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Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations

arXiv

引用

arXiv 2021年

作者： Pepe, Antonio Egger, Jan Codari, Marina Willemink, Martin J. Gsaxner, Christina Li, Jianning Roth, Peter M. Mistelbauer, Gabriel Schmalstieg, Dieter Fleischmann, Dominik Graz University of Technology Institute of Computer Graphics and Vision Inffeldgasse 16/II Graz8010 Austria Stanford University School of Medicine 3D and Quantitative Imaging Lab 300 Pasteur Drive StanfordCA94305 United States Laboratory Graz Austria Girardetstraße 2 Essen45131 Germany Otto-von-Guericke University Department of Simulation and Graphics. Universitätsplatz 2 Magdeburg39106 Germany

Objective: Surveillance imaging of chronic aortic diseases, such as dissections, relies on obtaining and comparing cross-sectional diameter measurements at predefined aortic landmarks, over time. Due to a lack of robust tools, the orientation of the cross-sectional planes is defined manually by highly trained operators. We show how manual annotations routinely collected in a clinic can be efficiently used to ease this task, despite the presence of a non-negligible interoperator variability in the measurements. Impact: Ill-posed but repetitive imaging tasks can be eased or automated by leveraging imperfect, retrospective clinical annotations. Methodology: In this work, we combine convolutional neural networks and uncertainty quantification methods to predict the orientation of such cross-sectional planes. We use clinical data randomly processed by 11 operators for training, and test on a smaller set processed by 3 independent operators to assess interoperator variability. Results: Our analysis shows that manual selection of cross-sectional planes is characterized by 95% limits of agreement (LOA) of 10.6◦ and 21.4◦ per angle. Our method showed to decrease static error by 3.57◦ (40.2%) and 4.11◦ (32.8%) against state of the art and LOA by 5.4◦ (49.0%) and 16.0◦ (74.6%) against manual processing. Conclusion: This suggests that pre-existing annotations can be an inexpensive resource in clinics to ease ill-posed and repetitive tasks like cross-section extraction for surveillance of aortic dissections. Copyright © 2021, The Authors. All rights reserved.

关键词： Blood vessels

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Sparse inpainting with smoothed particle hydrodynamics

arXiv

引用

arXiv 2020年

作者： Daropoulos, Viktor Augustin, Matthias Weickert, Joachim Interactive Graphics and Simulation Group Department of Computer Science Universität Innsbruck Technikerstraße 21a Innsbruck6020 Austria Mathematical Image Analysis Group Faculty of Mathematics and Computer Science Saarland University Campus E1.7 Saarbrücken66041 Germany

Digital image inpainting refers to techniques used to reconstruct a damaged or incomplete image by exploiting available image information. The main goal of this work is to perform the image inpainting process from a set of sparsely distributed image samples with the Smoothed Particle Hydrodynamics (SPH) technique. As, in its naive formulation, the SPH technique is not even capable of reproducing constant functions, we modify the approach to obtain an approximation which can reproduce constant and linear functions. Furthermore, we examine the use of Voronoi tessellation for defining the necessary parameters in the SPH method as well as selecting optimally located image samples. In addition to this spatial optimization, optimization of data values is also implemented in order to further improve the results. Apart from a traditional Gaussian smoothing kernel, we assess the performance of other kernels on both random and spatially optimized masks. Since the use of isotropic smoothing kernels is not optimal in the presence of objects with a clear preferred orientation in the image, we also examine anisotropic smoothing kernels. Our final algorithm can compete with well-performing sparse inpainting techniques based on homogeneous or anisotropic diffusion processes as well as with exemplar-based *** Codes 68U10 (Primary) 65D18, 76M28, 94A08 (Secondary) Copyright © 2020, The Authors. All rights reserved.

关键词： Hydrodynamics

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Cross-Domain conditional generative adversarial networks for stereoscopic hyperrealism in surgical training

arXiv

引用

arXiv 2019年

作者： Engelhardt, Sandy Sharan, Lalith Karck, Matthias Simone, Raaele De Wolf, Ivo Faculty of Computer Science Mannheim University of Applied Sciences Department of Cardiac Surgery Heidelberg University Hospital Dep. of Simulation and Graphics Magdeburg University

Phantoms for surgical training are able to mimic cutting and suturing properties and patient-individual shape of organs, but lack a realistic visual appearance that captures the heterogeneity of surgical scenes. In order to overcome this in endoscopic approaches, hyperreal- istic concepts have been proposed to be used in an augmented reality- setting, which are based on deep image-to-image transformation meth- ods. Such concepts are able to generate realistic representations of phan- toms learned from real intraoperative endoscopic sequences. Conditioned on frames from the surgical training process, the learned models are able to generate impressive results by transforming unrealistic parts of the image (e.g. the uniform phantom texture is replaced by the more hetero- geneous texture of the tissue). Image-to-image synthesis usually learns a mapping G : X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y . However, it does not necessarily force the generated images to be consistent and without ar- tifacts. In the endoscopic image domain this can affect depth cues and stereo consistency of a stereo image pair, which ultimately impairs surgi- cal vision. We propose a cross-domain conditional generative adversarial network approach (GAN) that aims to generate more consistent stereo pairs. The results show substantial improvements in depth perception and realism evaluated by 3 domain experts and 3 medical students on a 3D monitor over the baseline method. In 84 of 90 instances our proposed method was preferred or rated equal to the baseline. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Elastic mitral valve silicone replica made from 3D-printable molds offer advanced surgical training

Elastic mitral valve silicone replica made from 3D-printable...

