Probabilistic Models for Joint Segmentation, Detection and Tracking

Migrace buněk a buněčných částic hraje důležitou roli ve fungování živých organismů. Systematický výzkum buněčné migrace byl umožněn v posledních dvaceti letech rychlým rozvojem neinvazivních zobrazovacích technik a digitálních snímačů. Moderní zobrazovací systémy dovolují studovat chování buněčných populací složených z mnoha ticíců buněk. Manuální analýza takového množství dat by byla velice zdlouhavá, protože některé experimenty vyžadují analyzovat tvar, rychlost a další charakteristiky jednotlivých buněk. Z tohoto důvodu je ve vědecké komunitě velká poptávka po automatických metodách.Migration of cells and subcellular particles plays a crucial role in many processes in living organisms. Despite its importance a systematic research of cell motility has only been possible in last two decades due to rapid development of non-invasive imaging techniques and digital cameras. Modern imaging systems allow to study large populations with thousands of cells. Manual analysis of the acquired data is infeasible, because in order to gain insight into underlying biochemical processes it is sometimes necessary to determine shape, velocity and other characteristics of individual cells. Thus there is a high demand for automatic methods

Methods for Analysing Endothelial Cell Shape and Behaviour in Relation to the Focal Nature of Atherosclerosis

The aim of this thesis is to develop automated methods for the analysis of the spatial patterns, and the functional behaviour of endothelial cells, viewed under microscopy, with applications to the understanding of atherosclerosis. Initially, a radial search approach to segmentation was attempted in order to trace the cell and nuclei boundaries using a maximum likelihood algorithm; it was found inadequate to detect the weak cell boundaries present in the available data. A parametric cell shape model was then introduced to fit an equivalent ellipse to the cell boundary by matching phase-invariant orientation fields of the image and a candidate cell shape. This approach succeeded on good quality images, but failed on images with weak cell boundaries. Finally, a support vector machines based method, relying on a rich set of visual features, and a small but high quality training dataset, was found to work well on large numbers of cells even in the presence of strong intensity variations and imaging noise. Using the segmentation results, several standard shear-stress dependent parameters of cell morphology were studied, and evidence for similar behaviour in some cell shape parameters was obtained in in-vivo cells and their nuclei. Nuclear and cell orientations around immature and mature aortas were broadly similar, suggesting that the pattern of flow direction near the wall stayed approximately constant with age. The relation was less strong for the cell and nuclear length-to-width ratios. Two novel shape analysis approaches were attempted to find other properties of cell shape which could be used to annotate or characterise patterns, since a wide variability in cell and nuclear shapes was observed which did not appear to fit the standard parameterisations. Although no firm conclusions can yet be drawn, the work lays the foundation for future studies of cell morphology. To draw inferences about patterns in the functional response of cells to flow, which may play a role in the progression of disease, single-cell analysis was performed using calcium sensitive florescence probes. Calcium transient rates were found to change with flow, but more importantly, local patterns of synchronisation in multi-cellular groups were discernable and appear to change with flow. The patterns suggest a new functional mechanism in flow-mediation of cell-cell calcium signalling

Learned versus Hand-Designed Feature Representations for 3d Agglomeration

For image recognition and labeling tasks, recent results suggest that machine learning methods that rely on manually specified feature representations may be outperformed by methods that automatically derive feature representations based on the data. Yet for problems that involve analysis of 3d objects, such as mesh segmentation, shape retrieval, or neuron fragment agglomeration, there remains a strong reliance on hand-designed feature descriptors. In this paper, we evaluate a large set of hand-designed 3d feature descriptors alongside features learned from the raw data using both end-to-end and unsupervised learning techniques, in the context of agglomeration of 3d neuron fragments. By combining unsupervised learning techniques with a novel dynamic pooling scheme, we show how pure learning-based methods are for the first time competitive with hand-designed 3d shape descriptors. We investigate data augmentation strategies for dramatically increasing the size of the training set, and show how combining both learned and hand-designed features leads to the highest accuracy

BLINKING IN QUANTUM DOTS AND ENDOTHELIAL CELLS UNDER CURVATURE AND SHEAR STRESS

Two research topics at the interface of physics, materials science, and biology are presented in this dissertation, (1) blinking in quantum dots, and (2) endothelial cells under curvature and shear stress. Quantum dot (QD) blinking is characterized by switching between an “on” and an “off” state, and power-law distributions of on and off times with exponents from 1.0 to 2.0. The origin of blinking behavior in QDs, however, has remained a mystery. We report an energy-band model for QDs that captures the full range of blinking behavior reported in the literature and provides new insight into features such as the gray state, power-law distributions of on and off times, and the power-law exponents. The highly specialized endothelial cells in brain capillaries are a key component of the blood-brain barrier, forming a network of tight junctions that almost completely block paracellular transport. In contrast to vascular endothelial cells in other organs, we show that brain microvascular endothelial cells resist elongation in response to curvature and shear stress. Since the tight junction network is defined by endothelial cell morphology, these results suggest that there may be an evolutionary advantage to resisting elongation by minimizing the total length of cell-cell junctions per unit length of vessel

