Refine
Year of publication
Document Type
- ZIB-Report (19)
- Doctoral Thesis (1)
Has Fulltext
- yes (20) (remove)
Is part of the Bibliography
- no (20)
Keywords
- model-based inversion (3)
- Monte Carlo (2)
- blood oxygen saturation (2)
- inverse problem (2)
- quantitative photoacoustic imaging (2)
- spectral unmixing (2)
- 2-photon microscopy (1)
- 2D distance map (1)
- Amira (1)
- Beton (1)
The role of titanium surface nanotopography on preosteoblast morphology, adhesion and migration
(2017)
Surface structuring of titanium-based implants with appropriate nanotopographies can significantly modulate their impact on the biological behavior of cells populating these implants. Implant assisted bone tissue repair and regeneration require functional adhesion and expansion of bone progenitors. The surface nanotopography of implant materials used to support bone healing and its effect on cell behavior, in particular cell adhesion, spreading, expansion, and motility, is still not clearly understood. The aim of this study is to investigate preosteoblast proliferation, adhesion, morphology, and migration on different titanium materials with similar surface chemistry, but distinct nanotopographical features. Sonochemical treatment and anodic oxidation were employed to fabricate disordered – mesoporous titania (TMS), and ordered – titania nanotubular (TNT) topographies respectively. The morphological evaluation revealed a surface dependent shape, thickness, and spreading of cells owing to different adherence behavior. Cells were polygonal-shaped and well-spread on glass and TMS, but displayed an elongated fibroblast-like morphology on TNT surfaces. The cells on glass however, were much flatter than on nanostructured surfaces. Both nanostructured surfaces impaired cell adhesion, but TMS was more favorable for cell growth due to its support of cell attachment and spreading in contrast to TNT. Quantitative wound healing assay in combination with live-cell imaging revealed that cells seeded on TMS surfaces migrated in close proximity to neighboring cells and less directed when compared to the migratory behavior on other surfaces. The results indicate distinctly different cell adhesion and migration on ordered and disordered titania nanotopographies, providing important information that could be used in optimizing titanium-based scaffold design to foster bone tissue growth and repair.
This work introduces novel internal and external memory algorithms for computing voxel skeletons of massive voxel objects with complex network-like architecture and for converting these voxel skeletons to piecewise linear geometry, that is triangle meshes and piecewise straight lines. The presented techniques help to tackle the challenge of visualizing and analyzing 3d images of increasing size and complexity, which are becoming more and more important in, for example, biological and medical research. Section 2.3.1 contributes to the theoretical foundations of thinning algorithms with a discussion of homotopic thinning in the grid cell model. The grid cell model explicitly represents a cell complex built of faces, edges, and vertices shared between voxels. A characterization of pairs of cells to be deleted is much simpler than characterizations of simple voxels were before. The grid cell model resolves topologically unclear voxel configurations at junctions and locked voxel configurations causing, for example, interior voxels in sets of non-simple voxels. A general conclusion is that the grid cell model is superior to indecomposable voxels for algorithms that need detailed control of topology. Section 2.3.2 introduces a noise-insensitive measure based on the geodesic distance along the boundary to compute two-dimensional skeletons. The measure is able to retain thin object structures if they are geometrically important while ignoring noise on the object's boundary. This combination of properties is not known of other measures. The measure is also used to guide erosion in a thinning process from the boundary towards lines centered within plate-like structures. Geodesic distance based quantities seem to be well suited to robustly identify one- and two-dimensional skeletons. Chapter 6 applies the method to visualization of bone micro-architecture. Chapter 3 describes a novel geometry generation scheme for representing voxel skeletons, which retracts voxel skeletons to piecewise linear geometry per dual cube. The generated triangle meshes and graphs provide a link to geometry processing and efficient rendering of voxel skeletons. The scheme creates non-closed surfaces with boundaries, which contain fewer triangles than a representation of voxel skeletons using closed surfaces like small cubes or iso-surfaces. A conclusion is that thinking specifically about voxel skeleton configurations instead of generic voxel configurations helps to deal with the topological implications. The geometry generation is one foundation of the applications presented in Chapter 6. Chapter 5 presents a novel external memory algorithm for distance ordered homotopic thinning. The presented method extends known algorithms for computing chamfer distance transformations and thinning to execute I/O-efficiently when input is larger than the available main memory. The applied block-wise decomposition schemes are quite simple. Yet it was necessary to carefully analyze effects of block boundaries to devise globally correct external memory variants of known algorithms. In general, doing so is superior to naive block-wise processing ignoring boundary effects. Chapter 6 applies the algorithms in a novel method based on confocal microscopy for quantitative study of micro-vascular networks in the field of microcirculation.