引用

Workshop on Bildverarbeitung fur die Medizin, 2018

作者： Engelhardt, Sandy Sauerzapf, Simon Al-Maisary, Sameer Karck, Matthias Preim, Bernhard Wolf, Ivo De Simone, Raffaele Faculty for Computer Science Mannheim University of Applied Sciences Mannheim Germany Department of Simulation and Graphics Magdeburg University Magdeburg Germany Departement of Cardiac Surgery Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783662580950

Reconstructive mitral valve surgeries are demanding cardiac surgeries that are conducted in the mid-age of a surgical career at the earliest. Receiving years of training in patients is required to gain the experiences and skills necessary for surgical success. On top of that, the number of such surgeries is limited per hospital, therefore other means of training should be offered to facilitate a steep learning curve. Within the scope of this work, we equipped an existing physical simulator with patient-specific flexible replica of the mitral valve. We developed software to automatically produce a 3D-printable casting mold for silicone material, as this mimics properties of the valve tissue in terms of stitching and cutting. We show the feasibility of the approach and the usefulness of these models is evaluated by an experienced cardiac surgeon, successfully conducting reconstructive surgery on pathological silicone valves. © Springer-Verlag GmbH Deutschland 2018.

关键词： Silicones

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3D-print, virtual reality or regular screen? comparison of presentation modalities for 3D liver reconstructions

引用

HPB 2021年 23卷 S781-S781页

作者： Huettl, F. Saalfeld, P. Hansen, C. Preim, B. Poplawski, A. Kneist, W. Lang, H. Huber, T. University Medical Center of the Johannes Gutenberg-University Mainz Department of General Visceral and Transplant Surgery Germany Otto-von-Guericke University Magdeburg Institute of Simulation and Graphics Faculty of Computer Science Germany University Medical Center of the Johannes Gutenberg-University Mainz Institute of Medical Biostatistics Epidemiology and Informatics (IMBEI) Germany

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Deep learning of preconditioners for conjugate gradient solvers in urban water related problems

arXiv

引用

arXiv 2019年

作者： Sappl, Johannes Seiler, Laurent Harders, Matthias Rauch, Wolfgang Unit of Environmental Engineering Faculty of Engineering Sciences Universität Innsbruck Technikerstraße 13 Innsbruck6020 Austria Interactive Graphics and Simulation Group Department of Computer Science Universität Innsbruck Technikerstraße 21 A Innsbruck6020 Austria

Solving systems of linear equations is a problem occuring frequently in water engineering applications. Usually the size of the problem is too large to be solved via direct factorization. One can resort to iterative approaches, in particular the conjugate gradients method if the matrix is symmetric positive definite. Preconditioners further enhance the rate of convergence but hitherto only handcrafted ones requiring expert knowledge have been used. We propose an innovative approach employing Machine Learning, in particular a Convolutional Neural Network, to unassistedly design preconditioning matrices specifically for the problem at hand. Based on an in-depth case study in fluid simulation we are able to show that our learned preconditioner is able to improve the convergence rate even beyond well established methods like incomplete Cholesky factorization or Algebraic MultiGrid. Copyright © 2019, The Authors. All rights reserved.

关键词： Conjugate gradient method

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Variational networks for joint image reconstruction and classification of tumor immune cell interactions in melanoma tissue sections

Variational networks for joint image reconstruction and clas...

引用

Workshop on Bildverarbeitung fur die Medizin, 2018

作者： Effland, Alexander Hölzel, Michael Klatzer, Teresa Kobler, Erich Landsberg, Jennifer Neuhäuser, Leonie Pock, Thomas Rumpf, Martin Institute for Numerical Simulation University of Bonn Bonn Germany Institute of Clinical Chemistry and Clinical Pharmacology University of Bonn Bonn Germany Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Department of Dermatology and Allergy University of Bonn Bonn Germany

ISBN: (纸本)9783662580950

Immunotherapy is currently revolutionizing the treatment of cancer. Detailed analyses of tumor immune cell interaction in the tumor microenvironment will facilitate an accurate prediction of a patient’s clinical response. The automatic and reliable pre-screening of histological tissue sections for tumor infiltrating immune cells (TILs) will support the development of TIL-based predictive biomarkers for checkpoint immunotherapy. In this paper, a learning approach for image classification is presented, which allows various pattern inquires for different types of tissue section images. The underlying trainable reaction diffusion model combines classification and denoising. The model is trained using a stochastic generation of training data. The effectiveness of this approach is demonstrated for immunofluorescent and for Hematoxylin and Eosin (H&E) stained melanoma section images. A particular focus is on the classification of TILs in the proximity to melanoma cells in an experimental melanoma mouse model and in human melanoma. This new learning approach for images of melanoma tissue sections will refine the strategy for the practical clinical application of biomarker research. © Springer-Verlag GmbH Deutschland 2018.

