Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results

Soft computing and non-parametric techniques for effective video surveillance systems

Esta tesis propone varios objetivos interconectados para el diseño de un sistema de vídeovigilancia cuyo funcionamiento es pensado para un amplio rango de condiciones. Primeramente se propone una métrica de evaluación del detector y sistema de seguimiento basada en una mínima referencia. Dicha técnica es una respuesta a la demanda de ajuste de forma rápida y fácil del sistema adecuándose a distintos entornos. También se propone una técnica de optimización basada en Estrategias Evolutivas y la combinación de funciones de idoneidad en varios pasos. El objetivo es obtener los parámetros de ajuste del detector y el sistema de seguimiento adecuados para el mejor funcionamiento en una amplia gama de situaciones posibles Finalmente, se propone la construcción de un clasificador basado en técnicas no paramétricas que pudieran modelar la distribución de datos de entrada independientemente de la fuente de generación de dichos datos. Se escogen actividades detectables a corto plazo que siguen un patrón de tiempo que puede ser fácilmente modelado mediante HMMs. La propuesta consiste en una modificación del algoritmo de Baum-Welch con el fin de modelar las probabilidades de emisión del HMM mediante una técnica no paramétrica basada en estimación de densidad con kernels (KDE). _____________________________________This thesis proposes several interconnected objectives for the design of a video-monitoring system whose operation is thought for a wide rank of conditions. Firstly an evaluation technique of the detector and tracking system is proposed and it is based on a minimum reference or ground-truth. This technique is an answer to the demand of fast and easy adjustment of the system adapting itself to different contexts. Also, this thesis proposes a technique of optimization based on Evolutionary Strategies and the combination of fitness functions. The objective is to obtain the parameters of adjustment of the detector and tracking system for the best operation in an ample range of possible situations. Finally, it is proposed the generation of a classifier in which a non-parametric statistic technique models the distribution of data regardless the source generation of such data. Short term detectable activities are chosen that follow a time pattern that can easily be modeled by Hidden Markov Models (HMMs). The proposal consists in a modification of the Baum-Welch algorithm with the purpose of modeling the emission probabilities of the HMM by means of a nonparametric technique based on the density estimation with kernels (KDE)

