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arXiv 2023年

作者： Hackenberg, Maren Pfaffenlehner, Michelle Behrens, Max Pechmann, Astrid Kirschner, Janbernd Binder, Harald Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany Freiburg Center for Data Analysis and Modeling University of Freiburg Freiburg Germany Department of Neuropediatrics and Muscle Disorders Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany University of Freiburg Freiburg Germany

In a longitudinal clinical registry, different measurement instruments might have been used for assessing individuals at different time points. To combine them, we investigate deep learning techniques for obtaining a joint latent representation, to which the items of different measurement instruments are mapped. This corresponds to domain adaptation, an established concept in computer science for image data. Using the proposed approach as an example, we evaluate the potential of domain adaptation in a longitudinal cohort setting with a rather small number of time points, motivated by an application with different motor function measurement instruments in a registry of spinal muscular atrophy (SMA) patients. There, we model trajectories in the latent representation by ordinary differential equations (ODEs), where person-specific ODE parameters are inferred from baseline characteristics. The goodness of fit and complexity of the ODE solutions then allows to judge the measurement instrument mappings. We subsequently explore how alignment can be improved by incorporating corresponding penalty terms into model fitting. To systematically investigate the effect of differences between measurement instruments, we consider several scenarios based on modified SMA data, including scenarios where a mapping should be feasible in principle and scenarios where no perfect mapping is available. While misalignment increases in more complex scenarios, some structure is still recovered, even if the availability of measurement instruments depends on patient state. A reasonable mapping is feasible also in the more complex real SMA dataset. These results indicate that domain adaptation might be more generally useful in statistical modeling for longitudinal registry data. © 2023, CC BY.

关键词： Ordinary differential equations

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Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural network

arXiv

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arXiv 2023年

作者： Zhao, Wenzhao Wichtmann, Barbara D. Albert, Steffen Maurer, Angelika Zöllner, Frank G. Attenberger, Ulrike Hesser, Jürgen Interdisciplinary Center for Scientific Computing Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Germany Department of Diagnostic and Interventional Radiology University Hospital Bonn Germany Computer Assisted Clinical Medicine Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Germany Interdisciplinary Center for Scientific Computing Central Institute for Computer Engineering CSZ Heidelberg Center for Model-Based AI Data Analysis and Modeling in Medicine Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Germany

Group-equivariant convolutional neural networks (G-CNN) heavily rely on parameter sharing to increase CNN’s data efficiency and performance. However, the parameter-sharing strategy greatly increases the computational burden for each added parameter, which hampers its application to deep neural network models. In this paper, we address these problems by proposing a non-parameter-sharing approach for group equivariant neural networks. The proposed methods adaptively aggregate a diverse range of filters by a weighted sum of stochastically augmented decomposed filters. We give theoretical proof about how the continuous group convolution can be approximated by our methods. Our method applies to both continuous and discrete groups, where the augmentation is implemented using Monte Carlo sampling and bootstrap resampling, respectively. We demonstrate that our methods serve as an efficient extension of standard CNN. Experiments on group equivariance tests show how our methods can achieve superior performance to parameter-sharing group equivariant networks. Experiments on image classification and image denoising tasks show that in certain scenarios, with a suitable set of filter bases, our method helps improve the performance of standard CNNs and build efficient lightweight image denoising networks. The code will be available at https://***/ZhaoWenzhao/MCG_CNN. Copyright © 2023, The Authors. All rights reserved.

关键词： Image denoising

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EFFICIENT 3D AFFINELY EQUIVARIANT CNNS WITH ADAPTIVE FUSION OF AUGMENTED SPHERICAL FOURIER-BESSEL BASES

arXiv

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arXiv 2024年

作者： Zhao, Wenzhao Albert, Steffen Wichtmann, Barbara D. Maurer, Angelika Attenberger, Ulrike Zöllner, Frank G. Hesser, Jürgen School of Information Engineering Nanjing University of Finance and Economics Interdisciplinary Center for Scientific Computing Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Germany Computer Assisted Clinical Medicine Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Germany Clinic of Neuroradiology University Hospital Bonn Germany Department of Diagnostic and Interventional Radiology University Hospital Bonn Germany Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna Austria Interdisciplinary Center for Scientific Computing Central Institute for Computer Engineering CSZ Heidelberg Center for Model-Based AI Data Analysis and Modeling in Medicine Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Germany

