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A SEMI-AUTOMATIC CRANIAL IMPLANT DESIGN TOOL BASED ON RIGID ICP TEMPLATE ALIGNMENT AND VOXEL SPACE RECONSTRUCTION

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arXiv 2024年

作者： Lackner, Michael Puladi, Behrus Kleesiek, Jens Egger, Jan Li, Jianning Germany Institute of Computer Graphics and Vision Graz University of Technology Austria Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Germany Institute of Medical Informatics University Hospital RWTH Aachen Germany

In traumatic medical emergencies, the patients heavily depend on cranioplasty - the craft of neurocranial repair using cranial implants. Despite the improvements made in recent years, the design of a patient-specific implant (PSI) is among the most complex, expensive, and least automated tasks in cranioplasty. Further research in this area is needed. Therefore, we created a prototype application with a graphical user interface (UI) specifically tailored for semi-automatic implant generation, where the users only need to perform high-level actions. A general outline of the proposed implant generation process involves setting an area of interest, aligning the templates, and then creating the implant in voxel space. Furthermore, we show that the alignment can be improved significantly, by only considering clipped geometry in the vicinity of the defect border. The software prototype will be open-sourced at https://***/3Descape/Cranial_Implant_Design. © 2024, CC BY.

关键词： Graphical user interfaces

Product of Gaussian Mixture Diffusion Models

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arXiv 2023年

作者： Zach, Martin Kobler, Erich Chambolle, Antonin Pock, Thomas Institute of Computer Graphics and Vision Graz University of Technology Austria Klinik für Neuroradiologie Universitätsklinikum Bonn Germany and Mokaplan INRIA Paris France

In this work we tackle the problem of estimating the density fX of a random variable X by successive smoothing, such that the smoothed random variable Y fulfills the diffusion partial differential equation (∂t − ∆1)fY (·, t) = 0 with initial condition fY (·, 0) = fX. We propose a product-of-experts-type model utilizing Gaussian mixture experts and study configurations that admit an analytic expression for fY (·, t). In particular, with a focus on image processing, we derive conditions for models acting on filter-, wavelet-, and shearlet-responses. Our construction naturally allows the model to be trained simultaneously over the entire diffusion horizon using empirical Bayes. We show numerical results for image denoising where our models are competitive while being tractable, interpretable, and having only a small number of learnable parameters. As a byproduct, our models can be used for reliable noise level estimation, allowing blind denoising of images corrupted by heteroscedastic noise. © 2023, CC BY.

关键词： Random variables

PIEMAP: Personalized Inverse Eikonal Model from Cardiac Electro-Anatomical Maps 11th

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PIEMAP: Personalized Inverse Eikonal Model from Cardiac Elec...

11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020

作者： Grandits, Thomas Pezzuto, Simone Lubrecht, Jolijn M. Pock, Thomas Plank, Gernot Krause, Rolf Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Center for Computational Medicine in Cardiology Institute of Computational Science Università della Svizzera italiana Lugano Switzerland Institute of Biophysics Medical University of Graz Graz Austria BioTechMed-Graz Graz Austria

ISBN: (纸本)9783030681067

Electroanatomical mapping, a keystone diagnostic tool in cardiac electrophysiology studies, can provide high-density maps of the local electric properties of the tissue. It is therefore tempting to use such data to better individualize current patient-specific models of the heart through a data assimilation procedure and to extract potentially insightful information such as conduction properties. Parameter identification for state-of-the-art cardiac models is however a challenging task. In this work, we introduce a novel inverse problem for inferring the anisotropic structure of the conductivity tensor, that is fiber orientation and conduction velocity along and across fibers, of an eikonal model for cardiac activation. The proposed method, named PIEMAP, performed robustly with synthetic data and showed promising results with clinical data. These results suggest that PIEMAP could be a useful supplement in future clinical workflowss of personalized therapies. © 2021, Springer Nature Switzerland AG.

