Feature-based,Automated Segmentation of Cerebral Infarct Patterns Using T 2- and Diffusion-weighted Imaging

Diffusion-weighted imaging enables the diagnosis of cerebral ischemias very early, thus supporting therapies such as thrombolysis. However, morphology and tissue-characterizing parameters (e.g. relaxation times or water diffusion) may vary strongly in ischemic regions, indicating different underlying pathologic processes. As the determination of the parameters by a supervised segmentation is very time consuming, we evaluated whether different infarct patterns may be segmented by an automated, multidimensional feature-based method using a unified segmentation procedure. Ischemias were classified into 5 characteristic patterns. For each class, a 3D histogram based on T 2 - and diffusion-weighted images as well as calculated apparent diffusion coefficients (ADC) was generated from a representative data set. Healthy and pathologic tissue classes were segmented in the histogram as separate, local density maxima with freely shaped borders. Segmentation control parameters were optimized in a 3-step procedure. The method was evaluated using synthetic images as well as results of a supervised segmentation. For the analysis of cerebral ischemias, the optimal control parameter set led to sensitivities and specificities between 1.0 and 0.9.     

Colorization and automated segmentation of human T2 MR brain images for characterization of soft tissues

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described.     

Prostate segmentation on T2 MRI using Optimal Surface Detection

This paper deals with a T2 MRI prostate segmentation method. We assume to have an initial surface mesh obtained interactively or after a first rough segmentation. The surface of the prostate is then searched within the initial mesh neighborhood using the Optimal Surface Detection algorithm (OSD). This algorithm is based on the construction of a directed graph from the information obtained around the initial mesh. The optimal surface is then obtained by a graph cut. Three different cost functions for the graph have been explored, one based on the local gradient, another on a statistical model of shape and a third on a model of gradient profile. The parameters of this method have been tuned on 33 different T2 MRI volumes.     

Identification of gap junction blockers using automated fluorescence microscopy imaging

Gap junctions coordinate electrical signals and facilitate metabolic synchronization between cells. In this study, the authors have developed a novel assay for the identification of gap junction blockers using fluorescence microscopy imaging-based high-content screening technology. In the assay, the communication between neighboring cells through gap junctions was measured by following the redistribution of a fluorescent marker. The movement of calcein dye from dye-loaded donor cells to dye-free acceptor cells through gap junctions overexpressed on cell surface membranes was monitored using automated fluorescence microscopy imaging in a high-throughput compatible format. The fluorescence imaging technology consisted of automated focusing, image acquisition, image processing, and data mining. The authors have successfully performed a high-throughput screening of a 486,000- compound program with this assay, and they were able to identify false positives without additional experiments. Selective and pharmacologically interesting compounds were identified for further optimization.     

Benefits of automated crystallization plate tracking, imaging, and analysis

We describe the design of a database and software for managing and organizing protein crystallization data. We also outline the considerations behind the design of a fast web interface linking protein production data, crystallization images, and automated image analysis. The database and associated interfaces underpin the Oxford Protein Production Facility (OPPF) crystallization laboratory, collecting, in a routine and automatic manner, up to 100,000 images per day. Over 17 million separate images are currently held in this database. We discuss the substantial scientific benefits automated tracking, imaging, and analysis of crystallizations offers to the structural biologist: analysis of the time course of the trial and easy analysis of trials with related crystallization conditions. Features of this system address requirements common to many crystallographic laboratories that are currently setting up (semi-)automated crystallization imaging systems.     

Complete dissection of the Hb(64-76) determinant using T helper 1, T helper 2 clones, and T cell hybridomas.

