A differential structure approach to membrane segmentation in electron tomography

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.     

A novel three-dimensional variant of the watershed transform for segmentation of electron density maps

Electron density maps at moderate resolution are often difficult to interpret due to the lack of recognizable features. This is especially true for electron tomograms that suffer in addition to the resolution limitation from low signal-to-noise ratios. Reliable segmentation of such maps into smaller, manageable units can greatly facilitate interpretation. Here, we present a segmentation approach targeting three-dimensional electron density maps derived by electron microscopy. The approach consists of a novel three-dimensional variant of the immersion-based watershed algorithm. We tested the algorithm on calculated data and applied it to a wide variety of electron density maps ranging from reconstructions of single macromolecules to tomograms of subcellular structures. The results indicate that the algorithm is reliable, efficient, accurate, and applicable to a wide variety of biological problems.     

Reprint of "Three-dimensional elemental mapping of phosphorus by quantitative electron spectroscopic tomography (QuEST)" [J. Struct. Biol. 160 (2007) 35-48

We describe the development of quantitative electron spectroscopic tomography (QuEST), which provides 3-D distributions of elements on a nanometer scale. Specifically, it is shown that QuEST can be applied to map the distribution of phosphorus in unstained sections of embedded cells. A series of 2-D elemental maps is derived from images recorded in the energy filtering transmission electron microscope for a range of specimen tilt angles. A quantitative 3-D elemental distribution is then reconstructed from the elemental tilt series. To obtain accurate quantitative elemental distributions it is necessary to correct for plural inelastic scattering at the phosphorus L_2,3 edge, which is achieved by acquiring unfiltered and zero-loss images at each tilt angle. The data are acquired automatically using a cross correlation technique to correct for specimen drift and focus change between successive tilt angles. An algorithm based on the simultaneous iterative reconstruction technique (SIRT) is implemented to obtain quantitative information about the number of phosphorus atoms associated with each voxel in the reconstructed volume. We assess the accuracy of QuEST by determining the phosphorus content of ribosomes in a eukaryotic cell, and then apply it to estimate the density of nucleic acid in chromatin of the cell’s nucleus. From our experimental data, we estimate that the sensitivity for detecting phosphorus is 20 atoms in a 2.7 nm-sized voxel.     

Three-dimensional elemental mapping of phosphorus by quantitative electron spectroscopic tomography (QuEST)

We describe the development of quantitative electron spectroscopic tomography (QuEST), which provides 3-D distributions of elements on a nanometer scale. Specifically, it is shown that QuEST can be applied to map the distribution of phosphorus in unstained sections of embedded cells. A series of 2-D elemental maps is derived from images recorded in the energy filtering transmission electron microscope for a range of specimen tilt angles. A quantitative 3-D elemental distribution is then reconstructed from the elemental tilt series. To obtain accurate quantitative elemental distributions it is necessary to correct for plural inelastic scattering at the phosphorus L(2,3) edge, which is achieved by acquiring unfiltered and zero-loss images at each tilt angle. The data are acquired automatically using a cross correlation technique to correct for specimen drift and focus change between successive tilt angles. An algorithm based on the simultaneous iterative reconstruction technique (SIRT) is implemented to obtain quantitative information about the number of phosphorus atoms associated with each voxel in the reconstructed volume. We assess the accuracy of QuEST by determining the phosphorus content of ribosomes in a eukaryotic cell, and then apply it to estimate the density of nucleic acid in chromatin of the cell's nucleus. From our experimental data, we estimate that the sensitivity for detecting phosphorus is 20 atoms in a 2.7 nm-sized voxel.     