Filter-decomposition-based group equivariant convolutional neural networks (CNNs) have shown promising stability and data efficiency for 3D image feature extraction. However, these networks, which rely on parameter sharing and discrete transformation groups, often underperform in modern deep neural network architectures for processing volumetric images, such as the common 3D medical images. To address these limitations, this paper presents an efficient non-parameter-sharing continuous 3D affine group equivariant neural network for volumetric images. This network uses an adaptive aggregation of Monte Carlo augmented spherical Fourier-Bessel filter bases to improve the efficiency and flexibility of 3D group equivariant CNNs for volumetric data. Unlike existing methods that focus only on angular orthogonality in filter bases, the introduced spherical Bessel Fourier filter base incorporates both angular and radial orthogonality to improve feature extraction. Experiments on four medical image segmentation datasets show that the proposed methods achieve better affine group equivariance and superior segmentation accuracy than existing 3D group equivariant convolutional neural network layers, significantly improving the training stability and data efficiency of conventional CNN layers (at 0.05 significance level). The code is available at https://***/ZhaoWenzhao/WMCSFB. Copyright © 2024, The Authors. All rights reserved.

关键词： Spheres

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Improving prediction models by incorporating external data with weights based on similarity

arXiv

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arXiv 2024年

作者： Behrens, Max Farhadizadeh, Maryam Rohde, Angelika Rühle, Alexander Nicolay, Nils H. Binder, Harald Zöller, Daniela Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Stefan-Meier-Strase 26 Freiburg79104 Germany Freiburg Center for Data Analysis and Modeling University of Freiburg Ernst-Zermelo-Strase 1 Freiburg79104 Germany University of Freiburg Department of Mathematical Stochastics Ernst-Zermelo-Strase 1 Freiburg79104 Germany Department of Radiation Oncology University of Freiburg-Medical Center Robert-Koch-Strase 3 Freiburg79106 Germany Department of Radiation Oncology University of Leipzig Stephanstr. 9a Leipzig04103 Germany

In clinical settings, we often face the challenge of building prediction models based on small observational data sets. For example, such a data set might be from a medical center in a multicenter study. Differences between centers might be large, thus requiring specific models based on the data set from the target center. Still, we want to borrow information from the external centers, to deal with small sample sizes. There are approaches that either assign weights to each external data set or each external observation. To incorporate information on differences between data sets and observations, we propose an approach that combines both into weights that can be incorporated into a likelihood for fitting regression models. Specifically, we suggest weights at the data set level that incorporate information on how well the models that provide the observation weights distinguish between data sets. Technically, this takes the form of inverse probability weighting. We explore different scenarios where covariates and outcomes differ among data sets, informing our simulation design for method evaluation. The concept of effective sample size is used for understanding the effectiveness of our subgroup modeling approach. We demonstrate our approach through a clinical application, predicting applied radiotherapy doses for cancer patients. Generally, the proposed approach provides improved prediction performance when external data sets are similar. We thus provide a method for quantifying similarity of external data sets to the target data set and use this similarity to include external observations for improving performance in a target data set prediction modeling task with small data. © 2024, CC BY.

关键词： Forecasting

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Instance Segmentation of Densely Packed Cells Using a Hybrid Model of U-Net and Mask R-CNN 19th

Instance Segmentation of Densely Packed Cells Using a Hybrid...

