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@PHDTHESIS{Ruzaeva:1009666,
      author       = {Ruzaeva, Karina},
      othercontributors = {Berkels, Benjamin and Berlage, Thomas Leo and Wiechert,
                          Wolfgang},
      title        = {{G}eometry-aware image analysis for microfluidic live-cell
                      experimentation},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2025-03601},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2025},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2025},
      abstract     = {Time-lapse microscopy combined with advanced imaging
                      techniques offers new opportunities to approach fundamental
                      and applied biological questions. Specifically, microfluidic
                      tools enable the study of living cells under precisely
                      controlled environmental conditions (medium, temperature,
                      light) and allow for their observation through live-cell
                      microscopy. This approach enables the capture of
                      time-resolved cellular responses, which are recorded as
                      high-volume time-lapse image sequences. Processing these
                      large-scale image sequences poses numerous challenges for
                      image analysis, such as varying noise levels, low-intensity
                      gradients and limited image capture rates. Addressing these
                      challenges is crucial for developing biotechnological
                      processes as it becomes increasingly necessary better to
                      understand the behavior of microorganisms at the single-cell
                      level. Advanced image-processing techniques play a critical
                      role in extracting valuable insights from these datasets,
                      which are essential for optimizing biotechnological
                      applications. The dissertation “Geometry-aware image
                      analysis for microfluidic live-cell experimentation”
                      emphasizes the importance of incorporating prior knowledge
                      of microorganisms' geometry and behavior—specifically
                      their shape, size, and division mechanisms—into image
                      analysis techniques. In this dissertation, we present an
                      image processing workflow comprising ground truth data
                      generation, segmentation, and tracking. This ground truth
                      generation and subsequent segmentation and tracking
                      algorithms are informed by the geometry and behavioral
                      characteristics of cells, which are determined by the
                      selected microorganism. To implement this workflow, this
                      dissertation proposes ground truth data generation methods
                      that include both synthetic image simulations and the
                      processing of annotations from real data. For the
                      segmentation task, we combine geometry-aware variational
                      spline-based segmentation with machine learning-based
                      detection to enhance the accuracy of cell identification.
                      This approach is complemented by activity-based tracking
                      that monitors cell behavior over time, enabling the
                      extraction of critical parameters such as cell size, count,
                      and dynamic behavior. The extracted data can be used to
                      dynamically adjust bioprocess conditions to optimize growth
                      and yield, leading to greater efficiency and productivity in
                      biotechnological processes. By integrating this geometry and
                      behavior-aware image processing methods, including ground
                      truth generation, geometry-aware segmentation, and
                      activity-based tracking, this dissertation underscores the
                      potential of precise image analysis in enhancing live-cell
                      experimentation. The results not only improve the accuracy
                      of single-cell analysis but also help to optimize
                      biotechnological processes by understanding cellular
                      behavior.},
      cin          = {111410 / 112430 / 110000},
      ddc          = {510},
      cid          = {$I:(DE-82)111410_20170801$ / $I:(DE-82)112430_20140620$ /
                      $I:(DE-82)110000_20140620$},
      pnm          = {HDS LEE - Helmholtz School for Data Science in Life, Earth
                      and Energy (HDS LEE) (HDS-LEE-20190612) /
                      Doktorandenprogramm (PHD-PROGRAM-20170404)},
      pid          = {G:(DE-Juel1)HDS-LEE-20190612 /
                      G:(DE-HGF)PHD-PROGRAM-20170404},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2025-03601},
      url          = {https://publications.rwth-aachen.de/record/1009666},
}