Additive manufacturing (AM) allows building complex shapes with high accuracy. The X-ray Computed Tomography (XCT) is one of the promising non-destructive evaluation techniques for the evaluation of subsurface defects in an additively manufactured component. Automatic defect detection and segmentation methods can assist part inspection for quality control. However, automatic detection and segmentation of defects in XCT data of AM possess challenges due to contrast, size, and appearance of defects. In this research different deep learning techniques have been applied on publicly available XCT image datasets of additively manufactured cobalt chrome samples produced by the National Institute of Standards and Technology (NIST). To assist the data labeling image processing techniques were applied which are median filtering, auto local thresholding using Bernsen’s algorithm, and contour detection. A convolutional neural network (CNN) based state-of-art object algorithm YOLOv5 was applied for defect detection. Defect segmentation in XCT slices was successfully achieved applying U-Net, a CNN-based network originally developed for biomedical image segmentation. Three different variants of YOLOv5 which are YOLOv5s, YOLOv5m, and YOLOV5l were implemented in this study. YOLOv5s achieved defect detection mean average precision (mAP) of 88.45 % at an intersection over union (IoU) threshold of 0.5. And mAP of 57.78% at IoU threshold 0.5 to 0.95 using YOLOv5M was achieved. Additionally, defect detection recall of 87.65% was achieved using YOLOv5s, whereas a precision of 71.61 % was found using YOLOv5l. YOLOv5 and U-Net show promising results for defect detection and segmentation respectively. Thus, it is found that deep learning techniques can improve the automatic defect detection and segmentation in XCT data of AM.

The detection of cracks in sand moulds has long been a challenge for both safety and maintenance purposes. Traditional image processing techniques have been employed to identify and quantify these defects but have often proven to be inefficient, labour-intensive, and time-consuming. To address this issue, we sought to develop a more effective approach using deep learning techniques, specifically semantic segmentation. We initially examined three different architectures—U-Net, SegNet, and DeepCrack—to evaluate their performance in crack detection. Through testing and comparison, U-Net emerged as the most suitable choice for our project. To further enhance the model's accuracy, we combined U-Net with VGG-19, VGG-16, and ResNet architectures. However, these combinations did not yield the expected improvements in performance. Consequently, we introduced a new layer to the U-Net architecture, which significantly increased its accuracy and F1 score, making it more efficient for crack detection. Throughout the project, we conducted extensive comparisons between models to better understand the effects of various techniques such as batch normalization and dropout. To evaluate and compare the performance of the different models, we employed the loss function, accuracy, Adam optimizer, and F1 score as evaluation metrics. Some tables and figures explain the differences between models by using image comparison and evaluation metrics comparison; to show which model is better than the other. The conducted evaluations revealed that the U-Net architecture, when enhanced with an extra layer, proved superior to other models, demonstrating the highest scores and accuracy. This architecture has shown itself to be the most effective model for crack detection, thereby laying the foundation for a more cost-efficient and trustworthy approach to detecting and monitoring structural deficiencies.

