A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly max-Oracle

Structural support vector machines (SSVMs) are amongst the best performing models for structured computer vision tasks, such as semantic image segmentation or human pose estimation. Training SSVMs, however, is computationally costly, because it requires repeated calls to a structured prediction subroutine (called \emph{max-oracle}), which has to solve an optimization problem itself, e.g. a graph cut. In this work, we introduce a new algorithm for SSVM training that is more efficient than earlier techniques when the max-oracle is computationally expensive, as it is frequently the case in computer vision tasks. The main idea is to (i) combine the recent stochastic Block-Coordinate Frank-Wolfe algorithm with efficient hyperplane caching, and (ii) use an automatic selection rule for deciding whether to call the exact max-oracle or to rely on an approximate one based on the cached hyperplanes. We show experimentally that this strategy leads to faster convergence to the optimum with respect to the number of requires oracle calls, and that this translates into faster convergence with respect to the total runtime when the max-oracle is slow compared to the other steps of the algorithm. A publicly available C++ implementation is provided at http://pub.ist.ac.at/~vnk/papers/SVM.html

Characterising live cell behaviour: traditional label-free and quantitative phase imaging approaches

Label-free imaging uses inherent contrast mechanisms within cells to create image contrast without introducing dyes/labels, which may confound results. Quantitative phase imaging is label-free and offers higher content and contrast compared to traditional techniques. High-contrast images facilitate generation of individual cell metrics via more robust segmentation and tracking, enabling formation of a label-free dynamic phenotype describing cell-to-cell heterogeneity and temporal changes. Compared to population-level averages, individual cell-level dynamic phenotypes have greater power to differentiate between cellular responses to treatments, which has clinical relevance e.g. in the treatment of cancer. Furthermore, as the data is obtained label-free, the same cells can be used for further assays or expansion, of potential benefit for the fields of regenerative and personalised medicine

Biomedical Data Analysis with Prior Knowledge : Modeling and Learning

Modern research in biology and medicine is experiencing a data explosion in quantity and particularly in complexity. Efficient and accurate processing of these datasets demands state-of-the-art computational methods such as probabilistic graphical models, graph-based image analysis and many inference/optimization algorithms. However, the underlying complexity of biomedical experiments rules out direct out-of-the-box applications of these methods and requires novel formulation and enhancement to make them amendable to specific problems. This thesis explores novel approaches for incorporating prior knowledge into the data analysis workflow that leads to quantitative and meaningful interpretation of the datasets and also allows for sufficient user involvement. As discussed in Chapter 1, depending on the complexity of the prior knowledge, these approaches can be categorized as constrained modeling and learning. The first part of the thesis focuses on constrained modeling where the prior is normally explicitly represented as additional potential terms in the problem formulation. These terms prevent or discourage the downstream optimization of the formulation from yielding solutions that contradict the prior knowledge. In Chapter 2, we present a robust method for estimating and tracking the deuterium incorporation in the time-resolved hydrogen exchange (HX) mass spectrometry (MS) experiments with priors such as sparsity and sequential ordering. In Chapter 3, we introduce how to extend a classic Markov random field (MRF) model with a shape prior for cell nucleus segmentation. The second part of the thesis explores learning which addresses problems where the prior varies between different datasets or is too difficult to express explicitly. In this case, the prior is first abstracted as a parametric model and then its optimum parametrization is estimated from a training set using machine learning techniques. In Chapter 4, we extend the popular Rand Index in a cost-sensitive fashion and the problem-specific costs can be learned from manual scorings. This set of approaches becomes more interesting when the input/output becomes structured such as matrices or graphs. In Chapter 5, we present structured learning for cell tracking, a novel approach that learns optimum parameters automatically from a training set and allows for the use of a richer set of features which in turn affords improved tracking performance. Finally, conclusions and outlook are provided in Chapter 6

Automated processing of zebrafish imaging data: a survey

Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images