Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application

Generalizations of the Multicut Problem for Computer Vision

Graph decomposition has always been a very important concept in machine learning and computer vision. Many tasks like image and mesh segmentation, community detection in social networks, as well as object tracking and human pose estimation can be formulated as a graph decomposition problem. The multicut problem in particular is a popular model to optimize for a decomposition of a given graph. Its main advantage is that no prior knowledge about the number of components or their sizes is required. However, it has several limitations, which we address in this thesis: Firstly, the multicut problem allows to specify only cost or reward for putting two direct neighbours into distinct components. This limits the expressibility of the cost function. We introduce special edges into the graph that allow to define cost or reward for putting any two vertices into distinct components, while preserving the original set of feasible solutions. We show that this considerably improves the quality of image and mesh segmentations. Second, multicut is notorious to be NP-hard for general graphs, that limits its applications to small super-pixel graphs. We define and implement two primal feasible heuristics to solve the problem. They do not provide any guarantees on the runtime or quality of solutions, but in practice show good convergence behaviour. We perform an extensive comparison on multiple graphs of different sizes and properties. Third, we extend the multicut framework by introducing node labels, so that we can jointly optimize for graph decomposition and nodes classification by means of exactly the same optimization algorithm, thus eliminating the need to hand-tune optimizers for a particular task. To prove its universality we applied it to diverse computer vision tasks, including human pose estimation, multiple object tracking, and instance-aware semantic segmentation. We show that we can improve the results over the prior art using exactly the same data as in the original works. Finally, we use employ multicuts in two applications: 1) a client-server tool for interactive video segmentation: After the pre-processing of the video a user draws strokes on several frames and a time-coherent segmentation of the entire video is performed on-the-fly. 2) we formulate a method for simultaneous segmentation and tracking of living cells in microscopy data. This task is challenging as cells split and our algorithm accounts for this, creating parental hierarchies. We also present results on multiple model fitting. We find models in data heavily corrupted by noise by finding components defining these models using higher order multicuts. We introduce an interesting extension that allows our optimization to pick better hyperparameters for each discovered model. In summary, this thesis extends the multicut problem in different directions, proposes algorithms for optimization, and applies it to novel data and settings.Die Zerlegung von Graphen ist ein sehr wichtiges Konzept im maschinellen Lernen und maschinellen Sehen. Viele Aufgaben wie Bild- und Gittersegmentierung, Kommunitätserkennung in sozialen Netzwerken, sowie Objektverfolgung und Schätzung von menschlichen Posen können als Graphzerlegungsproblem formuliert werden. Der Mehrfachschnitt-Ansatz ist ein populäres Mittel um über die Zerlegungen eines gegebenen Graphen zu optimieren. Sein größter Vorteil ist, dass kein Vorwissen über die Anzahl an Komponenten und deren Größen benötigt wird. Dennoch hat er mehrere ernsthafte Limitierungen, welche wir in dieser Arbeit behandeln: Erstens erlaubt der klassische Mehrfachschnitt nur die Spezifikation von Kosten oder Belohnungen für die Trennung von zwei Nachbarn in verschiedene Komponenten. Dies schränkt die Ausdrucksfähigkeit der Kostenfunktion ein und führt zu suboptimalen Ergebnissen. Wir fügen dem Graphen spezielle Kanten hinzu, welche es erlauben, Kosten oder Belohnungen für die Trennung von beliebigen Paaren von Knoten in verschiedene Komponenten zu definieren, ohne die Menge an zulässigen Lösungen zu verändern. Wir zeigen, dass dies die Qualität von Bild- und Gittersegmentierungen deutlich verbessert. Zweitens ist das Mehrfachschnittproblem berüchtigt dafür NP-schwer für allgemeine Graphen zu sein, was die Anwendungen auf kleine superpixel-basierte Graphen einschränkt. Wir definieren und implementieren zwei primal-zulässige Heuristiken um das Problem zu lösen. Diese geben keine Garantien bezüglich der Laufzeit oder der Qualität der Lösungen, zeigen in der Praxis jedoch gutes Konvergenzverhalten. Wir führen einen ausführlichen Vergleich auf vielen Graphen verschiedener Größen und Eigenschaften durch. Drittens erweitern wir den Mehrfachschnitt-Ansatz um Knoten-Kennzeichnungen, sodass wir gemeinsam über Zerlegungen und Knoten-Klassifikationen mit dem gleichen Optimierungs-Algorithmus optimieren können. Dadurch wird der Bedarf der Feinabstimmung einzelner aufgabenspezifischer Löser aus dem Weg geräumt. Um die Allgemeingültigkeit dieses Ansatzes zu überprüfen, haben wir ihn auf verschiedenen Aufgaben des maschinellen Sehens, einschließlich menschliche Posenschätzung, Mehrobjektverfolgung und instanz-bewusste semantische Segmentierung, angewandt. Wir zeigen, dass wir Resultate von vorherigen Arbeiten mit exakt den gleichen Daten verbessern können. Abschließend benutzen wir Mehrfachschnitte in zwei Anwendungen: 1) Ein Nutzer-Server-Werkzeug für interaktive Video Segmentierung: Nach der Vorbearbeitung eines Videos zeichnet der Nutzer Striche auf mehrere Einzelbilder und eine zeit-kohärente Segmentierung des gesamten Videos wird in Echtzeit berechnet. 2) Wir formulieren eine Methode für simultane Segmentierung und Verfolgung von lebenden Zellen in Mikroskopie-Aufnahmen. Diese Aufgabe ist anspruchsvoll, da Zellen sich aufteilen und unser Algorithmus dies in der Erstellung von Eltern-Hierarchien mitberücksichtigen muss. Wir präsentieren außerdem Resultate zur Mehrmodellanpassung. Wir berechnen Modelle in stark verrauschten Daten indem wir mithilfe von Mehrfachschnitten höherer Ordnung Komponenten finden, die diesen Modellen entsprechen. Wir führen eine interessante Erweiterung ein, die es unserer Optimierung erlaubt, bessere Hyperparameter für jedes entdeckte Modell auszuwählen. Zusammenfassend erweitert diese Arbeit den Mehrfachschnitt-Ansatz in unterschiedlichen Richtungen, schlägt Algorithmen zur Inferenz in den resultierenden Modellen vor und wendet ihn auf neuartigen Daten und Umgebungen an