关键词： Heart

Sparse Message Passing Network with Feature Integration for Online Multiple Object Tracking

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arXiv 2022年

作者： Wang, Bisheng Possegger, Horst Bischof, Horst Cao, Guo School of Computer Science and Engerring Nanjing University of Science and Technology China Institute of Computer Graphics and Vision Graz University of Technology Austria

Existing Multiple Object Tracking (MOT) methods design complex architectures for better tracking performance. However, without a proper organization of input information, they still fail to perform tracking robustly and suffer from frequent identity switches. In this paper, we propose two novel methods together with a simple online Message Passing Network (MPN) to address these limitations. First, we explore different integration methods for the graph node and edge embeddings and put forward a new IoU (Intersection over Union) guided function, which improves long term tracking and handles identity switches. Second, we introduce a hierarchical sampling strategy to construct sparser graphs which allows to focus the training on more difficult samples. Experimental results demonstrate that a simple online MPN with these two contributions can perform better than many state-of-the-art methods. In addition, our association method generalizes well and can also improve the results of private detection based methods. © 2022, CC BY-NC-SA.

关键词： Message passing

Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy 8th

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Total Deep Variation for Noisy Exit Wave Reconstruction in T...

8th International Conference on Scale Space and Variational Methods in computer vision, SSVM 2021

作者： Pinetz, Thomas Kobler, Erich Doberstein, Christian Berkels, Benjamin Effland, Alexander Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Austrian Institute of Technology Vienna Austria AICES Graduate School RWTH Aachen University Aachen Germany Graz Austria Institute for Applied Mathematics University of Bonn Bonn Germany

ISBN: (纸本)9783030755485

Transmission electron microscopes (TEMs) are ubiquitous devices for high-resolution imaging on an atomic level. A key problem related to TEMs is the reconstruction of the exit wave, which is the electron signal at the exit plane of the examined specimen. Frequently, this reconstruction is cast as an ill-posed nonlinear inverse problem. In this work, we integrate the data-driven total deep variation regularizer to reconstruct the exit wave in this inverse problem. In several numerical experiments, the applicability of the proposed method is demonstrated for different materials. © 2021, Springer Nature Switzerland AG.

关键词： Numerical methods

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

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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

Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions

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Sit Back and Relax: Learning to Drive Incrementally in All W...

IEEE Symposium on Intelligent Vehicle

作者： Stefan Leitner M. Jehanzeb Mirza Wei Lin Jakub Micorek Marc Masana Mateusz Kozinski Horst Possegger Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Christian Doppler Laboratory for Semantic 3D Computer Vision Silicon Austria Labs TU Graz - SAL Dependable Embedded Systems Lab

In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they are often unable to perform well in all of them and suffer from catastrophic forgetting. To efficiently mitigate forgetting, we propose Domain-Incremental Learning through Activation Matching (DILAM), which employs unsupervised feature alignment to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i.e. test time) when the respective weather conditions are encountered. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector. Furthermore, contrary to previous domain-incremental learning approaches, we do not require the weather label when testing and propose to automatically infer the weather condition by a majority voting linear classifier.

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Enforcing connectivity of 3D linear structures using their 2D projections

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arXiv 2022年

作者： Oner, Doruk Osman, Hussein Kozinski, Mateusz Fua, Pascal Computer Vision Laboratory École Polytechnique Fédérale de Lausanne Switzerland Institute of Computer Graphics and Vision Graz University of Technology Austria

Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes. This is typically done using neural networks trained by minimizing voxel-wise loss functions that do not capture the topological properties of these structures. As a result, the connectivity of the recovered structures is often wrong, which lessens their usefulness. In this paper, we propose to improve the 3D connectivity of our results by minimizing a sum of topology-aware losses on their 2D projections. This suffices to increase the accuracy and to reduce the annotation effort required to provide the required annotated training data. Code is available at https://***/doruk-oner/ConnectivityOnProjections. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

In-Hand 3D Object Scanning from an RGB Sequence

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In-Hand 3D Object Scanning from an RGB Sequence

Conference on computer vision and Pattern Recognition (CVPR)

作者： Shreyas Hampali Tomas Hodan Luan Tran Lingni Ma Cem Keskin Vincent Lepetit Reality Labs at Meta 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.

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