Following axon pathfinding, growth cones transition from stochastic filopodial exploration to the formation of a limited number of synapses. How the interplay of filopodia and synapse assembly ensures robust connectivity in the brain has remained a challenging problem. Here, we developed a new 4D analysis method for filopodial dynamics and a data-driven computational model of synapse formation for R7 photoreceptor axons in developing Drosophila brains. Our live data support a 'serial synapse formation' model, where at any time point only a single 'synaptogenic' filopodium suppresses the synaptic competence of other filopodia through competition for synaptic seeding factors. Loss of the synaptic seeding factors Syd-1 and Liprin-α leads to a loss of this suppression, filopodial destabilization and reduced synapse formation, which is sufficient to cause the destabilization of entire axon terminals. Our model provides a filopodial 'winner-takes-all' mechanism that ensures the formation of an appropriate number of synapses.
In this paper, we present a software-assisted workflow for the alignment and matching of filamentous structures across a stack of 3D serial image sections. This is achieved by a combination of automatic methods, visual validation, and interactive correction. After an initial alignment, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. This is supported by a quality assessment that visualizes regions that have been already inspected and, thus, allows a trade-off between quality and manual labor.
The software tool was developed in collaboration with biologists who investigate microtubule-based spindles during cell division. To quantitatively understand the structural organization of such spindles, a 3D reconstruction of the numerous microtubules is essential. Each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The sections then need to be stitched, i.e. non-rigidly aligned; and the microtubules need to be traced in each section and connected across section boundaries. Experiments led to the conclusion that automatic methods for stitching alone provide only an incomplete solution to practical analysis needs. Automatic methods may fail due to large physical distortions, a low signal-to-noise ratio of the images, or other unexpected experimental difficulties. In such situations, semi-automatic validation and correction is required to rescue as much information as possible to derive biologically meaningful results despite of some errors related to data collection.
Since the correct stitching is visually not obvious due to the number of microtubules (up to 30k) and their dense spatial arrangement, these are difficult tasks. Furthermore, a naive inspection of each microtubule is too time consuming. In addition, interactive visualization is hampered by the size of the image data (up to 100 GB). Based on the requirements of our collaborators, we present a practical solution for the semi-automatic stitching of serial section image stacks with filamentous structures.
Purpose/Aims of the Study: Bone’s hierarchical structure can be visualized using a variety of methods. Many techniques, such as light and electron microscopy generate two-dimensional (2D) images, while micro computed tomography (μCT) allows a direct representation of the three-dimensional (3D) structure. In addition, different methods provide complementary structural information, such as the arrangement of organic or inorganic compounds. The overall aim of the present study is to answer bone research questions by linking information of different 2D and 3D imaging techniques. A great challenge in combining different methods arises from the fact that they usually reflect different characteristics of the real structure.
Materials and Methods: We investigated bone during healing by means of μCT and a couple of 2D methods. Backscattered electron images were used to qualitatively evaluate the tissue’s calcium content and served as a position map for other experimental data. Nanoindentation and X-ray scattering experiments were performed to visualize mechanical and structural properties. Results: We present an approach for the registration of 2D data in a 3D μCT reference frame, where scanning electron microscopies serve as a methodic link. Backscattered electron images are perfectly suited for registration into μCT reference frames, since both show structures based on the same physical principles. We introduce specific registration tools that have been developed to perform the registration process in a semi-automatic way.
