Visualization and Analysis of 3D Microscopic Images

In a wide range of biological studies, it is highly desirable to visualize and analyze three-dimensional (3D) microscopic images. In this primer, we first introduce several major methods for visualizing typical 3D images and related multi-scale, multi-time-point, multi-color data sets. Then, we discuss three key categories of image analysis tasks, namely segmentation, registration, and annotation. We demonstrate how to pipeline these visualization and analysis modules using examples of profiling the single-cell gene-expression of C. elegans and constructing a map of stereotyped neurite tracts in a fruit fly brain

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

TRACING MICROTUBULES IN LIVE CELL IMAGES

Microtubule (MT) dynamics are traditionally analyzed from time lapse images by manual techniques that are laborious, approximate and often limited. Recently, computer vision techniques have been applied to the problem of automated tracking of MTs in live cell images. Aside of very low signal to noise ratios, live cell images of MTs exhibit severe clutter for accurate tracing of MT body. Moreover, intersecting and overlapping MT regions appear brighter due to additive fluorescence. In this paper, we present a MT body tracing algorithm that addresses the clutter without imposing directional constraints. We show that MT dynamics can be quantified with enhanced precision, and novel measurements that are beyond manual feasibility, can be obtained accurately. We demonstrate our results on actual images of MTs obtained by live cell fluorescence microscopy. Index Terms — Biomedical image processing, Image line pattern analysis, Object detectio

Tracing microtubules in live cell images

Microtubule (MT) dynamics are traditionally analyzed from time lapse images by manual techniques that are laborious, approximate and often limited. Recently, computer vision techniques have been applied to the problem of automated tracking of MTs in live cell images. Aside of very low signal to noise ratios, live cell images of MTs exhibit severe clutter for accurate tracing of MT body. Moreover, intersecting and overlapping MT regions appear brighter due to additive fluorescence. In this paper, we present a MT body tracing algorithm that addresses the clutter without imposing directional constraints. We show that MT dynamics can be quantified with enhanced precision, and novel measurements that are beyond manual feasibility, can be obtained accurately. We demonstrate our results on actual images of MTs obtained by live cell fluorescence microscopy