Image segmentation plays an important role in medical image analysis. Many segmentation algorithms exist. Most of them produce data which are more or less not suitable for further surface extraction and anatomical modeling of human tissues. In this thesis, a novel segmentation technique based on the 3D Delaunay triangulation is proposed. A modified variational tetrahedral meshing approach is used to adapt a tetrahedral mesh to the underlying CT volumetric data, so that image edges are well approximated in the mesh. In order to classify tetrahedra into regions/tissues whose characteristics are similar, three different clustering schemes are presented. Finally, several methods for improving quality of the mesh and its adaptation to the image structure are also discussed.

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The performance of deep neural networks and estimations of their robustness has been rapidly developed. In contrast, despite the broad usage of deep convolutional neural networks (CNNs)[1] for medical image segmentation, research on their uncertainty estimations is being far less conducted. Deep learning tools in their nature do not capture the model uncertainty and in this sense, the output of deep neural networks needs to be critically analysed with quantitative measurements, especially for applications in the medical domain. In this work, epistemic uncertainty, which is one of the main types of uncertainties (epistemic and aleatoric) is analyzed and measured for volumetric medical image segmentation tasks (and possibly more diverse methods for 2D images) at pixel level and structure level. The deep neural network employed as a baseline is 3D U-Net architecture[2], which shares the essential structural concept with U-Net architecture[3], and various techniques are applied to quantify the uncertainty and obtain statistically meaningful results, including test-time data augmentation and deep ensembles. The distribution of the pixel-wise predictions is estimated by Monte Carlo simulations and the entropy is computed to quantify and visualize how uncertain (or certain) the predictions of each pixel are. During the estimation, given the increased network training time in volumetric image segmentation, training an ensemble of networks is extremely time-consuming and thus the focus is on data augmentation and test-time dropouts. The desired outcome is to reduce the computational costs of measuring the uncertainty of the model predictions while maintaining the same level of estimation performance and to increase the reliability of the uncertainty estimation map compared to the conventional methods. The proposed techniques are evaluated on publicly available volumetric image datasets, Combined Healthy Abdominal Organ Segmentation (CHAOS, a set of 3D in-vivo images) from Grand Challenge (https://chaos.grand-challenge.org/). Experiments with the liver segmentation task in 3D Computed Tomography (CT) show the relationship between the prediction accuracy and the uncertainty map obtained by the proposed techniques. / Prestandan hos djupa neurala nätverk och estimeringar av deras robusthet har utvecklats snabbt. Däremot, trots den breda användningen av djupa konvolutionella neurala nätverk (CNN) för medicinsk bildsegmentering, utförs mindre forskning om deras osäkerhetsuppskattningar. Verktyg för djupinlärning fångar inte modellosäkerheten och därför måste utdata från djupa neurala nätverk analyseras kritiskt med kvantitativa mätningar, särskilt för tillämpningar inom den medicinska domänen. I detta arbete analyseras och mäts epistemisk osäkerhet, som är en av huvudtyperna av osäkerheter (epistemisk och aleatorisk) för volymetriska medicinska bildsegmenteringsuppgifter (och möjligen fler olika metoder för 2D-bilder) på pixelnivå och strukturnivå. Det djupa neurala nätverket som används som referens är en 3D U-Net-arkitektur [2] och olika tekniker används för att kvantifiera osäkerheten och erhålla statistiskt meningsfulla resultat, inklusive testtidsdata-augmentering och djupa ensembler. Fördelningen av de pixelvisa förutsägelserna uppskattas av Monte Carlo-simuleringar och entropin beräknas för att kvantifiera och visualisera hur osäkra (eller säkra) förutsägelserna för varje pixel är. Under uppskattningen, med tanke på den ökade nätverksträningstiden i volymetrisk bildsegmentering, är träning av en ensemble av nätverk extremt tidskrävande och därför ligger fokus på dataaugmentering och test-time dropouts. Det önskade resultatet är att minska beräkningskostnaderna för att mäta osäkerheten i modellförutsägelserna samtidigt som man bibehåller samma nivå av estimeringsprestanda och ökar tillförlitligheten för kartan för osäkerhetsuppskattning jämfört med de konventionella metoderna. De föreslagna teknikerna kommer att utvärderas på allmänt tillgängliga volymetriska bilduppsättningar, Combined Healthy Abdominal Organ Segmentation (CHAOS, en uppsättning 3D in-vivo-bilder) från Grand Challenge (https://chaos.grand-challenge.org/). Experiment med segmenteringsuppgiften för lever i 3D Computed Tomography (CT) vissambandet mellan prediktionsnoggrannheten och osäkerhetskartan som erhålls med de föreslagna teknikerna.

