Model-based Curvilinear Network Extraction and Tracking toward Quantitative Analysis of Biopolymer Networks

Curvilinear biopolymer networks pervade living systems. They are routinely imaged by fluorescence microscopy to gain insight into their structural, mechanical, and dynamic properties. Image analysis can facilitate understanding the mechanisms of their formation and their biological functions from a quantitative viewpoint. Due to the variability in network geometry, topology and dynamics as well as often low resolution and low signal-to-noise ratio in images, segmentation and tracking networks from these images is challenging. In this dissertation, we propose a complete framework for extracting the geometry and topology of curvilinear biopolymer networks, and also tracking their dynamics from multi-dimensional images. The proposed multiple Stretching Open Active Contours (SOACs) can identify network centerlines and junctions, and infer plausible network topology. Combined with a $k$-partite matching algorithm, temporal correspondences among all the detected filaments can be established. This work enables statistical analysis of structural parameters of biopolymer networks as well as their dynamics. Quantitative evaluation using simulated and experimental images demonstrate its effectiveness and efficiency. Moreover, a principled method of optimizing key parameters without ground truth is proposed for attaining the best extraction result for any type of images. The proposed methods are implemented into a usable open source software ``SOAX\u27\u27. Besides network extraction and tracking, SOAX provides a user-friendly cross-platform GUI for interactive visualization, manual editing and quantitative analysis. Using SOAX to analyze several types of biopolymer networks demonstrates the potential of the proposed methods to help answer key questions in cell biology and biophysics from a quantitative viewpoint

Mathematical Methods for the Quantification of Actin-Filaments in Microscopic Images

In cell biology confocal laser scanning microscopic images of the actin filament of human osteoblasts are produced to assess the cell development. This thesis aims at an advanced approach for accurate quantitative measurements about the morphology of the bright-ridge set of these microscopic images and thus about the actin filament. Therefore automatic preprocessing, tagging and quantification interplay to approximate the capabilities of the human observer to intuitively recognize the filaments correctly. Numerical experiments with random models confirm the accuracy of this approach

Model and Appearance Based Analysis of Neuronal Morphology from Different Microscopy Imaging Modalities

The neuronal morphology analysis is key for understanding how a brain works. This process requires the neuron imaging system with single-cell resolution; however, there is no feasible system for the human brain. Fortunately, the knowledge can be inferred from the model organism, Drosophila melanogaster, to the human system. This dissertation explores the morphology analysis of Drosophila larvae at single-cell resolution in static images and image sequences, as well as multiple microscopy imaging modalities. Our contributions are on both computational methods for morphology quantification and analysis of the influence of the anatomical aspect. We develop novel model-and-appearance-based methods for morphology quantification and illustrate their significance in three neuroscience studies. Modeling of the structure and dynamics of neuronal circuits creates understanding about how connectivity patterns are formed within a motor circuit and determining whether the connectivity map of neurons can be deduced by estimations of neuronal morphology. To address this problem, we study both boundary-based and centerline-based approaches for neuron reconstruction in static volumes. Neuronal mechanisms are related to the morphology dynamics; so the patterns of neuronal morphology changes are analyzed along with other aspects. In this case, the relationship between neuronal activity and morphology dynamics is explored to analyze locomotion procedures. Our tracking method models the morphology dynamics in the calcium image sequence designed for detecting neuronal activity. It follows the local-to-global design to handle calcium imaging issues and neuronal movement characteristics. Lastly, modeling the link between structural and functional development depicts the correlation between neuron growth and protein interactions. This requires the morphology analysis of different imaging modalities. It can be solved using the part-wise volume segmentation with artificial templates, the standardized representation of neurons. Our method follows the global-to-local approach to solve both part-wise segmentation and registration across modalities. Our methods address common issues in automated morphology analysis from extracting morphological features to tracking neurons, as well as mapping neurons across imaging modalities. The quantitative analysis delivered by our techniques enables a number of new applications and visualizations for advancing the investigation of phenomena in the nervous system

Shape Memory Polymers as 2D Substrates and 3D Scaffolds for the Study of Cell Mechanobiology and Tissue Engineering

Tissue engineering is a promising, fast-growing field that combines cells, signals, and scaffolds to regenerate damaged tissues. To develop new, functional, engineered tissues, it is becoming increasingly important to understand how cell-material interactions affect the cell mechanobiological response. As a result, recent efforts have focused on developing complex synthetic materials that can mimic the dynamic in vivo cell environment. In this work, shape memory polymers (SMPs) were employed to develop dynamic 2D substrates and 3D scaffolds that undergo programmed changes in shape under cell compatible conditions. These substrates and scaffolds were applied in vitro and in vivo to demonstrate their potential as platforms to study cell mechanobiology and as functional tissue engineered constructs. The first part of this dissertation describes the fabrication and application of an SMP bilayer system capable of forming nano-scale wrinkles under cytocompatible conditions. Wrinkled substrates with easily tunable characteristics were employed to control the degree of cell alignment, with increased wrinkle amplitude and wrinkle orientation resulting in increased cell alignment until reaching a point of saturation. Active wrinkling with attached and viable cells was found to enable cell alignment to be “turned-on” on command. Additionally, cell migration on wrinkled substrates was assessed using quantitative, statistical-physics-based metrics which revealed cell motility atop anisotropic wrinkled substrates and which was more oriented and persistent than cell motility atop flat isotropic controls The second part of this dissertation describes the fabrication and application of porous 3D SMPs capable of expanding under physiological temperatures. A modified porogen-leaching approached was employed to fabricate highly porous, interconnected SMP scaffolds with tunable properties. The potential of SMP foams for use as synthetic bone substitutes was demonstrated in a mouse segmental defect model, where expanding foams were deployed intraoperatively to fill and conform to a critical size defect. Stiff SMP foams were able to maintain defect stability in a load-bearing application and integrated with the native bone after 12 weeks. Furthermore, deployable SMP foams showed potential for use as deployable cell-based therapies to facilitate bone repair, as expanding foams were able to support osteogenic differentiation of attached stem cells. This work demonstrates the potential of SMPs to be employed as dynamic materials to study cell-material interactions in dynamic environments and to aid in the development of functional tissue engineered constructs