三维肺部图像中疑似肺结节结构的自动提取

本文提出了一种肺部CT图像三维数据中自动提取疑似结节区域的方法。首先结合阈值分割、种子填充等方法,在三维体数据上分割出肺实质。进而利用改进的模糊C均值聚类,提取出结节及具有结节特征的血管、支气管等感兴趣区域。该工作对感兴趣区域的特征提取有重要意义,是早期肺癌计算机辅助诊断重要的一步。     

Maximum-Entropy Three-Dimensional Reconstruction with Deconvolution of the Contrast Transfer Function: A Test Application with Adenovirus

We have developed an objective, quantitative, and general algorithm to improve the fidelity of three-dimensional reconstructions made from electron micrographs while at the same time filtering much of the noise present in the recorded data. The new technique is called constrained maximum entropy tomography (COMET). The essence of the method is that it will produce the most featureless reconstruction that fits the projection data within their observational accuracy. In particular, the COMET procedure will minimise the detrimental effects of errors in the measured data and deconvolute the effects of the contrast transfer function. An objective test has been performed using COMET on a conventional image reconstruction obtained from cryo-electron micrographs of adenovirus. The density for hexon, the major coat protein of the virus, which is known to high resolution from X-ray crystallography, provided a known high-resolution control. The COMET reconstruction is in considerably better agreement with the crystallographic electron density than the original reconstruction, throughout the entire resolution range.     

Modeling the 3-D RNA distribution in the Balbiani Ring granule

Mature Balbiani Ring (BR) granules in situ were stained with the nucleic acid specific stain, osmium ammine-B, recorded by electron spectroscopic imaging and reconstructed by electron microscope tomography to examine the three-dimensional (3-D) distribution of BR heterogeneous nuclear RNA (hnRNA). The BR2 granules contain ca. 37 kb of mRNA. Reconstructed BR granules were selected to emphasize one of the prevalent conformations seen in the sectioned salivary glands, the en face or pin-wheel conformation. A variety of image processing and volume-rendering operations were applied to the set of reconstructed BR granules. Some of the conclusions of this study are the following: (1) RNA distribution is not uniform throughout the granule; (2) RNA is condensed into about ten particles per granule, which all appear to possess approximately the same RNA stain density; (3) heterogeneity exists in the positions and sizes of particles within the various BR granules. These data argue for the folding of a beaded ribbon, consisting of connected particulate condensations of BR mRNA, possessing considerable 3-D flexibility, even in the packaged state. A comparison of this beadedribbon model and a prior folded hnRNP fiber model is also presented.     

Cubic Membrane Structure in Amoeba (Chaos carolinensis) Mitochondria Determined by Electron Microscopic Tomography

Cubic membranes occur in a variety of membrane-bound organelles in many cell types. By transmission electron microscopy (TEM) these membrane systems appear to consist of highly curved periodic surfaces that fit mathematical models analogous to those used to describe lipidic cubic phases. For the first time, a naturally occurring cubic membrane system has been reconstructed in three dimensions by electron microscopic tomography, and its periodicity directly characterized. Double-tilt tomographic reconstruction of mitochondria in the amoeba, Chaos carolinensis, confirms that their cristae (inner membrane infoldings) have the cubic structure suggested by modeling studies based on thin-section TEM images. Analysis of the membrane surfaces in the reconstruction reveals the connectivity of the internal compartments within the mitochondria. In the cubic regions, the matrix is highly condensed and confined to a continuous, small space between adjacent cristal membranes. The cristae form large, undulating cisternae that communicate with the peripheral (inner membrane) compartment through narrow tubular segments as seen in other types of mitochondria. The cubic periodicity of these mitochondrial membranes provides an ideal specimen for measuring geometrical distortions in biological electron tomography. It may also prove to be a useful model system for studies of the correlation of cristae–matrix organization with mitochondrial activity.     

Three-dimensional geometric modeling of membrane-bound organelles in ventricular myocytes: bridging the gap between microscopic imaging and mathematical simulation

A general framework of image-based geometric processing is presented to bridge the gap between three-dimensional (3D) imaging that provides structural details of a biological system and mathematical simulation where high-quality surface or volumetric meshes are required. A 3D density map is processed in the order of image pre-processing (contrast enhancement and anisotropic filtering), feature extraction (boundary segmentation and skeletonization), and high-quality and realistic surface (triangular) and volumetric (tetrahedral) mesh generation. While the tool-chain described is applicable to general types of 3D imaging data, the performance is demonstrated specifically on membrane-bound organelles in ventricular myocytes that are imaged and reconstructed with electron microscopic (EM) tomography and two-photon microscopy (T-PM). Of particular interest in this study are two types of membrane-bound Ca²⁺-handling organelles, namely, transverse tubules (T-tubules) and junctional sarcoplasmic reticulum (jSR), both of which play an important role in regulating the excitation–contraction (E–C) coupling through dynamic Ca²⁺ mobilization in cardiomyocytes.     