Volumetric clouds are often used in video games in order to improve the realism or graphical quality of the game. However, in order to achieve real-time rendered clouds, optimizations have to be implemented as part of the rendering algorithm. These kinds of optimizations improve the performance, but can also have a negative impact on the visual quality of the clouds. This thesis investigates the use of bilinear interpolation for the purpose of improving the performance of a volumetric cloud renderer, while trying to avoid any substantial reduction in visual quality. This is extended by looking at the effect of adaptively sampling the pixel colors. The renderer itself is created in Unity3D using a ray marching algorithm. As part of the literature study, this research also explores different ways of measuring visual quality within real-time rendering. As a result of this, the thesis uses the Structural Similarity Index Measure to measure the visual quality. The research found that utilizing bilinear interpolation to ray march every eighth pixel results in a performance gain of 45%. However, it also reduces the visual quality of the volumetric clouds. This is counteracted by using adaptive sampling to interpolate only where the standard deviation of pixel colors is below a threshold. We cannot, however, determine the optimal value of this parameter, since it depends on the requirements of the renderer. Instead, it has to be determined on a case-by-case basis. / Volymetriska moln används i spel för att uppnå realism och förbättra den grafiska kvaliteten. Men för att uppnå realtidsrendering så måste optimeringar göras. Dessa typer av optimeringar förbättrar prestandan av programmet, men kan också försämra den visuella kvalteten. Den här studien undersöker hur en optimering baserad på bilinjär interpolering kan användas för att förbättra prestandan av volymetriska moln, utan att försämra den visuella kvaliteten i någon större utsträckning. Studien tittar även på hur adaptiv sampling av pixlarna påverkar optimeringen. För att utföra detta renderas molnen i Unity3D med hjälp av en ray marching-algoritm. Som del av litteraturstudien utforskas även olika sätt att evaluera visuell kvalitet inom realtidsrendering. Utifrån denna använder studien måttet Structural Similarity Index Measure för att mäta visuell kvalitet. Studien fann att den bilinjära interpoleringen resulterade i att prestandan ökade med 45% när endast var åttonde pixel är beräknad med ray marching, och resten interpoleras. Dock reduceras även den visuella kvaliteten av molnen. Detta kan motverkas med hjälp av adaptiv sampling. Då interpoleras endast pixlar där standardavvikelsen av de kringliggande pixlarna är under ett fördefinierat värde. Vi kan däremot inte definiera ett universiellt optimalt värde på detta värde. Det beror på att det optimala värdet beror på kraven vi har på programmet. Dessa kan variera från program till program. Därför måste detta bestämmas individuellt för varje program.

Water availability and arable lands are increasingly limiting resources in many parts of the U.S., particularly in semi-arid and arid regions. As a means of addressing food and fuel demands associated with burgeoning population growth, highly productive and water-use efficient crops need to be identified. One potential crop, Agave, merits consideration and evaluation due to its putative capability to provide sustenance and energy despite growing in water-limited regions and on marginal soils. However, little is known regarding the productivity these succulent plants will have under growing conditions of the Southwest, where high concentrated saline soils are abundant, and water is often limited. The objectives of these studies were to determine the effects of high levels of salinity and different volumetric water content levels (VWC) on plant growth, biomass accumulation, and nutrient uptake. I used a hydroponic study to compare the effects of four salinity treatments (0.5, 3, 6, and 9 dS m-1) on productivity of four Agave species (Agave parryi, Agave utahensis ssp. kaibabensis, Agave utahensis ssp. utahensis, and Agave weberi). In a second study, an automated irrigation system was established to examine four pre-determined VWC threshold set-points and simulated a gradient of well-watered to drought conditions, to evaluate how A. weberi would respond to varying levels of water availability. Salinity concentrations did not significantly affect root and plant dry weight accumulation in A. weberi, but all other agave plants experienced less biomass accumulation under high saline conditions (>6 dS m-1). Seedlings of A. utahensis were two times more likely to die in the two highest saline treatments (6 and 9 dS m-1) than the two lower treatments (0.5 dS m-1 and 3 dS m-1). Calcium, Mg, S, Mn levels decreased in both A. parryi and A. weberi at higher salinity levels. Agave weberi was able to tolerate salinity, but it also experienced lower biomass production ≤3 dS m-1. In the water-stress study, Agave weberi plants experienced a decrease of 2.11 g as compared to plants in the highest treatment. Plants in the intermediate VWC treatments had similar dry mass values as those in the highest treatment, which suggests that this species could have moderately high yields under limited water conditions, and consequently should be evaluated as a potential bioenergy crop for semi-arid regions, such as the U.S. Southwest. Agave shows considerable potential to be grown in arid and semi-arid regions that are moderately high in salinity and have limited water availability. Indeed, the cultivation of Agave as a crop appears to be a viable option for many areas of the Southwest. While some of the Agave species evaluated were quite productive under moderate salt and water stress, it is uncertain if growth will be significantly reduced if under these stress conditions for periods longer than 3 months.

