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

Probabilistic Tracking and Behavior Identification of Fluorescent Particles

Explicit and tractable characterizations of the dynamical behavior of virus particles are pivotal for a thorough understanding of the infection mechanisms of viruses. This thesis deals with the problem of extracting symbolic representations of the dynamical behavior of fluorescent particles from fluorescence microscopy image sequences. The focus is on the behavior of virus particles such as fusion with the cell membrane. A numerical representation is obtained by tracking the particles in the image sequences. We have investigated probabilistic tracking approaches, including approaches based on the Kalman filter as well as based on particle filters. For reasons of efficiency and robustness, we developed a tracking approach based on the probabilistic data association (PDA) algorithm in combination with an ellipsoidal sampling scheme that exploits effectively the image data via parametric appearance models. To track objects in close proximity, we compute the support that each image position provides to each tracked object relative to the support provided to the object's neighbors. After tracking, the problem of mapping the trajectory information computed by the tracking approaches to symbolic representations of the behavior arises. To compute symbolic representations of behaviors related to the fusion of single virus particles with the cell membrane based on their intensity over time, we developed a layered probabilistic approach based on stochastic hybrid systems as well as hidden Markov models (HMMs). We use a maxbelief strategy to efficiently combine both representations. The layered approach describes the intensity, intensity models, and behaviors of single virus particles. We introduce models for the evolution of the intensity and the behavior. To compute estimates for the intensity, intensity models, and behaviors we use a hybrid particle filter and the Viterbi algorithm. The developed approaches have been applied to synthetic images as well as to real microscopy image sequences displaying human immunodeficiency virus (HIV-1) particles. We have performed an extensive quantitative evaluation of the performance and a comparison with several existing approaches. It turned out that our approaches outperform previous ones, thus yielding more accurate and more reliable information about the behavior of virus particles. Moreover, we have successfully applied our tracking approaches to 3D image sequences displaying herpes simplex virus (HSV) replication compartments. We also applied the tracking approaches to image data displaying microtubule tips and analyzed their motion. In addition, our tracking approaches were successfully applied to the 2D and 3D image data of a Particle Tracking Challenge