A general model for multiphase texture segmentation and its applications to retinal image analysis

In this paper we propose a general variational segmentation model for multiphase texture segmentation based on fuzzy region competition principle. An important strength of the proposed framework is that different region terms (e.g. mutual information Kim et al. (2005) [1], local histogram Ni et al. (2009) [2] models for texture-based segmentation, and piecewise constant intensity model Chan and Vese (2001) [3] for intensity-based segmentation) can be included as appropriate to the problem. Constraints of different phases are considered by introducing Lagrangian multipliers into the energy functional, and a fast numerical solution is achieved by employing the fast dual projection algorithm Chambolle (2004) [4]. The proposed model has been applied to synthetic and natural images in order to make comparisons with other competing models in literature. Our results demonstrate superiority in dealing with multiphase texture segmentation problems. To demonstrate its usefulness in biomedical applications we have applied the new model to two retinal image segmentation problems: segmentation of capillary non-perfusion regions in fluorescein angiogram and segmentation of cellular layers of the retina in optical coherence tomography, and evaluated against the gold standard set by experts. The generalized overlap analysis shows good agreement for both applications. As a generic segmentation technique our new model has the potential to be extended for wider applications.     

The domain knowledge based graph-cut model for liver CT segmentation

In this paper, we present a semi-supervised approach for liver segmentation from computed tomography (CT) scans, which is based on the graph cut model integrated with domain knowledge. Firstly, some hard constraints are obtained according to the knowledge of liver characteristic appearance and anatomical location. Secondly, the energy function is constructed via knowledge based similarity measure. A path-based spatial connectivity measure is applied for robust regional properties. Finally, the image is interpreted as a graph, afterwards the segmentation problem is casted as an optimal cut on it, which can be computed through the existing max-flow algorithm. The model is evaluated on MICCAI 2007 liver segmentation challenge datasets and some other CT volumes from the hospital. The experimental results show its effectiveness and efficiency.     

Three-dimensional spatial reconstruction of coronary arteries based on fusion of intravascular optical coherence tomography and coronary angiography

We present a three-dimensional (3D) spatial reconstruction of coronary arteries based on fusion of intravascular optical coherence tomography (IVOCT) and digital subtraction angiography (DSA). Centerline of vessel in DSA images is exacted by multi-scale filtering, adaptive segmentation, morphology thinning and Dijkstra's shortest path algorithm. We apply the cross-correction between lumen shapes of IVOCT and DSA images and match their stenosis positions to realize co-registration. By matching the location and tangent direction of the vessel centerline of DSA images and segmented lumen coordinates of IVOCT along pullback path, 3D spatial models of vessel lumen are reconstructed. Using 1121 distinct positions selected from eight vessels, the correlation coefficient between 3D IVOCT model and DSA image in measuring lumen radius is 0.94% and 97.7% of the positions fall within the limit of agreement by Bland–Altman analysis, which means that the 3D spatial reconstruction IVOCT models and DSA images have high matching level.     

Conical tomography II: A method for the study of cellular organelles in thin sections

We have used conical electron tomography in order to reconstruct neuronal organelles in thin sections of plastic embedded rat somato-sensory cortical tissue. The conical tilt series were collected at a 55 degrees tilt and at 5 degrees rotations, aligned using gold particles as fiduciary markers, and reconstructed using the weighted back projection algorithm. After a refinement process based on projection matching, the 3D maps showed the "unit membrane pattern" along the entire reconstructed volume. This pattern is indicative of the bilayer arrangement of phospholipids in biological membranes. Based on Fourier correlation methods as well as the visualization of the "unit membrane" pattern, we estimated resolutions of approximately 4 nm. To illustrate the prospective advantages of conical tomography, we segmented "coated" vesicles in the reconstructed volumes. These vesicles were comprised of a central core enclosing a small lumen, and a protein "coating" extending into the cytoplasm. The "coated" vesicle was attached to the plasma membrane through a complex structure shaped as an arch where the ends are attached to the membrane and the crook is connected to the vesicle. We concluded that conical electron tomography of thin-sectioned specimens provides a powerful experimental approach for studying thin-sectioned neuronal organelles at resolution levels of approximately 4 nm.     

