Image Analysis for the Life Sciences - Computer-assisted Tumor Diagnostics and Digital Embryomics

Current research in the life sciences involves the analysis of such a huge amount of image data that automatization is required. This thesis presents several ways how pattern recognition techniques may contribute to improved tumor diagnostics and to the elucidation of vertebrate embryonic development. Chapter 1 studies an approach for exploiting spatial context for the improved estimation of metabolite concentrations from magnetic resonance spectroscopy imaging (MRSI) data with the aim of more robust tumor detection, and compares against a novel alternative. Chapter 2 describes a software library for training, testing and validating classification algorithms that estimate tumor probability based on MRSI. It allows flexible adaptation towards changed experimental conditions, classifier comparison and quality control without need for expertise in pattern recognition. Chapter 3 studies several models for learning tumor classifiers that allow for the common unreliability of human segmentations. For the first time, models are used for this task that additionally employ the objective image information. Chapter 4 encompasses two contributions to an image analysis pipeline for automatically reconstructing zebrafish embryonic development based on time-resolved microscopy: Two approaches for nucleus segmentation are experimentally compared, and a procedure for tracking nuclei over time is presented and evaluated

Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking

This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are structured objects characterized with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport players, convoy of cars) or of hundreds or thousands of components such as crowds of people. The group object tracking is closely linked with extended object tracking but at the same time has particular features which differentiate it from extended objects. Extended objects, such as in maritime surveillance, are characterized by their kinematic states and their size or volume. Both group and extended objects give rise to a varying number of measurements and require trajectory maintenance. An emphasis is given here to sequential Monte Carlo (SMC) methods and their variants. Methods for small groups and for large groups are presented, including Markov Chain Monte Carlo (MCMC) methods, the random matrices approach and Random Finite Set Statistics methods. Efficient real-time implementations are discussed which are able to deal with the high dimensionality and provide high accuracy. Future trends and avenues are traced. © 2013 Elsevier Inc. All rights reserved

Single-molecule tracking and its application in biomolecular binding detection

In the past two decades significant advances have been made in single-molecule detection, which enables the direct observation of single biomolecules at work in real time under physiological conditions. In particular, the development of single-molecule tracking (SMT) microscopy allows us to monitor the motion paths of individual biomolecules in living systems, unveiling the localization dynamics and transport modalities of the biomolecules that support the development of life. While 3D-SMT is probably the most suitable method for determining whether tracked molecules (can be any biomolecule such as DNA, membrane receptors, and transcription factors) form dimers or complexes with other molecules, great technical challenges remain to be overcome before the potential of 3D-SMT in biomolecular binding detection can be realized. This dissertation describes my work on recent methodology development to overcome these challenges, and new applications of the 3D-SMT technology in rare molecular species quantification. First, we provide an overview of current SMT technologies, with an emphasis on three-dimensional feedback controlled SMT. Advantages and drawbacks of each SMT method are outlined. Second, we describe the theoretical modeling and instrumentation of our confocal tracking microscope. Its multi-dimensional sensing capability (3D position, diffusion coefficient, fluorescence lifetime) is experimentally characterized. In order to maximize the tracking duration, we have also developed strategies to effectively slow-down fast diffusing molecule, and optimized the buffer conditions. Third, we show that our 3D-SMT microscope can detect biomolecular association/disassociated by two types of contrast mechanisms: diffusion rate and lifetime FRET signal. DNA transient binding is used as a model system because of ease of fluorescent labeling and tunable binding kinetics. Both of the two mechanisms involve tracking a fluorescent-labeled single-stranded DNA (ssDNA), but the second approach also requires its complementary strand to be labeled by a dark quencher. A combined analysis of multiple single-molecule trajectories allow us to measure the kinetics that is even beyond the physical bandwidth of the tracking system. In the end, we introduce the application of SMT in rare single-molecule species quantification. The theory for predicting the sensitivity and fidelity is established. Our work highlights the fundamental limitations that we are facing in precise single-molecule identification and quantification without amplification.Biomedical Engineerin

Biological image analysis

In biological research images are extensively used to monitor growth, dynamics and changes in biological specimen, such as cells or plants. Many of these images are used solely for observation or are manually annotated by an expert. In this dissertation we discuss several methods to automate the annotating and analysis of bio-images. Two large clusters of methods have been investigated and developed. A first set of methods focuses on the automatic delineation of relevant objects in bio-images, such as individual cells in microscopic images. Since these methods should be useful for many different applications, e.g. to detect and delineate different objects (cells, plants, leafs, ...) in different types of images (different types of microscopes, regular colour photographs, ...), the methods should be easy to adjust. Therefore we developed a methodology relying on probability theory, where all required parameters can easily be estimated by a biologist, without requiring any knowledge on the techniques used in the actual software. A second cluster of investigated techniques focuses on the analysis of shapes. By defining new features that describe shapes, we are able to automatically classify shapes, retrieve similar shapes from a database and even analyse how an object deforms through time