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19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020

作者： Konopczyński, Tomasz Heiman, Ron Woźnicki, Piotr Gniewek, Pawel Duvernoy, Marie-Cécilia Hallatschek, Oskar Hesser, Jürgen Department of Data Analysis and Modeling in Medicine Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Heidelberg Germany Departments of Physics and Integrative Biology University of California BerkeleyCA94720 United States Faculty of Medicine Medical University of Warsaw Warsaw Poland Tooploox Ltd. Wroclaw Poland

ISBN: (纸本)9783030614003

In malignant tumors and microbial infections, cells are commonly growing under confinement due to rapid proliferation in limited space. Nonetheless, this effect is poorly documented despite influencing drug efficiency. Studying budding yeast grown in space-limited micro-environments is a great tool to investigate this effect, conditioned on a robust cell instance segmentation. Due to confinement, cells become densely packed, impairing traditional segmentation methods. To tackle that problem, we show the performance of Mask-RCNN based methods on our dataset of budding yeast populations in a space-limited environment. We compare a number of methods, which include the pure Mask R-CNN, the 1st and 2nd place solution of the 2018 Kaggle data Science Bowl and a watershed ensemble variant of Mask R-CNN and U-Net. Additionally, we propose a Hybrid model that combines a semantic and an instance segmentation module in a sequential way. In the latter, the encoder-decoder architecture used for semantic segmentation produces a segmentation probability map, which is concatenated with the input image and then fed into the Mask R-CNN network in order to achieve the final instance segmentation result. Consequently, this model is able to efficiently share and reuse information at different levels between the two network modules. Our experiments demonstrate that the proposed model performs best and achieves a mean Average Precision (mAP) of 0.724 and a Dice coefficient of 0.9284 on our dataset. © 2020, Springer Nature Switzerland AG.

关键词： Cells

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Non-invasive Cardiac Output Monitoring in Congenital Heart Disease

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Current Treatment Options in Pediatrics 2023年第4期9卷 247-259页

作者： Tandon, Animesh Bhattacharya, Sarnab Morca, Ayse Inan, Omer T Munther, Daniel S Ryan, Shawn D Latifi, Samir Q Lu, Nanshu Lasa, Javier J Marino, Bradley S Baloglu, Orkun Department of Heart Vascular and Thoracic Division of Cardiology and Cardiovascular Medicine Children’s Institute Cleveland Clinic Children’s Cleveland OH United States Cleveland Clinic Children’s Center for Artificial Intelligence (C4AI) Department of Heart Vascular and Thoracic Children’s Institute Cleveland Clinic Children’s Cleveland OH United States Department of Pediatrics Cleveland Clinic Lerner College of Medicine of Case Western Reserve University Cleveland OH United States Department of Biomedical Engineering Cleveland Clinic Lerner Research Institute and Case School of Engineering at Case Western Reserve University Cleveland OH United States Department of Electrical and Computer Engineering The University of Texas at Austin Austin 78712 TX United States Department of Integrated Hospital Care Division of Critical Care Children’s Institute Cleveland Clinic Children’s Cleveland OH United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA United States Department of Mathematics and Statistics Cleveland State University Cleveland 44115 OH United States Center for Applied Data Analysis and Modeling Cleveland State University Cleveland 44115 OH United States Department of Aerospace Engineering and Engineering Mechanics Texas Materials Institute The University of Texas at Austin Austin 78712 TX United States Department of Biomedical Engineering The University of Texas at Austin Austin 78712 TX United States Department of Mechanical Engineering Texas Materials Institute The University of Texas at Austin Austin 78712 TX United States Divisions of Cardiology and Critical Care Department of Pediatrics Children’s Medical Center UT Southwestern Medical Center Dallas TX United States

Purpose of review: Cardiac output (CO) is a fundamental physiological parameter that measures the volume of blood that is pumped by the heart per unit of time, and helps define how oxygen is delivered to the tissues of the human body. In this paper, we discuss current methods of continuous CO monitoring while defining low CO syndrome (LCOS) and how analytical tools may help improve CO management in the subpopulation of patients with congenital heart disease (CHD). Recent findings: Non-invasive methods of measuring CO have become increasingly available in recent years. Advantages of non-invasive over invasive techniques include decreased risk of procedural complications, decreased exposure to sedative and/or anesthetic agents, and increased patient comfort. Pediatric patient populations are particularly sensitive to the risks and complications of invasive techniques given the relative size of current technologies to pediatric vascular and cardiac dimensions. Summary: Novel device technologies, combined with emerging analytical techniques, may help improve measurement of CO in children and those with CHD, and allow earlier detection of LCOS. © 2023, The Author(s).