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 datasets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present thesis 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.Comment: 218 pages, 58 figures, PhD thesis, Department of Mechanical Engineering, Karlsruhe Institute of Technology, published online with KITopen (License: CC BY-SA 3.0, http://dx.doi.org/10.5445/IR/1000057821

Super-resolution microscopy live cell imaging and image analysis

Novel fundamental research results provided new techniques going beyond the diffraction limit. These recent advances known as super-resolution microscopy have been awarded by the Nobel Prize as they promise new discoveries in biology and live sciences. All these techniques rely on complex signal and image processing. The applicability in biology, and particularly for live cell imaging, remains challenging and needs further investigation. Focusing on image processing and analysis, the thesis is devoted to a significant enhancement of structured illumination microscopy (SIM) and super-resolution optical fluctuation imaging (SOFI)methods towards fast live cell and quantitative imaging. The thesis presents a novel image reconstruction method for both 2D and 3D SIM data, compatible with weak signals, and robust towards unwanted image artifacts. This image reconstruction is efficient under low light conditions, reduces phototoxicity and facilitates live cell observations. We demonstrate the performance of our new method by imaging long super-resolution video sequences of live U2-OS cells and improving cell particle tracking. We develop an adapted 3D deconvolution algorithm for SOFI, which suppresses noise and makes 3D SOFI live cell imaging feasible due to reduction of the number of required input images. We introduce a novel linearization procedure for SOFI maximizing the resolution gain and show that SOFI and PALM can both be applied on the same dataset revealing more insights about the sample. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of the sample through the estimation of molecular parameters. For quantifying the outcome of our super-resolutionmethods, the thesis presents a novel methodology for objective image quality assessment measuring spatial resolution and signal to noise ratio in real samples. We demonstrate our enhanced SOFI framework by high throughput 3D imaging of live HeLa cells acquiring the whole super-resolution 3D image in 0.95 s, by investigating focal adhesions in live MEF cells, by fast optical readout of fluorescently labelled DNA strands and by unraveling the nanoscale organization of CD4 proteins on a plasma membrane of T-cells. Within the thesis, unique open-source software packages SIMToolbox and SOFI simulation tool were developed to facilitate implementation of super-resolution microscopy methods

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

Feature-preserving image restoration and its application in biological fluorescence microscopy

This thesis presents a new investigation of image restoration and its application to fluorescence cell microscopy. The first part of the work is to develop advanced image denoising algorithms to restore images from noisy observations by using a novel featurepreserving diffusion approach. I have applied these algorithms to different types of images, including biometric, biological and natural images, and demonstrated their superior performance for noise removal and feature preservation, compared to several state of the art methods. In the second part of my work, I explore a novel, simple and inexpensive super-resolution restoration method for quantitative microscopy in cell biology. In this method, a super-resolution image is restored, through an inverse process, by using multiple diffraction-limited (low) resolution observations, which are acquired from conventional microscopes whilst translating the sample parallel to the image plane, so referred to as translation microscopy (TRAM). A key to this new development is the integration of a robust feature detector, developed in the first part, to the inverse process to restore high resolution images well above the diffraction limit in the presence of strong noise. TRAM is a post-image acquisition computational method and can be implemented with any microscope. Experiments show a nearly 7-fold increase in lateral spatial resolution in noisy biological environments, delivering multi-colour image resolution of ~30 nm