Effective atomic number estimation using kV-MV dual-energy source in LINAC

Dual-energy computed tomography (DECT) imaging can measure the effective atomic number (EAN) as well as the electron density, and thus its adoption may improve dose calculations in brachytherapy and external photon/particle therapy. An expanded energy gap in dual-energy sources is expected to yield more accurate EAN estimations than conventional DECT systems, which typically span less than 100 kV. The aim of this paper is to assess a larger energy gap DECT by using a linear accelerator (LINAC) radiotherapy system with a kV X-ray imaging device, which are combined to provide X-rays in both the kV- and MV-energy ranges. Traditionally, the EAN is determined by parameterising the Hounsfield Unit; however, this is difficult in a kV-MV DECT due to different uncertainties in the reconstructed attenuation coefficient at each end of the energy spectrum. To overcome this problem, we included a new calibration step to produce the most likely linear attenuation coefficients, based upon the X-ray spectrum. To determine the X-ray spectrum, Monte Carlo calculations using GEANT4 were performed. Then the images were calibrated using information from eight inserts of known materials in a CIRS phantom (CIRS Inc., Norfolk, VA). Agreement between the estimated and empirical EANs in these inserts was within 11%. Validation was subsequently performed with the CatPhan500 phantom (The Phantom Laboratory, Salem). The estimated EAN for seven inserts agreed with the empirical values to within 3%. Accordingly, it can be concluded that, given properly reconstructed images based upon a well-determined X-ray spectrum, kV-MV DECT provides an excellent prediction for the EAN.     

Object Segmentation and Ground Truth in 3D Embryonic Imaging

Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets.     

A role for magnetic susceptibility in synthetic computed tomography

PurposeRadiotherapy treatment planning based on magnetic resonance imaging (MRI) benefits from increased soft-tissue contrast and functional imaging. MRI-only planning is attractive but limited by the lack of electron density information required for dose calculation, and the difficulty to differentiate air and bone. MRI can map magnetic susceptibility to separate bone from air. A method is introduced to produce synthetic CT (sCT) through automatic voxel-wise assignment of CT numbers from an MRI dataset processed that includes magnetic susceptibility mapping.MethodsVolumetric multi-echo gradient echo datasets were acquired in the heads of five healthy volunteers and fourteen patients with cancer using a 3 T MRI system. An algorithm for CT synthesis was designed using the volunteer data, based on fuzzy c-means clustering and adaptive thresholding of the MR data (magnitude, fat, water, and magnetic susceptibility). Susceptibility mapping was performed using a modified version of the iterative phase replacement algorithm. On patient data, the algorithm was assessed by direct comparison to X-ray computed tomography (CT) scans.ResultsThe skull, spine, teeth, and major sinuses were clearly distinguished in all sCT, from healthy volunteers and patients. The mean absolute CT number error between X-ray CT and sCT in patients ranged from 78 and 134 HU.ConclusionSusceptibility mapping using MRI can differentiate air and bone for CT synthesis. The proposed method is automated, fast, and based on a commercially available MRI pulse sequence. The method avoids registration errors and does not rely on a priori information, making it suitable for nonstandard anatomy.     

Membrane binding of a lipidated N-Ras protein studied in lipid monolayers

The adsorption of doubly lipidated full-length N-Ras protein on 1,2-dipalmitoyl-sn-phosphatidylcholine (DPPC) monolayers was studied by lateral pressure analysis, grazing incidence X-ray diffraction (GIXD), and specular reflectivity (XR). N-Ras protein adsorbs to the DPPC monolayer (lateral pressure of 20 mN/m) from the subphase thereby increasing the lateral pressure in the monolayer by 4 mN/m. The protein insertion does not alter the tilt angle and structure of the lipid molecules at the air/water interface but influences the electron density profile of the monolayer. Further, electron density differences into the subphase were observed. The Fresnel normalized reflectivity could be reconstructed in the analysis using box models yielding electron density profiles of the DPPC monolayer in the absence and in the presence of N-Ras protein. The electron density profiles of the DPPC monolayer in the presence of Ras showed clear intensity variations in the headgroup/glycerol/upper chain region, the so-called interface region where previous bilayer studies had confirmed Ras binding. Dedicated to Prof. K. Arnold on the occasion of his 65th birthday.     