We have generated cloned Th1 cells, Th2 cells, and T cell hybridomas specific for the single immunogenic peptide from the beta-chain of murine hemoglobin (Hb(64-76)). The availability of these various types of T cells provided us an unique opportunity to examine and dissect the T cell response to an immunogenic peptide. A panel of altered Hb peptides was made by replacing each amino acid in the Hb peptide (positions 64-76) with a conservative amino acid substitution or an alanine. Although none of the eleven T cell clones and hybridomas tested exhibited the same pattern of reactivity to the substituted Hb peptides, some general features were identified for all T cell responses. The primary T cell contact residue of Hb(64-76) was shown to be asparagine 72. For every Hb(64-76) specific T cell, no activation was observed using a peptide containing the conservative substitution of a glutamine for the asparagine at position 72. The flanking glutamic acid at position 73 was also required for a proliferative response for all of the Th1 and Th2 clones. The Th subtypes were not grossly unique in their responses to the substituted Hb peptides, but exhibited minor differences in fine specificity with the Th1 cells identifying more critical amino acids then did the Th2 cells. For the Th1 cells and also the T cell hybridomas, the phenylalanine at position 71 was critical for a T cell response. Analysis of peptide affinity for IEk molecules indicated that position 71 played a role in peptide binding to MHC. Secondary T cell contact residues, which were important for many but not all of the T cells, were identified at positions 69, 70, and 76. Overall T cell responses were minimally affected by changes in the amino acid residues at positions 64-68, 74, and 75. We have also demonstrated that cloned Th1 cells, Th2 cells and T hybridomas can be generated against the same Hb(64-76) determinant.     

Perfusion and molecular diffusion-weighted MR imaging of the brain: in vivo assessment of tissue alteration in cerebral ischemia

Summary.  The combined use of perfusion imaging (PI) and diffusion-weighted imaging (DWI) is opening a new window into the processes that occur during the first hours of ischemia. DWI detects changes in molecular diffusion associated with cytotoxic edema. PI characterizes the degree of regional hypoperfusion. Regions showing mismatches between DWI and PI, i.e. hypoperfused areas with normal diffusion behavior are considered potentially salvageable. We present results of 11 patients with an occlusion of the middle cerebral artery stem and spontaneous stroke evolution. Whereas the infarct was clearly visible on initial DWI and PI, surrounding tissue at risk of infarction was marked in all patients by an increased blood volume and transit time, but only in a subgroup (n = 3) where alteration were more pronounced this tissue at risk was progressively infarcted. These human DWI and PI data show alterations in the area of tissue at risk which correlates with infarct progression. Received June 29, 2001 Accepted August 6, 2001 Published online August 20, 2002     

A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data

This paper investigated application of a machine learning approach (Support vector machine, SVM) for the automatic recognition of gait changes due to ageing using three types of gait measures: basic temporal/spatial, kinetic and kinematic. The gaits of 12 young and 12 elderly participants were recorded and analysed using a synchronized PEAK motion analysis system and a force platform during normal walking. Altogether, 24 gait features describing the three types of gait characteristics were extracted for developing gait recognition models and later testing of generalization performance. Test results indicated an overall accuracy of 91.7% by the SVM in its capacity to distinguish the two gait patterns. The classification ability of the SVM was found to be unaffected across six kernel functions (linear, polynomial, radial basis, exponential radial basis, multi-layer perceptron and spline). Gait recognition rate improved when features were selected from different gait data type. A feature selection algorithm demonstrated that as little as three gait features, one selected from each data type, could effectively distinguish the age groups with 100% accuracy. These results demonstrate considerable potential in applying SVMs in gait classification for many applications.     

Influence of staining on fast automated cell segmentation, feature extraction and cell image analysis

FAZYTAN, a system for fast automated cell segmentation, cell image analysis and extraction of nuclear features, was used to analyze cervical cell images variously stained by the conventional Papanicolaou stain, the new Papanicolaou stain and hematoxylin and thionin only; the last two dyes are used as the nuclear stains in the two versions of the Papanicolaou stain. Other dyes were also tried in cell classification experiments. All cell images in the variously stained samples could be described by the same nuclear features as had been adapted for the discrimination of conventional-Papanicolaou-stained cells. Variances were lower for thionin-stained cells as compared with hematoxylin-stained cells. By application of spectrophotometry, it was confirmed that the spectra of the cytoplasmic counterstains are superimposed on those of the nuclear stains. It appears that a variety of dyes are suitable as cytologic stains for cell classification by the FAZYTAN system, provided that they achieve sufficiently strong nuclear-cytoplasmic contrast by precisely delineating the chromatin texture.     

