An adaptive speed term based on homogeneity for level-set segmentation

We tested on an edge map computed from a local homogeneity measurement, which is a potential replacement for the traditional gradient-based edge map in level-set segmentation. In existing level-set methods, the gradient information is used as a stopping criteria for curve evolution, and also provides the attracting force to the zero level-set from the target boundary. However, in a discrete implementation, the gradient-based term can never fully stop the level-set evolution even for ideal edges, leakage is often unavoidable. Also the effective distance of the attracting force and blurring of edges become a trade-off in choosing the shape and support of the smoothing filter. The proposed homogeneity measurement provides easier and more robust edge estimation, and the possibility of fully stopping the level-set evolution. The homogeneity term decreasing from a homogenous region to the boundary, which dramatically increases the effective distance of the attracting force and also provides additional measurement of the overall approximation to the target boundary. Therefore, it provides a reliable criteria of adaptively changing the advent speed. By using this term, the leakage problem was avoided effectively in most cases compared to traditional level-set methods. The computation of the homogeneity is fast and its extension to the 3D case is straightforward

Improving statistics for hybrid segmentation of high-resolution multichannel images

High-resolution multichannel textures are difficult to characterize with simple statistics and the high level of detail makes the selection of a particular contour using classical gradient-based methods not effective. We have developed a hybrid method that combines fuzzy connectedness and Voronoi diagram classification for the segmentation of color and multichannel objects. The multi-step classification process relies on homogeneity measures derived from moment statistics and histogram information. These color features have been optimized to best combine individual channel information in the classification process. The segmentation initialization requires only a set of interior and exterior seed points, minimizing user intervention and the influence of the initialization on the overall quality of the results. The method was tested on volumes from the Visible Human and on brain multi-protocol MRI data sets. The hybrid segmentation produced robust, rapid and finely detailed contours with good visual accuracy. The addition of quantized statistics and color histogram distances as classification features improved the robustness of the method with regards to initialization when compared to our original implementation

Persistence of antibody after a vi-tetanus toxoid conjugate vaccine and effect of boosting with a plain polysaccharide vaccine on Vi antibody and antigen-specific B cells

Background: Salmonella enterica serovar Typhi is estimated to cause 9 to 13 million cases of typhoid fever annually. Typhoid conjugate vaccines represent a promising prophylactic measure to prevent disease, but there are few data assessing persistence of immunity. The effect of a Vi polysaccharide booster vaccine in individuals previously vaccinated with the Vi-tetanus toxoid typhoid conjugate vaccine has not been assessed previously. Methods: Thirty five healthy adult volunteers received a single dose of the Vi conjugate vaccine (Vi-TT) and 37 received a single dose of Vi polysaccharide vaccine (Vi-PS) prior to oral challenge with live S. Typhi bacteria as part of a randomised controlled, phase 2b study. In addition to data previously published showing persistence of Vi IgG and IgA antibodies for 7 months after Vi vaccination, titres were measured at intervals until 13 months post-vaccination. Ten participants who received Vi-TT (both challenged and unchallenged) were re-vaccinated with Vi-PS at an interval of 19-23 months post-prime. Anti-Vi IgG and IgA titres, and Vi-specific antibody secreting cells and memory B cells were measured at seven days and one month post-boost. Findings: Vi IgG and IgA antibody titres remained significantly elevated above baseline levels 13 months after priming with Vi-TT, with a 4-fold rise retained in 90% and 88% of recipients (Vi IgG and IgA, respectively). Anti-Vi IgG and IgA antibody titres were found to persist at higher levels in participants who received a single dose of Vi-TT than in those who received Vi-PS. No significant boost in Vi-antibody titre was observed in response to oral challenge with S. Typhi bacteria, one month after vaccination. Following a Vi-PS booster vaccination in those previously vaccinated with Vi-TT, anti-Vi IgG and IgA titres were significantly elevated, with similar titres observed at one month post-boost compared with one month after primary vaccination. The frequency of Vi-specific IgA antibody secreting cells increased significantly 7 days post-boost compared with pre-boost. No memory B cell response was observed following Vi-PS booster vaccination. Interpretation: Strong persistence of anti-Vi IgG and IgA following Vi-TT vaccination suggests that the conjugate vaccine may offer durable protection, supporting its use in endemic settings

Hybrid Segmentation of Anatomical Data

We propose new hybrid methods for automated segmentation of radiological patient data and the Visible Human data. In this paper, we integrate boundary-based and region-based segmentation methods which amplifies the strength but reduces the weakness of both approaches. The novelty comes from combining a boundary-based method, the deformable model-based segmentation with region-based segmentation methods, the fuzzy connectedness and Voronoi Diagram-based segmentation, to develop hybrid methods that yield high precision, accuracy and efficiency. This work is a part of a NLM funded effort to provide a fully implemented and tested Visible Human Project Segmentation and Registration Toolkit (Insight)