The first Metatarsophalangeal (MTP) joint is essential for foot biomechanics and weight-bearing activities. Osteoarthritis in this joint can lead to pain, discomfort, and limited mobility. In order to treat this, Episurf Medical is working to produce individualized implants based on 3D segmentations of the joint. As manual segmentations are both time- and cost-consuming, and susceptible to human errors, automatic approaches are preferred. This thesis uses U-Net and DeepEdit as deep-learning based methods for segmentation of the MTP joint, with the latter being evaluated with and without user interactions. The dataset used in this study consisted of 38 CT images, where each model was trained on 30 images, and the remaining images were used as a test set. The final models were evaluated and compared with regards to the Dice Similarity Coefficient (DSC), precision, and recall. The U-Net model achieved DSC 0.944, precision 0.961, and recall 0.929. The automatic DeepEdit approach obtained DSC of 0.861, precision of 0.842, and recall of 0.891, while the interactive DeepEdit approach resulted in DSC of 0.918, precision of 0.912, and recall of 0.928. All pairwise comparisons in terms of precision and DSC showed significant differences (p<0.05), where U-Net had the highest performance, while the difference in recall was not found to be significant (p>0.05) for any comparison. The lower performances of DeepEdit compared to U-Net could be due to lower spatial resolution in the segmentations. Nevertheless, DeepEdit remains a promising method, and further investigations of unexplored areas could be addressed as future work. / Den första Metatarsalphalangeal(MTP) leden är viktig för fotens biomekanik och viktbärande aktiviteter. Artros i denna led kan leda till smärta, obehag och begränsad rörlighet. För att behandla detta arbetar Episurf Medical med att producera individanpassade implantat baserat på 3D segmenteringar av leden. Då manuella segmenteringar både är tids- och kostnadskrävande, samt känsliga för mänskliga fel, föredras automatiska metoder. Denna avhandling använder U-Net och DeepEdit som djupinlärningsbaserade metoder för segm- entering av MTP leden, varav det senare utvärderas med och utan användarint- eraktion. Datasetet som användes i denna studie bestod av 38 CT bilder, där varje modell tränades på 30 bilder och de återstående användes som testdata. De slutliga modellerna utvärderades och jämfördes med avseende på Dice Similarity Coefficient (DSC), precision och recall. U-Net modellen uppnådde DSC 0.944, precision 0.961 och recall 0.929. Den automatiska DeepEdit metoden erhöll DSC 0.861, precision 0.842 och recall 0.891, medan den interaktiva DeepEdit metoden resulterade i DSC 0.918, precision 0.912 och recall 0.928. Alla parvisa jämförelser avseende precision och DSC visade signifikanta skillnader (p<0.05), där U-Net hade den högsta prestandan, medan skillnaden i recall inte visade sig vara signifikant (p>0.05) för någon jämförelse. Den lägre prestandan för DeepEdit jämfört med U-Net kan bero på lägre spatiell upplösning i segmenteringarna. Dock är DeepEdit fortfarande en lovande metod, och ytterligare undersökningar av outforskade områden kan tas upp som framtida arbete.

In this project, a model based on a convolutional neural network have been developed with the aim of imputing missing genotype data. This model was based on an already existing autoencoder that was modified into a U-Net structure. The network was trained and used iteratively with the intention that the result would improve in each iteration. In order to do this, the output of the model was used as the input in the next iteration. The data used in this project was diploid genotype data, which was phased into haploids and then run separately through the network. In each iteration, the new haploids were generated based on the output haploids. These were used as in input in the next iteration. The result showed that the accuracy of the imputation improved slightly in every iteration. However, it did not surpass the same model that was trained for one single iteration. Further work is needed to make the model more useful.

Over the past few decades, additive manufacturing (AM) or 3D printing (3DP) technologies witnessed revolutionary growth in the manufacturing sector. Parts produced with metal AM techniques, especially Laser Powder Bed Fusion (LPBF), are often prone to porosity issues. The presence of pores leads to harmful effects such as crack formation and, eventually, premature failure of the component. Consequently, research in defect detection and pore prediction attracted substantial attention. Utilizing image-based porosity detection in preexisting systems is a simple, effective, and cost-efficient approach for final part inspection. This thesis investigates the possibility of predicting porosity using U-Net and its novel network architectures named RU-Net and RAU-Net, on an X-ray computed tomography (XCT) image dataset. Later, the performance of these models is analyzed and compared using precision, recall, F1 score, mAP, IoU metrics, and their hybrid losses combining BCG and Dice loss. RAU-Net outperforms RU-Net and U-Net in all these metrics by detecting more than 90% of actual pores while retaining 95% precision. While RU-Net and U-Net required additional training, RAU-Net achieved high performance in only 50 epochs, demonstrating its data efficiency and convergence. Due to its shorter training period, also leading to lower computational overhead, RAU-Net is suited for practical high throughput and low latency applications. Particularly in time-sensitive applications, RAU-Net can enable more widespread adoption of dense prediction networks. A custom script is developed for estimating the porosity percentage level in 3D printed metal components precisely, further enhancing final product inspection procedures. As a result, the entire quality control process is simplified, which allows for the quicker inspection of final components to deliver, by ensuring they meet required quality and reliability standards.