Purification of human haptoglobin 1-1, 2-1, and 2-2 using monoclonal antibody affinity chromatography

Similar to blood type, human plasma haptoglobin (Hp) is classified as 3 phenotypes: Hp 1-1, 2-1, or 2-2. The structural and functional relationship between the phenotypes, however, has not been studied in detail due to the complicated and difficult isolation procedures. This report provides a simple protocol that can be used to purify each Hp phenotype. Plasma was first passed through an affinity column coupled with a high affinity Hp monoclonal antibody. The bound material was washed with a buffer containing 0.2M NaCl and 0.02 M phosphate, pH 7.4, eluted at pH 11, and collected in tubes containing 1M Tris-HCl, pH 6.8. The crude Hp fraction was then chromatographed on a HPLC Superose 12 column in 0.05 M ammonium bicarbonate at a flow rate of 0.5 ml/min. The homogeneity of purified Hp 1-1, 2-1, or 2-2 was greater than 95% as judged by SDS-polyacrylamide gel electrophoresis. Essentially, each Hp isolated was not contaminated with hemoglobin and apolipoprotein A-I as that reported from the other methods, and was able to bind hemoglobin. Neuraminidase treatment demonstrated that the purified Hp possessed a carbohydrate moiety, while Western blot analysis confirmed alpha and beta chains corresponding to each Hp 1-1, 2-1, and 2-2 phenotype. The procedures described here represent a significant improvement in current purification methods for the isolation of Hp phenotypes. Circular dichroic spectra showed that the alpha-helical content of Hp 1-1 (29%) was higher than that of Hp 2-1 (22%), and 2-2 (21%). The structural difference with respect to its clinical relevance is discussed.     

Fully automated segmentation and morphometrical analysis of muscle fiber images.

BACKGROUND: Measurement of muscle fiber size and determination of size distribution is important in the assessment of neuromuscular disease. Fiber size estimation by simple inspection is inaccurate and subjective. Manual segmentation and measurement are time-consuming and tedious. We therefore propose an automated image analysis method for objective, reproducible, and time-saving measurement of muscle fibers in routinely hematoxylin-eosin stained cryostat sections. METHODS: The proposed segmentation technique makes use of recent advances in level set based segmentation, where classical edge based active contours are extended by region based cues, such as color and texture. Segmentation and measurement are performed fully automatically. Multiple morphometric parameters, i.e., cross sectional area, lesser diameter, and perimeter are assessed in a single pass. The performance of the computed method was compared to results obtained by manual measurement by experts. RESULTS: The correct classification rate of the computed method was high (98%). Segmentation and measurement results obtained manually or automatically did not reveal any significant differences. CONCLUSIONS: The presented region based active contour approach has been proven to accurately segment and measure muscle fibers. Complete automation minimizes user interaction, thus, batch processing, as well as objective and reproducible muscle fiber morphometry are provided.     

Accurate automated quantitative imaging of tortoise erythrocytes using the nis image analysis system

Abstract

The standard method for assessing blood cell characteristics using an ocular micrometer is time-consuming and limited. We used the Nikon NIS Elements imaging software and May- Grünwald-Giemsa staining to determine whether automated image analysis is suitable for rapid and accurate quantitative morphometry of erythrocytes. Blood was collected during four seasons from 126 geometric tortoises and the blood smears were evaluated for cell (C) and nuclear (N) characteristics of the erythrocytes. We measured area, length (L), width (W), perimeter, elongation and pixelation intensity, and calculated L/W and N/C areas.

Erythrocyte size differed among cohorts; females, the larger sex, had smaller erythrocytes than either males or juveniles. Males had more elongated erythrocytes than females and erythrocytes of adults were more elongated than those of juveniles. Erythrocyte size and shape influence the efficiency of gas exchange owing to surface area to volume ratios, which are greater for small, elongated cells than for large, round cells. The high N/C ratio and low pixelation intensities of males and juveniles indicate that they may have had more immature erythrocytes in their circulation than females. The use of pixelation intensity to indicate the presence of immature erythrocytes was validated by seasonal differences that corresponded to the biology of the tortoises. Pixelation intensity was lowest in winter. We found that automated image analysis is a rapid and reliable method for determining cell size and shape, and it offers the potential for distinguishing among developmental stages that differ in staining intensity. The method should be useful for rapid health assessments, particularly of threatened species, and for comparative studies among different vertebrates.     

