Comparative assessment of induced abnormal mitotic events by high-throughput light sheet imaging and image analysis

In recent years, three-dimensional (3D) in vitro cell culture models such as spheroids and organoids have revolutionized life science research by providing a much more reliable context resembling the in vivo microenvironment. These systems yield important cell-to-cell interactions and induce cell differentiation. However, no conventional microscopy setup can provide sufficient imaging throughput as well as spatial and temporal resolution to enable full 3D live imaging and analysis down to subcellular processes. In this project, we established state-of-the-art light sheet microscopy for live, long-term imaging of a short interfering ribonucleic acid (siRNA) treated 3D cell culture model. Due to the high temporal and special resolution of the light sheet microscope, we minimized imaging artifacts and achieved unprecedented visual representations of spheroids throughout development and upon gene knock-down by siRNAs. Furthermore, we deployed a high-throughput image analysis pipeline and machine learning classification to evaluate global, cellular and subcellular features for a precise, quantitative gene knock-down phenotype description. The RNA interference (RNAi) induced gene knock-down phenotypes were replicated and compared by a novel molecular, site-specificepigenome modifying method. Throughout this project, we carefully evaluated every step of the workflow to improved its throughput and increased its reproducibility and usability. We addressed the key challenges in light sheet microscopy, such as sample preparation, data handling, image processing and analysis, thereby establishing quantitative light sheet microscopy screening of 3D cell culture models for many research applications. In total, we believe that our workflow can provide the basis for high-content analysis of 3D cell culture models for future research, enabling much more detailed functional experiments and basic research studies

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

A Novel System and Image Processing for Improving 3D Ultrasound-guided Interventional Cancer Procedures

Image-guided medical interventions are diagnostic and therapeutic procedures that focus on minimizing surgical incisions for improving disease management and reducing patient burden relative to conventional techniques. Interventional approaches, such as biopsy, brachytherapy, and ablation procedures, have been used in the management of cancer for many anatomical regions, including the prostate and liver. Needles and needle-like tools are often used for achieving planned clinical outcomes, but the increased dependency on accurate targeting, guidance, and verification can limit the widespread adoption and clinical scope of these procedures. Image-guided interventions that incorporate 3D information intraoperatively have been shown to improve the accuracy and feasibility of these procedures, but clinical needs still exist for improving workflow and reducing physician variability with widely applicable cost-conscience approaches. The objective of this thesis was to incorporate 3D ultrasound (US) imaging and image processing methods during image-guided cancer interventions in the prostate and liver to provide accessible, fast, and accurate approaches for clinical improvements. An automatic 2D-3D transrectal ultrasound (TRUS) registration algorithm was optimized and implemented in a 3D TRUS-guided system to provide continuous prostate motion corrections with sub-millimeter and sub-degree error in 36 ± 4 ms. An automatic and generalizable 3D TRUS prostate segmentation method was developed on a diverse clinical dataset of patient images from biopsy and brachytherapy procedures, resulting in errors at gold standard accuracy with a computation time of 0.62 s. After validation of mechanical and image reconstruction accuracy, a novel 3D US system for focal liver tumor therapy was developed to guide therapy applicators with 4.27 ± 2.47 mm error. The verification of applicators post-insertion motivated the development of a 3D US applicator segmentation approach, which was demonstrated to provide clinically feasible assessments in 0.246 ± 0.007 s. Lastly, a general needle and applicator tool segmentation algorithm was developed to provide accurate intraoperative and real-time insertion feedback for multiple anatomical locations during a variety of clinical interventional procedures. Clinical translation of these developed approaches has the potential to extend the overall patient quality of life and outcomes by improving detection rates and reducing local cancer recurrence in patients with prostate and liver cancer