization over all shape and pose parameters is prone to fail without coarse-level initialization, we propose an incremental approach that starts by splitting the sequence into carefully selected overlapping segments within which the optimization is likely to succeed. We reconstruct the object shape and track its poses independently within each segment, then merge all the segments before performing a global optimization. We show that our method is able to reconstruct the shape and color of both textured and challenging texture-less objects, outperforms classical methods that rely only on appearance features, and that its performance is close to recent methods that assume known camera poses. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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Coarse to fine vertebrae localization and segmentation with spatialconfiguration-Net and U-Net 15

Coarse to fine vertebrae localization and segmentation with ...

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15th International Joint Conference on computer vision, Imaging and computer graphics Theory and Applications, VISIGRAPP 2020

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

ISBN: (纸本)9789897584022

Localization and segmentation of vertebral bodies from spine CT volumes are crucial for pathological diagnosis, surgical planning, and postoperative assessment. However, fully automatic analysis of spine CT volumes is difficult due to the anatomical variation of pathologies, noise caused by screws and implants, and the large range of different field-of-views. We propose a fully automatic coarse to fine approach for vertebrae localization and segmentation based on fully convolutional CNNs. In a three-step approach, at first, a U-Net localizes the rough position of the spine. Then, the SpatialConfiguration-Net performs vertebrae localization and identification using heatmap regression. Finally, a U-Net performs binary segmentation of each identified vertebrae in a high resolution, before merging the individual predictions into the resulting multi-label vertebrae segmentation. The evaluation shows top performance of our approach, ranking first place and winning the MICCAI 2019 Large Scale Vertebrae Segmentation Challenge (VerSe 2019). Copyright © 2020 by SCITEPRESS – Science and technology Publications, Lda. All rights reserved

关键词： computerized tomography

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FAST3D: Flow-aware self-training for 3D object detectors

arXiv

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arXiv 2021年

作者： Fruhwirth-Reisinger, Christian Opitz, Michael Possegger, Horst Bischof, Horst Christian Doppler Laboratory for Embedded Machine Learning Institute of Computer Graphics and Vision Graz University of Technology

In the field of autonomous driving, self-training is widely applied to mitigate distribution shifts in LiDAR-based 3D object detectors. This eliminates the need for expensive, high-quality labels whenever the environment changes (e.g., geographic location, sensor setup, weather condition). State-of-the-art self-training approaches, however, mostly ignore the temporal nature of autonomous driving data. To address this issue, we propose a flow-aware self-training method which enables unsupervised domain adaptation for 3D object detectors on continuous LiDAR point clouds. In order to get reliable pseudo-labels, we leverage scene flow to propagate detections through time. In particular, we introduce a flow-based multi-target tracker, that exploits flow consistency to filter and refine resulting tracks. The emerged precise pseudo-labels then serve as a basis for model re-training. Starting with a pre-trained KITTI model, we conduct experiments on the challenging Waymo Open Dataset to demonstrate the effectiveness of our approach. Without any prior target domain knowledge, our results show a significant improvement over the state-of-the-art. © 2021, CC BY-NC-SA.

关键词： Object detection

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Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data

arXiv

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arXiv 2024年

作者： Heine, Lukas Hörst, Fabian Fragemann, Jana Luijten, Gijs Egger, Jan Bahnsen, Fin H. Sarfraz, M. Saquib Kleesiek, Jens Seibold, Constantin Institute for AI in Medicine University Hospital Essen Germany Mercedes-Benz Tech Innovation Germany Cancer Research Center Cologne Essen West German Cancer Center Essen University Hospital Essen Germany Institute of Computer Graphics and Vision Graz University of Technology Austria Department of Physics TU Dortmund University Germany

In industries such as healthcare, finance, and manufacturing, analysis of unstructured textual data presents significant challenges for analysis and decision making. Uncovering patterns within large-scale corpora and understanding their semantic impact is critical, but depends on domain experts or resource-intensive manual reviews. In response, we introduce Spacewalker in this system demonstration paper, an interactive tool designed to analyze, explore, and annotate data across multiple modalities. It allows users to extract data representations, visualize them in low-dimensional spaces and traverse large datasets either exploratory or by querying regions of interest. We evaluated Spacewalker through extensive experiments and annotation studies, assessing its efficacy in improving data integrity verification and annotation. We show that Spacewalker reduces time and effort compared to traditional methods. The code of this work is available here. © 2024, CC BY.

关键词： Data integrity

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An Efficient Elitist Covariance Matrix Adaptation for Continuous Local Search in High Dimension

An Efficient Elitist Covariance Matrix Adaptation for Contin...

引用

IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Li, Zhenhua Deng, Jingda Gao, Weifeng Zhang, Qingfu Liu, Hai-Lin Institute of Computer Graphics and Vision Graz University of Technology

ISBN: (纸本)9781728121536

In this paper, we propose a computationally efficient variant of elitist covariance matrix evolution strategy for continuous local search in high dimensional space. It focuses on searching in a low-dimensional subspace expanded by a small number of promising search directions. This leads to the linear internal computational complexity of each iteration, which enables the algorithm to scale to high dimensional problems. We conduct comprehensive experiments to evaluate the parameter sensitivity and the algorithm's performance. The experimental results validate that the proposed algorithm reduces the running time by a factor of ten, and it can be easily scaled up to n > 1000 on a set of commonly used test functions.

关键词： Covariance matrices Matrix decomposition Adaptation models Search problems Optimization Convergence Computational modeling

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CLASSIFICATION OF LUNG CANCER SUBTYPES ON CT IMAGES WITH SYNTHETIC PATHOLOGICAL PRIORS

arXiv

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arXiv 2023年

作者： Zhu, Wentao Jin, Yuan Ma, Gege Chen, Geng Egger, Jan Zhang, Shaoting Metaxas, Dimitris N. Research Center for Healthcare Data Science Zhejiang Lab Hangzhou311121 China School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi’an710072 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Shanghai Artificial Intelligence Laboratory Shanghai200120 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case’s CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the "gold standard" information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), and F1 score. © 2023, CC0.

关键词： Classification (of information)

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HO-3D v3: Improving the accuracy of hand-object annotations of the HO-3D dataset

arXiv

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arXiv 2021年

作者： Hampali, Shreyas Sarkar, Sayan Deb Lepetit, Vincent Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Université Paris-Est École des Ponts ParisTech Paris France

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