Jamming and Unjamming in Cancer Cells

Jamming' ist ein faszinierender, nicht vollständig verstandener Prozess in der Physik der weichen Materie. Zelluläres Jamming' tritt auch in biologischem Gewebe auf und muss sich im Fall von Krebszellen im Tumorgewebe aufgrund der dichten Packung der Zellen über der dichtesten Kugelpackung anders verhalten als die bekannten 'Jamming' Systeme. In meiner Dissertation skizziere ich wesentliche Ergebnisse zum Verständnis dieses neuen physikalischen Phänomens. Meine Erkenntnisse tragen dazu bei die Dichotomie zwischen den Theorien der dichteinduzierten und der forminduzierten 'Zelljamming' aufzulösen. Die gewonnenen Erkenntnisse weisen auf die Möglichkeit hin Krebszellformen und deren Zellkernformen als Tumormarker für die Metastasierung zu verwenden. Ich fand ein kritisches Skalierungsverhalten für die Dynamik der Neuanordnung von Zellen in der Nähe des Jamming-Übergangs, abhängig von der Zellform der Nachbarschaft. Dies ist der bisher stärkste Beweis dafür, dass die Zellformen als Kontrollparameter für das 'Zelljamming' fungieren können. Die Zellanzahldichte beeinflusst ebenfalls das 'Jamming', ihr Einfluss kann jedoch als eine Verlangsamung der Eigengeschwindigkeit der Zellen beschrieben werden. Eine hohe Zellanzahldichte allein würde also nur die Viskosität des Gewebes erhöhen und es nicht verfestigen. Darüber hinaus habe ich gezeigt, dass es in dicht gepackten dreidimensionalen Zellsphäroiden sowie in Primärtumorstücken einen mit der Zellform verbundenen 'Jamming'-Übergang gibt. Ich verbinde das 'Unjamming' von Zellen mit dem Fortschreiten des Krebses, indem ich zeigte, dass die Herunterregulierung des Adhäsionsmoleküls E-Cadherin, die ein typischer Schritt während der Krebentwicklung ist, einen 'Unjamming'-Übergang verursacht. Bei diesem 'Unjamming'-Übergang kommt es zu einem ausgeprägten Verlust der Kohäsion und einem reduzierten Volumenanteil der Zellen, was zeigt, dass das 'Zelljamming' einen hohen Volumenanteil erfordert

Cerebellar Structure Segmentation and Shape Analysis with Application to Cerebellar Ataxia

The cerebellum plays an important role in motor control and cognitive functions. Cerebellar dysfunction can lead to a wide range of movement disorders. Despite the significant impact on the lives of patients, the current standard of diagnosis, prognosis, and treatment for cerebellar disease is limited. Magnetic resonance (MR) imaging based morphometric analysis of the cerebellum, which studies the brain structural pattern associated with disease and functional decline, is of great interest and importance. It sets the stage for developing disease-modifying therapies, monitoring individual patient progress, and designing efficient therapeutic trials. Compared to the cerebrum, morphometric analysis in the cerebellum has been limited. Automated and accurate volumetric analysis techniques are lacking. Methods using MR based morphometric biomarkers to predict disease type and functional decline have been lacking or inconclusive. The work presented in this thesis is motivated by the need for better cerebellar structure segmentation and effective structure-function correlation and prediction methods in cerebellar disease. The thesis makes four major contributions. First, we proposed an automated method for segmenting cerebellar lobules from MR images. The proposed method achieved better performance than two state-of-the-art segmentation methods when validated on a cohort of 15 subjects including both healthy controls and patients with various degrees of cerebellar atrophy. Second, we presented two highly-informative shape representations to characterize cerebellar structures: a landmark shape representation of the collection of cerebellar lobules and a level set based whole cerebellar shape representation. Third, we developed an analysis pipeline to classify healthy controls and different ataxia types and to visualize disease specific cerebellar atrophy patterns based on the proposed shape representations and high-dimensional pattern classification methods. The classification performance is evaluated on a cohort consisting of healthy controls and different cerebellar ataxia types. The visualized cerebellar atrophy patterns are consistent with the regional volume decreases observed in previous studies in cerebellar ataxia. Compared to existing analysis methods, the proposed method provides intuitive and detailed visualization of the differences of overall size and shape of the cerebellum, as well as that of individual lobules. Fourth and the last, we developed and tested a similar analysis pipeline for functional score prediction and function specific cerebellar atrophy pattern visualization

