Model-based automated extraction of microtubules from electron tomography volume

Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis, and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors, such as poor contrast, inhomogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2-D/3-D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on eight publicly available datasets (six 2-D data sets, one 3-D data set, and one 3-D synthetic data set) demonstrate its superior performance to other state-of-the-art methods.

3D reconstruction of elastin fibres in coronary adventitia

A 3D reconstruction of individual fibres in vascular tissue is necessary to understand the microstructure properties of the vessel wall.  The objective of this study is to determine the 3D microstructure of elastin fibres in the adventitia of coronary arteries.  Quantification of fibre geometry is challenging due to the complex interwoven structure of the fibres.  In particular, accurate linking of gaps remains a significant challenge, and complex features such as long gaps and interwoven fibres have not been adequately addressed by current fibre reconstruction algorithms.  We use a novel line Laplacian deformation method, which better deals with fibre shape uncertainty to reconstruct elastin fibres in the coronary adventitia of five swine. A cost function, based on entropy and Euler Spiral, was used in the shortest path search. We find that mean diameter of elastin fibres is 1.67 ± 1.42 μm and fibre orientation is clustered around two major angles of 8.9˚ and 81.8˚.  Comparing with CT-FIRE, we find that our method gives more accurate estimation of fibre width.  To our knowledge, the measurements obtained using our algorithm represent the first investigation focused on the reconstruction of full elastin fibre length.  Our data provide a foundation for a 3D microstructural model of the coronary adventitia to elucidate the structure-function relationship of elastin fibres.

Enhancement of Vascular Structures in 3D and 2D Angiographic Images

A number of imaging techniques are being used for diagnosis and treatment of vascular pathologies like stenoses, aneurysms, embolisms, malformations and remodelings, which may affect a wide range of anatomical sites. For computer-aided detection and highlighting of potential sites of pathology or to improve visualization and segmentation, angiographic images are often enhanced by Hessian based filters. These filters aim to indicate elongated and/or rounded structures by an enhancement function based on Hessian eigenvalues. However, established enhancement functions generally produce a response, which exhibits deficiencies such as poor and non-uniform response for vessels of different sizes and varying contrast, at bifurcations and aneurysms. This may compromise subsequent analysis of the enhanced images. This paper has three important contributions: i) reviews several established enhancement functions and elaborates their deficiencies, ii) proposes a novel enhancement function, which overcomes the deficiencies of the established functions, and iii) quantitatively evaluates and compares the novel and the established enhancement functions on clinical image datasets of the lung, cerebral and fundus vasculatures.

Quantifying nerve architecture in murine and human airways using three-dimensional computational mapping

The quantitative histological analysis of airway innervation using tissue sections is challenging because of the sparse and patchy distribution of nerves. Here we demonstrate a method using a computational approach to measure airway nerve architecture that will allow for more complete nerve quantification and the measurement of structural peripheral neuroplasticity in lung development and disease. We demonstrate how our computer analysis outperforms manual scoring in quantifying three-dimensional nerve branchpoints and lengths. In murine lungs, we detected airway epithelial nerves that have not been previously identified because of their patchy distribution, and we quantified their three-dimensional morphology using our computer mapping approach. Furthermore, we show the utility of this approach in bronchoscopic forceps biopsies of human airways, as well as the esophagus, colon, and skin.

Automatic Segmentation and Quantification of Filamentous Structures in Electron Tomography

Electron tomography is a promising technology for imaging ultrastructures at nanoscale resolutions. However, image and quantitative analyses are often hindered by high levels of noise, staining heterogeneity, and material damage either as a result of the electron beam or sample preparation. We have developed and built a framework that allows for automatic segmentation and quantification of filamentous objects in 3D electron tomography. Our approach consists of three steps: (i) local enhancement of filaments by Hessian filtering; (ii) detection and completion (e.g., gap filling) of filamentous structures through tensor voting; and (iii) delineation of the filamentous networks. Our approach allows for quantification of filamentous networks in terms of their compositional and morphological features. We first validate our approach using a set of specifically designed synthetic data. We then apply our segmentation framework to tomograms of plant cell walls that have undergone different chemical treatments for polysaccharide extraction. The subsequent compositional and morphological analyses of the plant cell walls reveal their organizational characteristics and the effects of the different chemical protocols on specific polysaccharides.

Actin filament tracking in electron tomograms of negatively stained lamellipodia using the localized radon transform

The aim of this work was to develop a protocol for automated tracking of actin filaments in electron tomograms of lamellipodia embedded in negative stain. We show that a localized version of the Radon transform for the detection of filament directions enables three-dimensional visualizations of filament network architecture, facilitating extraction of statistical information including orientation profiles. We discuss the requirements for parameter selection set by the raw image data in the context of other, similar tracking protocols.

Segmentation and tracking of cytoskeletal filaments using open active contours

We use open active contours to quantify cytoskeletal structures imaged by fluorescence microscopy in two and three dimensions. We developed an interactive software tool for segmentation, tracking, and visualization of individual fibers. Open active contours are parametric curves that deform to minimize the sum of an external energy derived from the image and an internal bending and stretching energy. The external energy generates (i) forces that attract the contour toward the central bright line of a filament in the image, and (ii) forces that stretch the active contour toward the ends of bright ridges. Images of simulated semiflexible polymers with known bending and torsional rigidity are analyzed to validate the method. We apply our methods to quantify the conformations and dynamics of actin in two examples: actin filaments imaged by TIRF microscopy in vitro, and actin cables in fission yeast imaged by spinning disk confocal microscopy.

Automated identification of microtubules in cellular electron tomography

We describe a method for automatically finding the location and conformations of microtubules in tomograms of high-pressure frozen, freeze substituted cells. Our approach uses two steps: a preprocessing step that finds locations in the tomograms that are likely to lie inside microtubules and a tracking step that uses the preprocessed data to identify the trajectories of individual microtubules. We test this method on a reconstruction of a Caenorhabditis elegans mitotic spindle and we compare our results with those obtained by a human expert who manually segmented the same data. At present, the method could be used to assist the analysis of large-scale tomography reconstructions. With further improvements, it may be possible to reliably segment cellular tomograms without human intervention.

Methods for segmentation and interpretation of electron tomographic reconstructions

Electron tomography has become a powerful tool for revealing the molecular architecture of biological cells and tissues. In principle, electron tomography can provide high-resolution mapping of entire proteomes. The achievable resolution (3-8 nm) is capable of bridging the gap between live-cell imaging and atomic resolution structures. However, the relevant information is not readily accessible from the data and needs to be identified, extracted, and processed before it can be used. Because electron tomography imaging and image acquisition technologies have enjoyed major advances in the last few years and continue to increase data throughput, the need for approaches that allow automatic and objective interpretation of electron tomograms becomes more and more urgent. This chapter provides an overview of the state of the art in this field and attempts to identify the major bottlenecks that prevent approaches for interpreting electron tomography data to develop their full potential.