通过SEC-UNet精准分割糖尿病视网膜病变眼底OCT图像脉络膜层

目的 糖尿病视网膜病变（DR）是糖尿病的严重并发症，可导致患者视力下降甚至失明。脉络膜的早期检查在DR诊断中起着至关重要的作用。然而，由于DR患者的光学相干层析成像（OCT）中存在脉络膜和巩膜边界模糊、视网膜病变阴影等问题，导致大多数现有算法无法精准分割脉络膜层。本文目的在于提高DR患者OCT图像中脉络膜层分割的精准度。方法 本文提出了一种结合挤压激励连接（SEC）模块和UNet的网络，简称SEC-UNet，不仅增强Unet的局部细节目标关注能力，且能跳出局部最优来增强整体表达能力。结果 SEC-UNet模型的ROC曲线下面积（AUC）达到0.993 0，优于传统UNet模型和SE-UNet模型。这表明SEC-UNet能够获得准确、完整的脉络膜层分割结果。统计分析脉络膜参数变化发现，与正常眼相比，87.1%的DR患者脉络膜中央凹1 mm内体积增加，这证明了DR很可能导致脉络膜增厚。结论 该技术有望成为一种新的辅助诊断工具，帮助医生研究脉络膜在糖尿病眼病的预防、发病机制和预后中的作用。     

Extended fuzzy c-means: an analyzing data clustering problems

In recent years the use of fuzzy clustering techniques in medical diagnosis is increasing steadily, because of the effectiveness of fuzzy clustering techniques in recognizing the systems in the medical database to help medical experts in diagnosing diseases. This study focuses on clustering lung cancer dataset into three types of cancers which are leading cause of cancer death in the world. This paper invents effective fuzzy clustering techniques by incorporating hyper tangent kernel function, and entropy methods for analyzing the Lung Cancer database to assist physician in diagnosing lung cancer. Further this paper proposes an algorithm to initialize the cluster centers to speed up the process of the algorithms. The effectiveness of the proposed methods has been proved through the experimental works on synthetic dataset, Wine dataset and IRIS dataset in terms of running time, number of iterations, visual segmentation effects and clustering accuracy. And then this paper proposes the proposed method on Lung cancer database to divide it into three types of lung cancers. In addition this paper proves the superiority of the proposed methods by comparing the obtained classes with reference classes through Error Matrix.     

Dose calculation validation of a convolution algorithm in a solid water phantom

PurposeThe dose calculated using a convolution algorithm should be validated in a simple homogeneous water-equivalent phantom before clinical use. The dose calculation accuracy within a solid water phantom was investigated.MethodsThe specific Gamma knife design requires a dose rate calibration within a spherical solid water phantom. The TMR10 algorithm, which approximates the phantom material as liquid water, correctly computes the absolute dose in water. The convolution algorithm, which considers electron density miscalculates the dose in water as the phantom Hounsfield units were converted into higher electron density when the original CT calibration curve was used. To address this issue, the electron density of liquid water was affected by modifying the CT calibration curve. The absolute dose calculated using the convolution algorithm was compared with that computed by the TMR10. The measured depth dose profiles were also compared to those computed by the convolution and TMR10 algorithms. A patient treatment was recalculated in the solid-water phantom and the delivery quality assurance was checked.ResultsThe convolution algorithm and the TMR10 calculate an absolute dose within 1% when using the modified CT calibration curve. The dose depth profile calculated using the convolution algorithms was superimposed on the TMR10 and measured dose profiles when the modified CT calibration curve was applied. The Gamma index was better than 93%.ConclusionsDose calculation algorithms, which consider electron density, require a CT calibration curve adapted to the phantom material to correctly compute the dose in water.