关键词： Cardiac index Cardiac output Congenital heart disease Non-invasive continuous cardiac output monitoring

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The importance of monitoring wet age-related macular degeneration patients during coronavirus disease 2019 pandemic: A retrospective study of assessment of functional and structural outcomes

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ACTA OPHTHALMOLOGICA 2022年第Sup267期100卷

作者： Tsiropoulos, Georgios N. Vallee, Rodolphe Castro, Daniela Gallo Ambresin, Aude Swiss Visio Montchoisi Lausanne Switzerland RétinElysée Ophthalmology Center Lausanne Switzerland Department of Health Sciences Medical School Aristotle University of Thessaloniki (A.U.Th) Thessaloniki Greece Laboratory of Mathematics and Applications (LMA) CNRS 7348 Data Analysis and Computations Through Imaging Modeling Mathematics (DACTIM team) University of Poitiers Poitiers France Faculty of Biology and Medicine University of Lausanne (UNIL) Lausanne Switzerland

Purpose Intravitreal anti-Vascular Endothelial Growth Factor (anti-VEGF) injections are the gold standard treatment for wet Age-related Macular Degeneration (wet AMD). Coronavirus disease 2019 (COVID-19), and the resulting confinement, led to the cancellation of many programmed intravitreal anti-VEGF injection visits. We compared the functional and structural visual outcomes of patients that did not respect their intravitreal anti-VEGF injection intervals during the period of confinement due to COVID-19 (group 1) and those who respected them (group 2). Methods Wet AMD patients of Swiss Visio Montchoisi and RétinElysée were included. Best Corrected Visual Acuity (BCVA) and Optical Coherence Tomography (OCT) changes between the first post- and last pre-confinement visit were assessed, with favorable and unfavorable being the possible outcomes for each change. Results Group 1 (n = 89, 109 eyes) had 13.41% greater rate of unfavorable BCVA outcome and 38.27% greater rate of unfavorable OCT outcome than Group 2 (n = 96, 122 eyes) (p = 0.04, p < 0.0001, respectively). The multivariate analysis showed that the more the patients deviated from their programmed injections and the higher the BCVA pre-confinement, the higher the rate of unfavorable BCVA outcome (p = 0.03, p = 0.02, respectively). The OCT changes were not a predictive factor for an unfavorable BCVA outcome. The rates of pigment epithelial detachment (PED) and intraretinal fluid (IRF) increase in the first post-confinement visit were higher in group 1 than in group 2 (p = 0.0004, p = 0.0374, respectively), with a relative increase of unfavorable OCT outcome of 200% and 66.67%, respectively. Conclusions COVID-19 pandemic led many patients to refrain from their routine intravitreal anti-VEGF injection visits, allowing us to analyze the role of designated intervals in the treatment of patients with wet AMD. During a next confinement due to COVID-19, or circumstances similar to it, ophthalmologists should ensur

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The use of 3D-printed surgical guides and models for sinus lift surgery planning and education

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Journal of 3D Printing in medicine 2019年第3期3卷 145-155页

作者： Tuce, Robert-Angelo Arjoca, Stelian Neagu, Monica Neagu, Adrian Department of Functional Sciences Center for Modeling Biological Systems and Data Analysis Victor Babeş University of Medicine and Pharmacy Timişoara Romania Department of Physics and Astronomy University of Missouri Columbia 65211 MO United States

Aim: To develop 3D printing and dentistry methods for building physical models that enable one to simulate a sinus lift surgery. Materials & methods: The Blue Sky Plan software was used to create a digital model from the cone beam computed tomography scan of the subject's maxilla and to design a surgical guide for sinus augmentation and implant placement. The anatomical model and surgical guide were 3D printed from dental resin. Results: Sinus augmentation and dental implant insertion were carried out on the model as examples of practical skills training for residents in maxillofacial surgery. Conclusion: The 3D-printed models and surgical guides are useful training materials. They could be helpful also in a dental practice for surgical planning and for illustrating the procedure to the patient. © 2019 Future medicine Ltd.