关键词： Tumors

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On interval methods with zero rewriting and exact geometric computation 7th

On interval methods with zero rewriting and exact geometric ...

引用

7th International Conference on Mathematical Aspects of computer and Information Sciences, MACIS 2017

作者： Schirra, Stefan Wilhelm, Martin Department of Simulation and Graphics Faculty of Computer Science University of Magdeburg Universitätsplatz 2 Magdeburg39106 Germany

ISBN: (纸本)9783319724522

We oppose interval-symbol methods with zero rewriting developed by Shirayanagi and Sekigawa [14, 31–33] to the exact geometric computation paradigm [17, 37], especially to exact decisions computation via lazy adaptive evaluation with expression-dags, in doing so carving out analogies and disparities. © Springer International Publishing AG 2017.

关键词： Numerical methods

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Improving surgical training phantoms by hyperrealism: Deep unpaired image-to-image translation from real surgeries

arXiv

引用

arXiv 2018年

作者： Engelhardt, Sandy De Simone, Raffaele Full, Peter M. Karck, Matthias Wolf, Ivo Dep. of Simulation and Graphics Magdeburg University Germany Department of Cardiac Surgery Heidelberg University Hospital Germany Faculty of Computer Science Mannheim University of Applied Sciences Germany

Current 'dry lab' surgical phantom simulators are a valuable tool for surgeons which allows them to improve their dexterity and skill with surgical instruments. These phantoms mimic the haptic and shape of organs of interest, but lack a realistic visual appearance. In this work, we present an innovative application in which representations learned from real intraoperative endoscopic sequences are transferred to a surgical phantom scenario. The term hyperrealism is introduced in this field, which we regard as a novel subform of surgical augmented reality for approaches that involve real-time object transfigurations. For related tasks in the computer vision community, unpaired cycle-consistent Generative Adversarial Networks (GANs) have shown excellent results on still RGB images. Though, application of this approach to continuous video frames can result in ickering, which turned out to be especially prominent for this application. Therefore, we propose an extension of cycle-consistent GANs, named tempCycleGAN, to improve temporal consistency. The novel method is evaluated on captures of a silicone phantom for training endoscopic reconstructive mitral valve procedures. Synthesized videos show highly realistic results with regard to 1) replacement of the silicone appearance of the phantom valve by intraoperative tissue texture, while 2) explicitly keeping crucial features in the scene, such as instruments, sutures and prostheses. Compared to the original CycleGAN approach, tempCycleGAN efficiently removes ickering between frames. The overall approach is expected to change the future design of surgical training simulators since the generated sequences clearly demonstrate the feasibility to enable a considerably more realistic training experience for minimally-invasive procedures. Copyright © 2018, The Authors. All rights reserved.

关键词： Simulators

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Virtual and Augmented Reality Systems for Renal Interventions: A Systematic Review

引用

IEEE Reviews in Biomedical Engineering 2017年 10卷 78-94页

作者： Detmer, Felicitas J. Hettig, Julian Schindele, Daniel Schostak, Martin Hansen, Christian Institute for Simulation and Graphics Department of Computer Science Otto-von-Guericke University Magdeburg39106 Germany Department of Bioengineering George Mason University FairfaxVA22030 United States Clinic of Urology and Pediatric Urology University Hospital of Magdeburg Magdeburg39120 Germany

Purpose: Many virtual and augmented reality systems have been proposed to support renal interventions. This paper reviews such systems employed in the treatment of renal cell carcinoma and renal stones. Methods: A systematic literature search was performed. Inclusion criteria were virtual and augmented reality systems for radical or partial nephrectomy and renal stone treatment, excluding systems solely developed or evaluated for training purposes. Results: In total, 52 research papers were identified and analyzed. Most of the identified literature (87%) deals with systems for renal cell carcinoma treatment. About 44% of the systems have already been employed in clinical practice, but only 20% in studies with ten or more patients. Main challenges remaining for future research include the consideration of organ movement and deformation, human factor issues, and the conduction of large clinical studies. Conclusion: Augmented and virtual reality systems have the potential to improve safety and outcomes of renal interventions. In the last ten years, many technical advances have led to more sophisticated systems, which are already applied in clinical practice. Further research is required to cope with current limitations of virtual and augmented reality assistance in clinical environments. © 2017 IEEE.

关键词： Augmented reality

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