Solcellspaneler har fått ökad uppmärksamhet som en betydande källa till förnybar energi på grund av den ökande medvetenheten om klimatförändringar och behovet av hållbara energilösningar. I Sverige har detta lett till en kraftig ökning av solcellsanläggningar. I och med ökningen av solcellspaneler i urbana miljöer, har behovet av att kartlägga och inventera dessa anläggningar växt. Framsteg inom artificiell intelligens (AI) och bildanalys har öppnat möjligheter för automatiserade metoder som effektivt kan identifiera och segmentera solcellspaneler. Syftet med detta arbete är att utforska potentialen hos traditionella AI-modeller, som Support Vector Machines (SVM) och Random Forest (RF), samt faltningsnätverk av typen U-net, för att identifiera och segmentera solcellspaneler i svenska flygfotodata. Vidare undersöks i arbetet hur dessa modeller reagerar på reducerad datamängd samt vad för sorts features som höjer de traditionella modellernas prestanda i syfte att inventera solcellspaneler. Under arbetet skapas ett dataset om 2268, 1152 RGBI-data, där solcellspaneler utgör 22,8 procent av pixlarna. Data är hämtad från Lantmäteriet och har en spatial upplösning på 0.16m/pixel. Tre modeller implementeras och jämförs under olika förhållanden. Flertalet features utvunna från datasetet presenteras och förändringar av prestanda vid träning med dessa features mäts. För en utvärdering av modellernas precision tränas de först på 70% av det totala datasetet och utvärderas på de resterande 30%. En andra utvärdering utförs med reducerad datamängd där 35% av den totala datamängden används för träning och 30% för utvärdering. Prestandamätningar utförs på samma dataset för alla modeller där traditionella modeller tränas på RGB, RGBI, RGBI+features. U-net-modellen tränas på RGB-data. Resultaten visar att U-net-modellen presterar bäst i syfte att segmentera solcellspaneler med en F1-score på 0.91 och MCC på 0.89. Näst bäst är RF med en F1-score på 0.81 samt MCC på 0.76. Vid halvering av mängd träningsdata observeras störst negativ förändring av prestation på U-net-modellen, medan de traditionella modellerna syns påverkas mindre. Rätt urval av features observeras markant höja prestationen hos de traditionella modellerna. Sammanfattningsvis påvisar resultaten att neurala nätverk presterar bättre än traditionella modeller vid inventering av svenska solcellspaneler och betonar samtidigt vikten av rätt feature selection hos traditionella maskininlärningsmodeller. / Solar panels have gained increased attention as a significant source of renewable energy due to the growing awareness of climate change and the need for sustainable energy solutions. In Sweden, this has led to a rise in solar installations. With the increase of solar panels in urban areas, the need for accurate mapping and inventory of these installations has grown. Advances in artificial intelligence (AI) and image analysis have opened possibilities for automated methods for high precision identification av segmentation of solar panels. These automated methods reduce time consumption, resource use and the risk of human error. The aim of this work is to explore the potential of traditional AI models such as Support Vector Machines (SVM) and Random Forest (RF), as well as convolutional neural networks with the U-net architecture, to identify and segment solar panels in Swedish aerial imagery. Furthermore, the study investigates how these models perform with reduced data quantities. The study also examines which types of features enhance the performance of traditional models for the purpose of inventorying solar panels. During the study, a dataset of 2268, 1152 RGBI data was created, where solar panels constitute 22.8 percent of the pixels. This data, sourced from Lantmäteriet, has a spatial resolution of 0.16m/pixel. Three models were implemented and compared under various conditions. Multiple features extracted from the dataset were presented and performance changes during training with these features were measured. For evaluation the models were first trained on 70% of the total dataset and evaluated on the remaining 30%. A second evaluation was conducted with reduced data, using 35% for training and 30% for evaluation. Performance measurements were carried out on the same dataset for all models, where the traditional models were trained on RGB, RGBI, and RGBI + features, while the U-net model was trained on RGB data. In evaluation the U-net model achieved the highest performance in solar panel segmentation with an F1-score of 0.91 and an MCC of 0.89, followed by RF with an F1-score of 0.81 and an MCC of 0.76. Halving the training data resulted in a bigger impact on U-net's performance than on the traditional models. Optimal feature selection substantially improved traditional models, doubling SVM's F1-score when trained with additional features. In summary, the results indicate that neural networks perform better than traditional models in inventorying Swedish solar panels and emphasize the importance of correct feature selection in traditional machine learning models.

Accurate medical image segmentation is crucial for safely and effectively administering radiation therapy in cancer treatment. State of the art methods for automatic segmentation of 3D images are currently based on the U-net machine learning architecture. The current U-net models are large, often containing millions of parameters. However, the size of these machine learning models can be decreased by removing parts of the models, in what is called pruning. One algorithm, called simultaneous training and pruning (STAMP) has shown capable of reducing the model sizes upwards of 80% while keeping similar or higher levels of performance for medical image segmentation tasks.  This thesis investigates the impact of using the STAMP algorithm to reduce model size and inference time for medical image segmentation on 3D images, using one MRI and two CT datasets. Surprisingly, we show that pruning convolutional filters randomly achieves performance comparable, if not better than STAMP, provided that the filters are always removed from the largest parts of the U-net.  Inspired by these results, a modified "Flat U-net" is proposed, where an equal number of convolutional filters are used in all parts of the U-net, similar to what was achieved after pruning with our simplified pruning algorithm. The modified U-net achieves similar levels of test dice score as both a regular U-net and the STAMP pruning algorithm, on multiple datasets while avoiding pruning altogether. In addition to this the proposed modification reduces the model size by more than a factor of 12, and the number of computations by around 35%, compared to a normal U-net with the same number of input-layer convolutional filters.