Classification of mouse sperm motility patterns using an automated multiclass support vector machines model

Vigorous sperm motility, including the transition from progressive to hyperactivated motility that occurs in the female reproductive tract, is required for normal fertilization in mammals. We developed an automated, quantitative method that objectively classifies five distinct motility patterns of mouse sperm using Support Vector Machines (SVM), a common method in supervised machine learning. This multiclass SVM model is based on more than 2000 sperm tracks that were captured by computer-assisted sperm analysis (CASA) during in vitro capacitation and visually classified as progressive, intermediate, hyperactivated, slow, or weakly motile. Parameters associated with the classified tracks were incorporated into established SVM algorithms to generate a series of equations. These equations were integrated into a binary decision tree that sequentially sorts uncharacterized tracks into distinct categories. The first equation sorts CASA tracks into vigorous and nonvigorous categories. Additional equations classify vigorous tracks as progressive, intermediate, or hyperactivated and nonvigorous tracks as slow or weakly motile. Our CASAnova software uses these SVM equations to classify individual sperm motility patterns automatically. Comparisons of motility profiles from sperm incubated with and without bicarbonate confirmed the ability of the model to distinguish hyperactivated patterns of motility that develop during in vitro capacitation. The model accurately classifies motility profiles of sperm from a mutant mouse model with severe motility defects. Application of the model to sperm from multiple inbred strains reveals strain-dependent differences in sperm motility profiles. CASAnova provides a rapid and reproducible platform for quantitative comparisons of motility in large, heterogeneous populations of mouse sperm.     

LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks

Digital pathology and microscope image analysis is widely used in comprehensive studies of cell morphology. Identification and analysis of leukocytes in blood smear images, acquired from bright field microscope, are vital for diagnosing many diseases such as hepatitis, leukaemia and acquired immune deficiency syndrome (AIDS). The major challenge for robust and accurate identification and segmentation of leukocyte in blood smear images lays in the large variations of cell appearance such as size, colour and shape of cells, the adhesion between leukocytes (white blood cells, WBCs) and erythrocytes (red blood cells, RBCs), and the emergence of substantial dyeing impurities in blood smear images. In this paper, an end‐to‐end leukocyte localization and segmentation method is proposed, named LeukocyteMask, in which pixel‐level prior information is utilized for supervisor training of a deep convolutional neural network, which is then employed to locate the region of interests (ROI) of leukocyte, and finally segmentation mask of leukocyte is obtained based on the extracted ROI by forward propagation of the network. Experimental results validate the effectiveness of the propose method and both the quantitative and qualitative comparisons with existing methods indicate that LeukocyteMask achieves a state‐of‐the‐art performance for the segmentation of leukocyte in terms of robustness and accuracy .     

Production of interleukin 2, interleukin 3, and interferon by mouse T lymphocyte clones of Lyt-2+ and -2- phenotype

T lymphocyte clones were derived by stimulation of positively selected Lyt-2+ and Lyt-2- lymphocytes with Con A in an Interleukin 2 (IL 2)-enriched medium under conditions of limiting dilution. Forty clones were expanded and tested, after activation by Con A, for the production of IL 2, IL 3, and interferon (IFN). Thirteen Lyt-2- clones were all co-producers of IL 2 and IL 3, and 10/13 were also producers of IFN. Twenty-seven Lyt-2+ clones were much more heterogeneous, 13 being IL 2 and IL 3 nonproducers, whereas 14 produced variable and poorly correlated amounts of IL 2 and IL 3. Three Lyt-2+ clones were observed to produce IL 2 or IL 3 alone. The majority of the Lyt-2- (10/13) and of the Lyt 2+ (21/27) clones were also producers of IFN. Exogenous IL 2 added during the activation of the Lyt-2+ by Con A did not enhance IL 3 production, whereas it did enhance IFN production by some but not all Lyt-2+ clones. Thus, among the T lymphocytes of the Lyt-2+ and -2- phenotypes there are cells capable of releasing IL 2, IL 3, and IFN. This supports the concept that these phenotypes are associated with antigen recognition rather than with cell functions, but it is apparent that the functional capacities of individual T cells, if accurately represented by their clonal progeny, are far from uniform.     