Conclusions: By applying this routine, we were able to exactly locate structural information (e.g. mineral particle properties) in the 3D bone volume. In bone healing studies this will help to better understand basic formation, remodeling and mineralization processes.
Quantitative PA tomography of high resolution 3-D images: experimental validation in tissue phantoms
(2019)
Quantitative photoacoustic tomography aims recover the spatial distribution of absolute chromophore concentrations and their ratios from deep tissue, high-resolution images. In this study, a model-based inversion scheme based on a Monte-Carlo light transport model is experimentally validated on 3-D multispectral images of a tissue phantom acquired using an all-optical scanner with a planar detection geometry. A calibrated absorber allowed scaling of the measured data during the inversion, while an acoustic correction method was employed to compensate the effects of limited view detection. Chromophore- and fluence-dependent step sizes and Adam optimization were implemented to achieve rapid convergence. High resolution 3-D maps of absolute concentrations and their ratios were recovered with high accuracy. Potential applications of this method include quantitative functional and molecular photoacoustic tomography of deep tissue in preclinical and clinical studies.
Mitotic and meiotic spindles are microtubule-based structures to faithfully segregate chromosomes. Electron tomography is currently the method of choice to analyze the three-dimensional architecture of both types of spindles. Over the years, we have developed methods and software for automatic segmentation and stitching of microtubules in serial sections for large-scale reconstructions. Three-dimensional reconstruction of microtubules, however, is only the first step towards biological insight. The second step is the analysis of the structural data to derive measurable spindle properties. Here, we present a comprehensive set of techniques to quantify spindle parameters. These techniques provide quantitative analyses of specific microtubule classes and are applicable to a variety of tomographic reconstructions of spindles from different organisms.
Neural circuit mapping is generating datasets of 10,000s of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches.
We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types including searching neurons against transgene expression patterns. Finally we show that NBLAST is effective with data from other invertebrates and zebrafish.
The goal of quantitative photoacoustic tomography (qPAT) is to recover maps of the chromophore distributions from multiwavelength images of the initial pressure. Model-based inversions that incorporate the physical processes underlying the photoacoustic (PA) signal generation represent a promising approach. Monte-Carlo models of the light transport are computationally expensive, but provide accurate fluence distributions predictions, especially in the ballistic and quasi-ballistic regimes. Here, we focus on the inverse problem of 3D qPAT of blood oxygenation and investigate the application of the Monte-Carlo method in a model-based inversion scheme. A forward model of the light transport based on the MCX simulator and acoustic propagation modeled by the k-Wave toolbox was used to generate a PA image data set acquired in a tissue phantom over a planar detection geometry. The combination of the optical and acoustic models is shown to account for limited-view artifacts. In addition, the errors in the fluence due to, for example, partial volume artifacts and absorbers immediately adjacent to the region of interest are investigated. To accomplish large-scale inversions in 3D, the number of degrees of freedom is reduced by applying image segmentation to the initial pressure distribution to extract a limited number of regions with homogeneous optical parameters. The absorber concentration in the tissue phantom was estimated using a coordinate descent parameter search based on the comparison between measured and modeled PA spectra. The estimated relative concentrations using this approach lie within 5 % compared to the known concentrations. Finally, we discuss the feasibility of this approach to recover the blood oxygenation from experimental data.
We present a level of detail method for trees based on ellipsoids and lines. We leverage the Expectation Maximization algorithm with a Gaussian Mixture Model to create a hierarchy of high-quality leaf clusterings, while the branches are simplified using agglomerative bottom-up clustering to preserve the connectivity. The simplification runs in a preprocessing step and requires no human interaction. For a fly by over and through a scene of 10k trees, our method renders on average at 40 ms/frame, up to 6 times faster than billboard clouds with comparable artifacts.