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For manufacturers in the wood industry, an important way to make the production more effective is to automate the process of detecting defects and different attributes on boards. One important attribute on most boards is heartwood and sapwood. This thesis project was conducted at the company MiCROTEC and aims to investigate methods to classify heartwood and sapwood on boards. The dataset used in this project consisted of oak boards. In order to increase the amount of information retrieved from the boards, hyperspectral imaging was used instead of conventional RGB cameras. Based on this data, deep learning models in the form of U-Net and U-within-U-Net architecture as well as different spectral dimensionality reduction methods were developed to segment boards in heartwood and sapwood. The performance of these deep learning models was compared to PLS-DA and SVM. PLS-DA has already been used at MiCROTEC and has been used in this work for comparison as a baseline model.   The result of the thesis work showed that a deep learning approach could increase the F1-Score from 0.730 for the baseline classifier PLS-DA to an F1-Score of 0.918, and that the different spectral reduction methods only had a small impact on the result. The increase in F1-score was mainly due to an increase in precision, since the PLS-DA had a similar recall as the deep learning models.

The ability to quantify uncertainties associated with neural network predictions is crucial when they are relied upon in decision-making processes, especially in safety-critical applications like radiation therapy. In this paper, a single-model estimator of both epistemic and aleatoric uncertainties in a regression 3D U-net used for radiation dose prediction is presented. To capture epistemic uncertainty, Monte Carlo Dropout is employed, leveraging dropout during test-time inference to obtain a distribution of predictions. The variability among these predictions is used to estimate the model’s epistemic uncertainty. For quantifying aleatoric uncertainty quantile regression, which models conditional quantiles of the output distribution, is used. The method enables the estimation of prediction intervals of a user-specified significance level, where the difference between the upper and lower bound of the interval quantifies the aleatoric uncertainty. The proposed approach is evaluated on two datasets of prostate and breast cancer patient geometries and corresponding radiation doses. Results demonstrate that the quantile regression method provides well-calibrated prediction intervals, allowing for reliable aleatoric uncertainty estimation. Furthermore, the epistemic uncertainty obtained through Monte Carlo Dropout proves effective in identifying out-of-distribution examples, highlighting its usefulness for detecting anomalous cases where the model makes uncertain predictions. / Förmågan att kvantifiera osäkerheter i samband med neurala nätverksprediktioner är avgörande när de åberopas i beslutsprocesser, särskilt i säkerhetskritiska tillämpningar såsom strålterapi. I denna rapport presenteras en en-modellsimplementation för att uppskatta både epistemiska och aleatoriska osäkerheter i ett 3D regressions-U-net som används för att prediktera stråldos. För att fånga epistemisk osäkerhet används Monte Carlo Dropout, som utnyttjar dropout under testtidsinferens för att få en fördelning av prediktioner. Variabiliteten mellan dessa prediktioner används för att uppskatta modellens epistemiska osäkerhet. För att kvantifiera den aleatoriska osäkerheten används kvantilregression, eller quantile regression, som modellerar de betingade kvantilerna i outputfördelningen. Metoden möjliggör uppskattning av prediktionsintervall med en användardefinierad signifikansnivå, där skillnaden mellan intervallets övre och undre gräns kvantifierar den aleatoriska osäkerheten. Den föreslagna metoden utvärderas på två dataset innehållandes geometrier för prostata- och bröstcancerpatienter och korresponderande stråldoser. Resultaten visar på att kvantilregression ger välkalibrerade prediktionsintervall, vilket tillåter en tillförlitlig uppskattning av den aleatoriska osäkerheten. Dessutom visar sig den epistemiska osäkerhet som erhålls genom Monte Carlo Dropout vara användbar för att identifiera datapunkter som inte tillhör samma fördelning som träningsdatan, vilket belyser dess lämplighet för att upptäcka avvikande datapunkter där modellen gör osäkra prediktioner.