Visualization and correction of automated segmentation,tracking and lineaging from 5-D stem cell image sequences

Background

Neural stem cells are motile and proliferative cells that undergo mitosis, dividing to produce daughter cells and ultimately generating differentiated neurons and glia. Understanding the mechanisms controlling neural stem cell proliferation and differentiation will play a key role in the emerging fields of regenerative medicine and cancer therapeutics. Stem cell studies in vitro from 2-D image data are well established. Visualizing and analyzing large three dimensional images of intact tissue is a challenging task. It becomes more difficult as the dimensionality of the image data increases to include time and additional fluorescence channels. There is a pressing need for 5-D image analysis and visualization tools to study cellular dynamics in the intact niche and to quantify the role that environmental factors play in determining cell fate.

Results

We present an application that integrates visualization and quantitative analysis of 5-D (x,y,z,t,channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks.

Conclusions

By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. We combine unsupervised image analysis algorithms with an interactive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-328) contains supplementary material, which is available to authorized users.     

Functional analysis of 2-5A-dependent RNase and 2-5a using 2',5'-oligoadenylate-cellulose

2-5A is an intracellular effector that has been implicated in interferon action, hormonal regulation, and cell growth control. 2-5A action is mediated through its activation of 2-5A-dependent RNase (RNase L, RNase F). Affinity resins [2-5A-cellulose and core (2-5A)-cellulose] were chemically synthesized for purification and immobilization of 2-5A-dependent RNase from mouse L cells and rabbit reticulocyte lysates. The breakdown of poly(U)-[3'-32P]Cp to acid-soluble fragments was demonstrated using the 2-5A-dependent RNase:2-5A -cellulose complex; this activity was enhanced by adding (free) 2-5A. In contrast, RNase activity was measured from the 2-5A-dependent RNase:core (2-5A)-cellulose complex only after the addition of free 2-5A. The rabbit reticulocyte 2-5A-dependent RNase is activated only by tetramer or higher oligomers of 2-5A; therefore there was breakdown of poly(U)-[3'-32P]Cp using core (2-5A)-cellulose-bound reticulocyte 2-5A-dependent RNase after addition of tetramer 2-5A but there was no poly(U) degradation in the presence of trimer 2-5A. The absence of significant general nuclease in the assays was demonstrated by the resistance to breakdown of poly(C)-[3'-32P]Cp (not susceptible to 2-5A-dependent RNase). Moreover, core (2-5A)-cellulose was used to develop a sensitive (subnanomolar) assay for the detection of authentic 2-5A. 2-5A, or the material to be tested, was added to mouse L-cell 2-5A-dependent RNase:core (2-5A)-cellulose complex in the presence of poly(U)-[3'-32P]Cp. The concentration of 2-5A in the sample could be measured from the amount of poly(U) degradation. Several closely related analogs of 2-5A were tested and found to be completely inactive. The technology described herein may be applied to the study of the regulation of 2-5A-dependent RNase, the detection of 2-5A from cells and tissues, and other aspects of the 2-5A system.     

Gurmarin sensitivity of sweet taste responses is associated with co-expression patterns of T1r2, T1r3, and gustducin

Gurmarin (Gur) is a peptide that selectively suppresses sweet taste responses in rodents. The inhibitory effect of Gur differs among tongue regions and mouse strains. Recent studies demonstrated that co-expression levels of genes controlling sweet receptors (T1r2/T1r3 heterodimer) versus Gα-protein, gustducin, are much lower in Gur-insensitive posterior circumvallate papillae than in Gur-sensitive anterior fungiform papillae. Here, we investigated the potential link of Gur-sensitivity with the co-expression for T1r2/T1r3 receptors and gustducin by comparing those of taste tissues of Gur-sensitive (B6, dpa congenic strains) and Gur-weakly-sensitive (BALB) strains. The results indicated that co-expression ratios among T1r2, T1r3, and gustducin in the fungiform papillae were significantly lower in Gur-weakly-sensitive BALB mice than in Gur-sensitive B6 and dpa congenic mice. This linkage between Gur-sensitivity and co-expression for T1r2/T1r3 receptors versus gustducin suggests that gustducin may be a key molecule involved in the pathway for Gur-sensitive sweet responses.