[pt] A visualização volumétrica é uma técnica amplamente utilizada para visualizar dados escalares tridimensionais. Para melhorar a percepção de profundidade e forma, diversas técnicas de iluminação foram propostas, adicionando diferentes tipos de efeitos. Neste trabalho, foi explorada uma nova estratégia para calcular oclusão de ambiente direcional e sombras para volume ray casting. Ela consiste em avaliar a oclusão de um traçado de cone atraves de integrais Gaussianas posicionadas de maneira discreta ao longo do eixo do cone. O valor resultado é utilizado para adicionar oclusão de ambiente direcional e sombras. A partir dos coeficientes de extinção dados pela função de transferência, um volume extra é gerado computando amplitudes representativas de distribuições Gaussianas. O Mipmapping também é utilizado para avaliar de maneira efetiva integrais Gaussianas em diferentes tamanhos posicionadas ao longo do eixo principal do cone, adaptando uma estratégia de circle packing in a circle. Nos resultados, é demonstrado que o método proposto obteve um melhor balanço entre performance e qualidade, comparado com trabalhos propostos anteriormente, com a vantagem de combinar oclusão de ambiente direcional e sombras utilizando o mesmo framework. Em seguida, exploramos três estratégias de visualização volumétrica para reservatórios de petróleo, representados por malhas irregulares contendo distorções geométricas e descontinuidades. Estes algoritmos foram implementados a partir de uma representação compacta que guarda o modelo em GPU. Testes comparativos de performance e qualidade foram feitos utilizando diferentes modelos de reservatório. Por fim, investigamos o ganho de percepção ao adicionar a nossa proposta de oclusão de ambiente direcional. Os algoritmos foram todos implementados utilizando programação de shaders para capacitar a geração de visualizações interativas. / [en] Volume rendering is a widely used technique to visualize 3D scalar data. To enhance visual shape and depth perception, distinct illumination techniques have been proposed, adding different types of lighting effects. In this thesis, we explore a new strategy to compute directional ambient occlusion and shadows for volume ray casting. Our algorithm computes occlusion of traced cones by evaluating Gaussian integrals at discrete samples along the cone axis. The computed occlusion is then used to add directional ambient occlusion effects and to generate shadows. Given the extinction coefficient data volume, we create one extra volume computing representative amplitudes of Gaussian functions. Mipmapping is then used to effectively evaluate Gaussian integrals with different sizes placed along the cone axis, adapting a circle packing approach. We demonstrate that the proposed method delivers a better balance between quality results and performance when compared to previous specialized procedures, with the advantage of combining directional ambient occlusion and shadow generation under the same framework. We also explore three volume rendering algorithms for black oil reservoir models, represented by irregular hexahedral meshes with geometry distortions and discontinuities. These algorithms were implemented under a compact representation that stores the model in the GPU. We compare performance and image quality delivered by each strategy by running a set of experiments with different models. We then investigate the gain in perception when applying our technique to compute directional ambient occlusion effects. The algorithms were entirely implemented on graphics card to produce interactive visualizations.

Measuring soil moisture content (SMC) in mining waste rock is important for assessing and modelling hydrological processes which influence pollutant release. Here, an experimental setup containing mining waste rock is established to compare the performance of 4 Arduino capacitance moisture sensors to one single Time Domain Reflectometry (TDR) sensor. Furthermore, the performance of these sensors is evaluated in both sieved and unsieved mining waste rock. Fitted calibration curves are provided for both the TDR- and Arduino-sensors individually and in combination. These calibration curves are evaluated using the RMSE and R 2 of each curve and compared between sensors and soil texture. It is concluded that using more capacitance sensors significantly improves the fit statistics of the calibration curves and that using at least 4 capacitance sensors can enhance calibration curve fitting. For both the TDR and capacitance sensors, the calibration curves in sieved soil provided the best fit, meaning that soil specific calibration of sensors is recommended. On a sensor individual basis, the temporal precision of the TDR sensor was superior to each individual capacitance sensor. Use of 4 or more Arduino capacitance sensors may especially be justified in circumstances where the spatial variability of SMC is addressed by executing a large number of measurements. Here, the feasibility of the Arduino sensor system means that the use of these low-cost sensors, despite their reduced temporal precision, can be upscaled at relatively small costs.

The environmental impacts of the construction industry, and the built environment, as the end product of construction activity, are massive due to the extensive use of energy and resources. Sustainability and circular economy are recognized as the main solution to the existing environmental impacts of this industry and the main approach to the sustainable development of it. The transition of a construction project’s life cycle from a linear one starting with material processing and ending up at demolition stage to a real cyclic life cycle needs defining alternative end-of-life scenarios. Deconstruction as one of the alternative end-of-life scenarios is defined as salvaging material of a dismantled structure for reuse or recycling. Design for deconstruction refers to the importance of considering deconstruction as the end-of-life scenario in the design stage of structures. This thesis aims to define the existing challenges and opportunities in the deconstruction of timber structures by the study of cases in which dis- and re-assembly are done. The study also aims to measure deconstructability by developing an indicator system for design for deconstruction of volumetric timber structures based on design for disassembly principles existing in ISO 20887:2020 and the practical principles of the case studies. This is done by interviewing people at five different companies who were engaged in the dis- and re-assembly of structures. The study of these cases shows the gap between disassembly principles and real-world experience from a learn-from-practice point of view. The developed tool as the indicator system is consisted of 25 indicators under seven principles and is able to measure and provide a deconstructability grade of structures out of 100 for each principle and a total deconstructability grade based on all principles. / Measurable Reusability for Wooden Houses (Mätbar Återbrukbarhet för Trähus)

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