Molecular correlates of vaccine-induced protection against typhoid fever

BACKGROUNDTyphoid fever is caused by the Gram-negative bacterium Salmonella enterica serovar Typhi and poses a substantial public health burden worldwide. Vaccines have been developed based on the surface Vi-capsular polysaccharide of S. Typhi; these include a plain-polysaccharide-based vaccine, ViPS, and a glycoconjugate vaccine, ViTT. To understand immune responses to these vaccines and their vaccine-induced immunological protection, molecular signatures were analyzed using bioinformatic approaches.METHODSBulk RNA-Seq data were generated from blood samples obtained from adult human volunteers enrolled in a vaccine trial, who were then challenged with S. Typhi in a controlled human infection model (CHIM). These data were used to conduct differential gene expression analyses, gene set and modular analyses, B cell repertoire analyses, and time-course analyses at various post-vaccination and post-challenge time points between participants receiving ViTT, ViPS, or a control meningococcal vaccine.RESULTSTranscriptomic responses revealed strong differential molecular signatures between the 2 typhoid vaccines, mostly driven by the upregulation in humoral immune signatures, including selective usage of immunoglobulin heavy chain variable region (IGHV) genes and more polarized clonal expansions. We describe several molecular correlates of protection against S. Typhi infection, including clusters of B cell receptor (BCR) clonotypes associated with protection, with known binders of Vi-polysaccharide among these.CONCLUSIONThe study reports a series of contemporary analyses that reveal the transcriptomic signatures after vaccination and infectious challenge, while identifying molecular correlates of protection that may inform future vaccine design and assessment.TRIAL REGISTRATIONClinicalTrials.gov NCT02324751

Chapter 1 : Hybrid Segmentation Methods

We propose a Hybrid Segmentation Engine that consists of component modules, for automated segmentation of radiological patient and the Visible Human data. We integrate boundary-based and region-based segmentation methods to exploit the strength of each method hopefully to cover the weakness of the other method

Control of Invasive Salmonella Disease in Africa: Is There a Role for Human Challenge Models?

Invasive Salmonella disease in Africa is a major public health concern. With evidence of the transcontinental spread of the Salmonella Typhi H58 haplotype, improved estimates of the burden of infection and understanding of the complex interplay of factors affecting disease transmission are needed to assist with efforts aimed at disease control. In addition to Salmonella Typhi, invasive nontyphoidal Salmonella are increasingly recognized as an important cause of febrile illness and mortality in sub-Saharan Africa. Human experimental oral challenge studies with Salmonella can be used as a model to offer unique insights into host-pathogen interactions as well as a platform to efficiently test new diagnostic and vaccine candidates. In this article, we review the background and use of human challenge studies to date and discuss how findings from these studies may lead to progress in the control of invasive Salmonella disease in Africa

A tool for evaluating heterogeneity in avidity of polyclonal antibodies

Diversity in specificity of polyclonal antibody (pAb) responses is extensively investigated in vaccine efficacy or immunological evaluations, but the heterogeneity in antibody avidity is rarely probed as convenient tools are lacking. Here we have developed a polyclonal antibodies avidity resolution tool (PAART) for use with label-free techniques, such as surface plasmon resonance and biolayer interferometry, that can monitor pAb-antigen interactions in real time to measure dissociation rate constant (kd) for defining avidity. PAART utilizes a sum of exponentials model to fit the dissociation time-courses of pAb-antigens interactions and resolve multiple kd contributing to the overall dissociation. Each kd value of pAb dissociation resolved by PAART corresponds to a group of antibodies with similar avidity. PAART is designed to identify the minimum number of exponentials required to explain the dissociation course and guards against overfitting of data by parsimony selection of best model using Akaike information criterion. Validation of PAART was performed using binary mixtures of monoclonal antibodies of same specificity but differing in kd of the interaction with their epitope. We applied PAART to examine the heterogeneity in avidities of pAb from malaria and typhoid vaccinees, and individuals living with HIV-1 that naturally control the viral load. In many cases, two to three kd were dissected indicating the heterogeneity of pAb avidities. We showcase examples of affinity maturation of vaccine induced pAb responses at component level and enhanced resolution of heterogeneity in avidity when antigen-binding fragments (Fab) are used instead of polyclonal IgG antibodies. The utility of PAART can be manifold in examining circulating pAb characteristics and could inform vaccine strategies aimed to guide the host humoral immune response

A Methodology for Evaluating Image Segmentation Algorithms

The purpose of this paper is to describe a framework for evaluating image segmentation algorithms. Image segmentation consists of object recognition and delineation. For evaluating segmentation methods, three factors - precision (reproducibility), accuracy (agreement with truth), and efficiency (time taken) – need to be considered for both recognition and delineation. To assess precision, we need to choose a figure of merit (FOM), repeat segmentation considering all sources of variation, and determine variations in FOM via statistical analysis. It is impossible usually to establish true segmentation. Hence, to assess accuracy, we need to choose a surrogate of true segmentation and proceed as for precision. To assess efficiency, both the computational and the user time required for algorithm and operator training and for algorithm execution should be measured and analyzed. Precision, accuracy, and efficiency are interdependent. It is difficult to improve one factor without affecting others. Segmentation methods must be compared based on all three factors. The weight given to each factor depends on application

Evaluation of Ischemic Stroke Hybrid Segmentation in a Rat Model of Temporary Middle Cerebral Artery Occlusion using Ground Truth from Histologic and MR data

A segmentation method that quantifies cerebral infarct using rat data with ischemic stroke is evaluated using ground truth from histologic and MR data. To demonstrate alternative approach to rapid quantification of cerebral infarct volumes using histologic stained slices that requires scarifying animal life, a study with MR acquire volumetric rat data is proposed where ground truth is obtained by manual delineations by experts and automated segmentation is assessed for accuracy. A framework for evaluation of segmentation is used that provides more detailed accuracy measurements than mere cerebral infarct volume. Our preliminary experiment shows that ground truth derived from MRI data is at least as good as the one obtained from the histologic slices for evaluating segmentation algorithms for accuracy. Therefore we can develop and evaluate automated segmentation methods for rapid quantification of stroke without the necessitating animal sacrifice