关键词： 3D digital models biomodels dental education dental implant digital light processing lateral window osteotomy Schneiderian membrane sinus augmentation sinus floor elevation surgical templates

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Patient opinion on the monitoring of their wet age-related macular degeneration during coronavirus disease 2019 pandemic and on the importance of telemedicine

引用

ACTA OPHTHALMOLOGICA 2022年第S267期100卷

作者： Tsiropoulos, Georgios N. Vallee, Rodolphe Calci, Coraline Castro, Daniela Gallo Ambresin, Aude Swiss Visio Montchoisi Lausanne Switzerland RétinElysée Ophthalmology Center Lausanne Switzerland Department of Health Sciences Medical School Aristotle University of Thessaloniki (A.U.Th) Thessaloniki Greece Laboratory of Mathematics and Applications (LMA) CNRS 7348 Data Analysis and Computations Through Imaging Modeling Mathematics (DACTIM team) University of Poitiers Poitiers France Centre Hospitalier Universitaire Vaudois (CHUV) Yverdon-les-Bains Switzerland Faculty of Biology and Medicine University of Lausanne (UNIL) Lausanne Switzerland

Purpose To present the patients’ view on the monitoring of their wet Age-related Macular Degeneration (wet AMD) during Coronavirus disease 2019 (COVID-19), and the impact that telemedicine could have on it. Methods Wet AMD patients of Swiss Visio Montchoisi and RétinElysée (Lausanne, Switzerland), who had intravitreal anti-Vascular Endothelial Growth Factor (anti-VEGF) injections before and after the first period of the Swiss confinement due to COVID-19, completed two questionnaires. The first evaluated their view on their adherence to their scheduled injections during the confinement, and on the future use of telemedicine. The second, adapted from National Eye Institute Visual Function Questionnaire-25, evaluated their opinion on their visual function during the confinement. Results From a total of 130 patients, 8.5% responded they did not respect their assigned injection interval during the confinement (group 1), while 91.5% responded they did (group 2). The majority of group 1 (37.5%) responded they did not attend their scheduled visit to avoid using public transportation. The majority of group 2 (78.7%) considered the continuation of their treatment more important than the risk of contracting COVID-19. Before the lockdown, patients of group 2 had more need for help from others than the patients of group 1 (p = 0.02). In a future lockdown, 36.3% of group 1 and 8.7% of group 2 would prefer remote monitoring of their wet AMD via telemedicine (p = 0.02), 54.5% and 86.9% would prefer to visit the clinic (p = 0.02), while 9% and 4.3% would avoid the visit, respectively. 70% of group 1 and 33.6% of group 2 would rather use the telemedicine application in their home than visit a telemedicine center (p = 0.04). Conclusions Despite our availability to provide continuous intravitreal anti-VEGF injections during the COVID-19 confinement, many wet AMD patients did not receive their scheduled treatment. In similar future circumstances, most patients would prefer to visit the

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Practical resources for enhancing the reproducibility of mechanistic modeling in systems biology

arXiv

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arXiv 2021年

作者： Blinov, Michael L. Gennari, John H. Karr, Jonathan R. Moraru, Ion I. Nickerson, David P. Sauro, Herbert M. Center for Cell Analysis and Modeling UConn Health FarmingtonCT United States Department of Biomedical Informatics and Medical Education University of Washington SeattleWA United States Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New YorkNY United States Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY United States Auckland Bioengineering Institute University of Auckland Auckland New Zealand Department of Bioengineering University of Washington SeattleWA United States

Although reproducibility is a core tenet of the scientific method, it remains challenging to reproduce many results. Surprisingly, this also holds true for computational results in domains such as systems biology where there have been extensive standardization efforts. For example, Tiwari et al. recently found that they could only repeat 50% of published simulation results in systems biology. Toward improving the reproducibility of computational systems research, we identified several resources that investigators can leverage to make their research more accessible, executable, and comprehensible by others. In particular, we identified several domain standards and curation services, as well as powerful approaches pioneered by the software engineering industry that we believe many investigators could adopt. Together, we believe these approaches could substantially enhance the reproducibility of systems biology research. In turn, we believe enhanced reproducibility would accelerate the development of more sophisticated models that could inform precision medicine and synthetic biology. © 2021, CC BY.

关键词： Reusability

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