<p dir="ltr">Live cell imaging is a method to optically investigate living cells using microscopy images. It plays an increasingly important role in biomedical research as well as drug development. In this thesis, we focus on label-free mammalian cell tracking and label-free abnormally shaped nuclei segmentation of microscopy images. We propose a method to use a precomputed velocity field to enhance cell tracking performance. Additionally, we propose an ensemble method, Weighted Mask Fusion (WMF), combining the results of multiple segmentation models with shape analysis, to improve the final nuclei segmentation mask. We also propose an edge-aware Mask RCNN and introduce a hybrid architecture, an ensemble of CNNs and Swin-Transformer Edge Mask R-CNNs (HER-CNN), to accurately segment irregularly shaped nuclei of microscopy images. Our experiments indicate that our proposed method outperforms other existing methods for cell tracking and abnormally shaped nuclei segmentation.</p><p dir="ltr">While image-based dietary assessment methods reduce the time and labor required for nutrient analysis, the major challenge with deep learning-based approaches is that the performance is heavily dependent on the quality of the datasets. Challenges with food data include suffering from high intra-class variance and class imbalance. In this thesis, we present an effective clustering-based training framework named ClusDiff for generating high-quality and representative food images. From experiments, we showcase our method’s effectiveness in enhancing food image generation. Additionally, we conduct a study on the utilization of synthetic food images to address the class imbalance issue in long-tailed food classification.</p>

<div>Recent advances in optical microscopy have enabled biologists collect fluorescence microscopy volumes cellular and subcellular structures of living tissue. This results in collecting large datasets of microscopy volume and needs image processing aided automated quantification method. To quantify biological structures a first and fundamental step is segmentation. Yet, the quantitative analysis of the microscopy volume is hampered by light diffraction, distortion created by lens aberrations in different directions, complex variation of biological structures. This thesis describes several proposed segmentation methods to identify various biological structures such as nuclei or tubules observed in fluorescence microscopy volumes. To achieve nuclei segmentation, multiscale edge detection method and 3D active contours with inhomogeneity correction method are used for segmenting nuclei. Our proposed 3D active contours with inhomogeneity correction method utilizes 3D microscopy volume information while addressing intensity inhomogeneity across vertical and horizontal directions. To achieve tubules segmentation, ellipse model fitting to tubule boundary method and convolutional neural networks with inhomogeneity correction method are performed. More specifically, ellipse fitting method utilizes a combination of adaptive and global thresholding, potentials, z direction refinement, branch pruning, end point matching, and boundary fitting steps to delineate tubular objects. Also, the deep learning based method combines intensity inhomogeneity correction, data augmentation, followed by convolutional neural networks architecture. Moreover, this thesis demonstrates a new deconvolution method to improve microscopy image quality without knowing the 3D point spread function using a spatially constrained cycle-consistent adversarial networks. The results of proposed methods are visually and numerically compared with other methods. Experimental results demonstrate that our proposed methods achieve better performance than other methods for nuclei/tubules segmentation as well as deconvolution.</div>

Out-of-distribution detection considers methods used to detect data that deviates from the underlying data distribution used to train some machine learning model. This is an important topic, as artificial neural networks have previously been shown to be capable of producing arbitrarily confident predictions, even for anomalous samples that deviate from the training distribution. Previous work has developed many reportedly effective methods for out-of-distribution detection, but these are often evaluated on data that is semantically different from the training data, and therefore does not necessarily reflect the true performance that these methods would show in more challenging conditions. In this work, six unsupervised out-of- distribution detection methods are evaluated and compared under more challenging conditions, in the context of classification of semantically similar image data using deep neural networks. It is found that the performance of all methods vary significantly across the tested datasets, and that no one method is consistently superior. Encouraging results are found for a method using ensembles of deep neural networks, but overall, the observed performance for all methods is considerably lower than in many related works, where easier tasks are used to evaluate the performance of these methods. / Begreppet “out-of-distribution detection” (OOD-detektion) avser metoder vilka används för att upptäcka data som avviker från den underliggande datafördelningen som använts för att träna en maskininlärningsmodell. Detta är ett viktigt ämne, då artificiella neuronnät tidigare har visat sig benägna att generera godtyckligt säkra förutsägelser, även på data som avviker från den underliggande träningsfördelningen. Tidigare arbeten har producerat många välpresterande OOD-detektionsmetoder, men dessa har ofta utvärderats på data som är semantiskt olikt träningsdata, och reflekterar därför inte nödvändigtvis metodernas förmåga under mer utmanande förutsättningar. I detta arbete utvärderas och jämförs sex oövervakade OOD-detektionsmetoder under utmanande förhållanden, i form av klassificering av semantiskt liknande bilddata med hjälp av djupa neuronnät. Arbetet visar att resultaten för samtliga metoder varierar markant mellan olika data och att ingen enskild modell är konsekvent överlägsen de andra. Arbetet finner lovande resultat för en metod som utnyttjar djupa neuronnätsensembler, men överlag så presterar samtliga modeller sämre än vad tidigare arbeten rapporterat, där mindre utmanande data har nyttjats för att utvärdera metoderna.