Assessment of Ca2+ Dynamics in Human Retinal Pigment Epithelial Cell Cultures

Retinal pigment epithelium is a monolayer of cells located beneath photoreceptors of the retina maintaining their functionality. Malfunction of RPE leads to retinal degenerative diseases, such as age-related macular degeneration and Stargardt disease. Ca2+ is a ubiquitous ion that takes part in regulation of vital cellular processes. The knowledge of Ca2+ dynamics is essential for understanding RPE physiology. This is especially important for functionality assessment of cells intended for transplantation and for drug testing. The aim of this thesis was to study spontaneous and mechanically induced Ca2+ activity in human RPE and to assess the effect of cellular maturation and wounding on the [Ca2+]i dynamics. For this, various methods, such as fluorescent Ca2+ imaging, immunofluorescence staining, PCR, and mathematical modeling were applied. In addition, novel methods were developed to analyze large amounts of Ca2+ imaging data. ARPE-19 and human embryonic stem cell-derived RPE cells (hESC-RPE) were used as RPE cell models. In this thesis, it was shown that both ARPE-19 and hESC-RPE exhibit intercellular Ca2+ waves upon mechanical stimulation. With live-cell Ca2+ imaging and mathematical modeling, it was demonstrated that in ARPE-19 cells, the mechanically induced Ca2+ waves propagate intracellularly through gap junctions and extracellularly involving diffusion of a paracrine factor. By applying in-house image analysis tools for the experimental fluorescence time-series, it was found that in hESC-RPE cells, spontaneous [Ca2+]i transients and the ability to propagate intercellular Ca2+ waves upon mechanical stimulation strongly depend on the maturation status of the cells. Finally, it was demonstrated that wounding affects spontaneous Ca2+ activity close to the wound edges, and cells within the healed areas resemble Ca2+ dynamics of immature hESC-RPE. To conclude, this thesis has provided important insights into human RPE Ca2+ dynamics, as well as into the events of single cell mechanical stimulation and large scale monolayer wounding. In addition, it was demonstrated that maturation drastically affects RPE Ca2+ dynamics. This knowledge and the developed image analysis algorithms contribute to understanding RPE physiology and can facilitate establishment of novel tools for assessment of RPE functionality prior to transplantation and in drug testing assays

Imaging studies of peripheral nerve regeneration induced by porous collagen biomaterials

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references.There is urgent need to develop treatments for inducing regeneration in injured organs. Porous collagen-based scaffolds have been utilized clinically to induce regeneration in skin and peripheral nerves, however still there is no complete explanation about the underlying mechanism. This thesis utilizes advanced microscopy to study the expression of contractile cell phenotypes during wound healing, a phenotype believed to affect significantly the final outcome. The first part develops an efficient pipeline for processing challenging spectral fluorescence microscopy images. Images are segmented into regions of objects by refining the outcome of a pixel-wide model selection classifier by an efficient Markov Random Field model. The methods of this part are utilized by the following parts. The second part extends the image informatics methodology in studying signal transduction networks in cells interacting with 3D matrices. The methodology is applied in a pilot study of TGFP signal transduction by the SMAD pathway in fibroblasts seeded in porous collagen scaffolds. Preliminary analysis suggests that the differential effect of TGFP1 and TGFP3 to cells could be attributed to the "non-canonical" SMADI and SMAD5. The third part is an ex vivo imaging study of peripheral nerve regeneration, which focuses on the formation of a capsule of contractile cells around transected rat sciatic nerves grafted with collagen scaffolds, 1 or 2 weeks post-injury. It follows a recent study that highlights an inverse relationship between the quality of the newly formed nerve tissue and the size of the contractile cell capsule 9 weeks post-injury. Results suggest that "active" biomaterials result in significantly thinner capsule already 1 week post-injury. The fourth part describes a novel method for quantifying the surface chemistry of 3D matrices. The method is an in situ binding assay that utilizes fluorescently labeled recombinant proteins that emulate the receptor of , and is applied to quantify the density of ligands for integrins a113, a2p1 on the surface of porous collagen scaffolds. Results provide estimates for the density of ligands on "active" and "inactive" scaffolds and demonstrate that chemical crosslinking can affect the surface chemistry of biomaterials, therefore can affect the way cells sense and respond to the material.by Dimitrios S. Tzeranis.Ph. D