Browsing by Subject "Membrane"

Now showing 1 - 4 of 4
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    A differential structure approach to membrane segmentation in electron tomography
    (2011-09) Martinez-Sanchez, Antonio; Inmaculada, García; Fernández, Jose-Jesús; Ingeniería de la Información y las Comunicaciones
    Electron tomography allows three-dimensional visualization of cellular landscapes in molecular detail. Segmentation is a paramount stage for the interpretation of the reconstructed tomograms. Although several computational approaches have been proposed, none has prevailed as a generic method and thus segmentation through manual annotation is still a common choice. In this work we introduce a segmentation method targeted at membranes, which define the natural limits of compartments within biological specimens. Our method is based on local differential structure and on a Gaussian-like membrane model. First, it isolates information through scale-space and finds potential membrane-like points at a local scale. Then, the structural information is integrated at a global scale to yield the definite segmentation. We show and validate the performance of the algorithm on a number of tomograms under different experimental conditions.
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    A ridge-based framework for segmentation of 3D electron microscopy datasets
    (Elsevier, 2013-01) Martinez-Sanchez, Antonio; García, Inmaculada; Fernández, Jose-Jesús; Ingeniería de la Información y las Comunicaciones
    Three-dimensional (3D) electron microscopy (EM) has become a major player in structural cell biology as it enables the analysis of subcellular architecture at an unprecedented level of detail. Interpretation of the resulting 3D volumes strongly depends on segmentation, which consists in decomposing the volume into their structural components. The computational approaches proposed so far have not turned out to be of general applicability. Thus, manual segmentation still remains a prevalent method. Here, a new computational framework for segmentation of 3D EM datasets is introduced. It relies on detection and characterization of ridges (i.e. local maxima). The detected ridges are modelled as asymmetric Gaussian functions whose parameters constitute ridge descriptors. This local information is then used to cluster the ridges, which leads to the ultimate segmentation. In this work we focus on membranes and locally planar structures in general. The performance of the framework is illustrated with its application to a number of complex 3D datasets and a quantitative analysis.
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    Linkage between cell membrane proteins and actin-based cytoskeleton the cytoskeletal-driven cellular functions
    (Murcia : F. Hernández, 2000) Fais, S.; Luciani, F.; Logoui, M.; Parlato, S.; Lozupone, F.
    Asymmetric organization of the plasma membrane and cytosolic organelles is fundamental for a variety of cells, including bacteria, yeast and eukaryotic cells (Nelson, 1992). The degree into which cells polarize is characterized by their ability to create and maintain morphologically and biochemically distinct plasma membrane domains. The generation and maintenance of polarized distribution of membrane components (proteins and lipids) is thus critical to the ability of cells to perform complex activities such as cell-to-cell interactions, vectorial transport and secretion, cellular immunity, development and morphogenesis. Modification of cellular polarity may potentially lead to abnormal cellular activities and various pathological disorders (Molitoris, 1991; Carone et al., 1994; Chen et al., 1995). Our review shows the complex interplay between membrane proteins and the cytoskeletal network in determining the "polarized phenotype" in the cell. We provide evidence that membrane/cytoskeleton interaction is the key to regulation of the vast majority of cellular functions.
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    Robust membrane detection based on tensor voting for electron tomography
    (Elsevier, 2014-04) Martinez-Sanchez, Antonio; García, Inmaculada; Asano, Shoh; Lucic, Valdan; Fernandez, Jose-Jesus; Ingeniería de la Información y las Comunicaciones
    Electron tomography enables three-dimensional (3D) visualization and analysis of the subcellular architecture at a resolution of a few nanometers. Segmentation of structural components present in 3D images (tomograms) is often necessary for their interpretation. However, it is severely hampered by a number of factors that are inherent to electron tomography (e.g. noise, low contrast, distortion). Thus, there is a need for new and improved computational methods to facilitate this challenging task. In this work, we present a new method for membrane segmentation that is based on anisotropic propagation of the local structural information using the tensor voting algorithm. The local structure at each voxel is then refined according to the information received from other voxels. Because voxels belonging to the same membrane have coherent structural information, the underlying global structure is strengthened. In this way, local information is easily integrated at a global scale to yield segmented structures. This method performs well under low signal-to-noise ratio typically found in tomograms of vitrified samples under cryo-tomography conditions and can bridge gaps present on membranes. The performance of the method is demonstrated by applications to tomograms of different biological samples and by quantitative comparison with standard template matching procedure.