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

From Manual to Automated Design of Biomedical Semantic Segmentation Methods

Digital imaging plays an increasingly important role in clinical practice. With the number of images that are routinely acquired on the rise, the number of experts devoted to analyzing them is by far not increasing as rapidly. This alarming disparity calls for automated image analysis methods to ease the burden on the experts and prevent a degradation of the quality of care. Semantic segmentation plays a central role in extracting clinically relevant information from images, either all by themselves or as part of more elaborate pipelines, and constitutes one of the most active fields of research in medical image analysis. Thereby, the diversity of datasets is mirrored by an equally diverse number of segmentation methods, each being optimized for the datasets they are addressing. The resulting diversity of methods does not come without downsides: The specialized nature of these segmentation methods causes a dataset dependency which makes them unable to be transferred to other segmentation problems. Not only does this result in issues with out-of-the-box applicability, but it also adversely affects future method development: Improvements over baselines that are demonstrated on one dataset rarely transfer to another, testifying a lack of reproducibility and causing a frustrating literature landscape in which it is difficult to discern veritable and long lasting methodological advances from noise. We study three different segmentation tasks in depth with the goal of understanding what makes a good segmentation model and which of the recently proposed methods are truly required to obtain competitive segmentation performance. To this end, we design state of the art segmentation models for brain tumor segmentation, cardiac substructure segmentation and kidney and kidney tumor segmentation. Each of our methods is evaluated in the context of international competitions, ensuring objective performance comparison with other methods. We obtained the third place in BraTS 2017, the second place in BraTS 2018, the first place in ACDC and the first place in the highly competitive KiTS challenge. Our analysis of the four segmentation methods reveals that competitive segmentation performance for all of these tasks can be achieved with a standard, but well-tuned U-Net architecture, which is surprising given the recent focus in the literature on finding better network architectures. Furthermore, we identify certain similarities between our segmentation pipelines and notice that their dissimilarities merely reflect well-structured adaptations in response to certain dataset properties. This leads to the hypothesis that we can identify a direct relation between the properties of a dataset and the design choices that lead to a good segmentation model for it. Based on this hypothesis we develop nnU-Net, the first method that breaks the dataset dependency of traditional segmentation methods. Traditional segmentation methods must be developed by experts, going through an iterative trial-and-error process until they have identified a good segmentation pipeline for a given dataset. This process ultimately results in a fixed pipeline configuration which may be incompatible with other datasets, requiring extensive re-optimization. In contrast, nnU-Net makes use of a generalizing method template that is dynamically and automatically adapted to each dataset it is applied to. This is achieved by condensing domain knowledge about the design of segmentation methods into inductive biases. Specifically, we identify certain pipeline hyperparameters that do not need to be adapted and for which a good default value can be set for all datasets (called blueprint parameters). They are complemented with a comprehensible set of heuristic rules, which explicitly encode how the segmentation pipeline and the network architecture that is used along with it must be adapted for each dataset (inferred parameters). Finally, a limited number of design choices is determined through empirical evaluation (empirical parameters). Following the analysis of our previously designed specialized pipelines, the basic network architecture type used is the standard U-Net, coining the name of our method: nnU-Net (”No New Net”). We apply nnU-Net to 19 diverse datasets originating from segmentation competitions in the biomedical domain. Despite being applied without manual intervention, nnU-Net sets a new state of the art in 29 out of the 49 different segmentation tasks encountered in these datasets. This is remarkable considering that nnU-Net competed against specialized manually tuned algorithms on each of them. nnU-Net is the first out-of-the-box tool that makes state of the art semantic segmentation methods accessible to non-experts. As a framework, it catalyzes future method development: new design concepts can be implemented into nnU-Net and leverage its dynamic nature to be evaluated across a wide variety of datasets without the need for manual re-tuning. In conclusion, the thesis presented here exposed critical weaknesses in the current way of segmentation method development. The dataset dependency of segmentation methods impedes scientific progress by confining researchers to a subset of datasets available in the domain, causing noisy evaluation and in turn a literature landscape in which results are difficult to reproduce and true methodological advances are difficult to discern. Additionally, non-experts were barred access to state of the art segmentation for their custom datasets because method development is a time consuming trial-and-error process that needs expertise to be done correctly. We propose to address this situation with nnU-Net, a segmentation method that automatically and dynamically adapts itself to arbitrary datasets, not only making out-of-the-box segmentation available for everyone but also enabling more robust decision making in the development of segmentation methods by enabling easy and convenient evaluation across multiple datasets

Social and Affective Neuroscience of Everyday Human Interaction

This Open Access book presents the current state of the art knowledge on social and affective neuroscience based on empirical findings. This volume is divided into several sections first guiding the reader through important theoretical topics within affective neuroscience, social neuroscience and moral emotions, and clinical neuroscience. Each chapter addresses everyday social interactions and various aspects of social interactions from a different angle taking the reader on a diverse journey. The last section of the book is of methodological nature. Basic information is presented for the reader to learn about common methodologies used in neuroscience alongside advanced input to deepen the understanding and usability of these methods in social and affective neuroscience for more experienced readers

Higher level techniques for the artistic rendering of images and video

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Content-aware approach for improving biomedical image analysis: an interdisciplinary study series

Biomedicine is a highly interdisciplinary research area at the interface of sciences, anatomy, physiology, and medicine. In the last decade, biomedical studies have been greatly enhanced by the introduction of new technologies and techniques for automated quantitative imaging, thus considerably advancing the possibility to investigate biological phenomena through image analysis. However, the effectiveness of this interdisciplinary approach is bounded by the limited knowledge that a biologist and a computer scientist, by professional training, have of each other’s fields. The possible solution to make up for both these lacks lies in training biologists to make them interdisciplinary researchers able to develop dedicated image processing and analysis tools by exploiting a content-aware approach. The aim of this Thesis is to show the effectiveness of a content-aware approach to automated quantitative imaging, by its application to different biomedical studies, with the secondary desirable purpose of motivating researchers to invest in interdisciplinarity. Such content-aware approach has been applied firstly to the phenomization of tumour cell response to stress by confocal fluorescent imaging, and secondly, to the texture analysis of trabecular bone microarchitecture in micro-CT scans. Third, this approach served the characterization of new 3-D multicellular spheroids of human stem cells, and the investigation of the role of the Nogo-A protein in tooth innervation. Finally, the content-aware approach also prompted to the development of two novel methods for local image analysis and colocalization quantification. In conclusion, the content-aware approach has proved its benefit through building new approaches that have improved the quality of image analysis, strengthening the statistical significance to allow unveiling biological phenomena. Hopefully, this Thesis will contribute to inspire researchers to striving hard for pursuing interdisciplinarity