Reactivation of persistent human adenoviruses (HAdVs) is associated with high morbidity and mortality in paediatric haematopoietic stem cell transplant (HSCT) recipients. Although invasive HAdV ...infections mainly arise from the gastrointestinal (GI) tract, the specific sites of HAdV persistence are not well characterised. We prospectively screened biopsies from 143 non-HSCT paediatric patients undergoing GI endoscopy and monitored serial stool specimens from 148 paediatric HSCT recipients for the presence of HAdV by real-time PCR. Persistence of HAdV in the GI tract was identified in 31% of children, with the highest prevalence in the terminal ileum. In situ hybridisation and immunohistochemistry identified HAdV persistence in lymphoid cells of the lamina propria, whereas biopsies from five transplant recipients revealed high numbers of replicating HAdV in intestinal epithelial cells. The prevalence of HAdV species, the frequencies of persistence in the GI tract and reactivations post transplant indicated a correlation of intestinal HAdV shedding pre-transplant with high risk of invasive infection. HAdV persistence in the GI tract is a likely origin of infectious complications in immunocompromised children. Intestinal lymphocytes represent a reservoir for HAdV persistence and reactivation, whereas the intestinal epithelium is the main site of viral proliferation preceding dissemination. The findings have important implications for assessing the risk of life-threatening invasive HAdV infections.
In this work we propose a novel multi-scale operator which is based on the full color information within an image. In order to evaluate the method, we extract features from endoscopic images using ...this operator and classify the images according to the respective class of polyps. Display omitted
► Compared to other LBP-based operators LCVP uses all color information available, yet yielding a more compact descriptor for an image. ► LCVP is up to 7.5 times faster compared to other LBP-based methods evaluated. ► In terms of a classification of polyps the accuracy of LCVP differs insignificantly only from previously developed methods.
In this work we propose a novel method to describe local texture properties within color images with the aim of automated classification of endoscopic images. In contrast to comparable Local Binary Patterns operator approaches, where the respective texture operator is almost always applied to each color channel separately, we construct a color vector field from an image. Based on this field the proposed operator computes the similarity between neighboring pixels. The resulting image descriptor is a compact 1D-histogram which we use for a classification using the k-nearest neighbors classifier.
To show the usability of this operator we use it to classify magnification-endoscopic images according to the pit pattern classification scheme. Apart from that, we also show that compared to previously proposed operators we are not only able to get competitive classification results in our application scenario, but that the proposed operator is also able to outperform the other methods either in terms of speed, feature compactness, or both.
Abstract Due to the optics used in endoscopes, a typical degradation observed in endoscopic images are barrel-type distortions. In this work we investigate the impact of methods used to correct such ...distortions in images on the classification accuracy in the context of automated celiac disease classification. For this purpose we compare various different distortion correction methods and apply them to endoscopic images, which are subsequently classified. Since the interpolation used in such methods is also assumed to have an influence on the resulting classification accuracies, we also investigate different interpolation methods and their impact on the classification performance. In order to be able to make solid statements about the benefit of distortion correction we use various different feature extraction methods used to obtain features for the classification. Our experiments show that it is not possible to make a clear statement about the usefulness of distortion correction methods in the context of an automated diagnosis of celiac disease. This is mainly due to the fact that an eventual benefit of distortion correction highly depends on the feature extraction method used for the classification.
A large amount of digital image material is routinely captured during esophagogastroduodenoscopies but, for the most part, is not used for confirming the diagnosis process of celiac disease which is ...primarily based on histological examination of biopsies. Recently, considerable effort has been undertaken to make use of image material by developing semi- or fully-automated systems to improve the diagnostic workup. Recently, focus was especially laid on developing state-of-the-art deep learning architectures, exploiting the endoscopist’s expert knowledge and on making systems fully automated and thereby completely observer independent. In this work, we summarize recent trends in the field of computer-aided celiac disease diagnosis based on upper endoscopy and discuss about recent progress, remaining challenges, limitations currently prohibiting a deployment in clinical practice and future efforts to tackle them.
Abstract Automated classification of duodenal texture patches with histological ground truth in case of pediatric celiac disease is proposed. The classical focus of classification in this context is ...a two-class problem: mucosa affected by celiac disease and unaffected duodenal tissue. We extend this focus and apply classification according to a modified Marsh scheme into four classes. In addition to other techniques used previously for classification of endoscopic imagery, we apply local binary pattern (LBP) operators and propose two new operator types, one of which adapts to the different properties of wavelet transform subbands. The achieved results are promising in that operators based on LBP turn out to achieve better results compared to many other texture classification techniques as used in earlier work. Specifically, the proposed wavelet-based LBP scheme achieved the best overall accuracy of all feature extraction techniques considered in the two-class case and was among the best in the four-class scheme. Results also show that a classification into four classes is feasible in principle however when compared to the two-class case we note that there is still room for improvement due to various reasons discussed.
Highlights ► Exploiting the visual nature of pit patterns on the colonic mucosa. ► Roughly four times faster compared to a previously developed approach. ► Significantly higher classification rates ...compared to our previous work. ► More robust against overfitting when compared to other methods.
Abstract In most recent computer aided celiac disease diagnosis approaches, image regions (patches) showing discriminative features necessarily need to be manually extracted by the medical doctor, ...prior to the automated classification pipeline. However, although the obtained classification outcomes based on such semi-automated systems are attractive, a human interaction finally is undesired. In this work, fully automated approaches are investigated which are based on the measurement of several image quality properties. Firstly, we investigate a method based on optimization of single quality measures as well as an approach based on weighted combinations of these metrics. Furthermore, a weighted decision-level and a weighted feature-level fusion method are investigated which are not based on the selection of one single best patch, but on a weighted combination. In a large experimental setting, we evaluate these methods with respect to the achieved overall classification rates. Finally, especially the proposed feature-level fusion method supplies the best performances and comes close to manual experts' patch selection as far as the accuracy is concerned.
Images captured under non-laboratory conditions potentially suffer from various degradations. Especially noise, blur and scale-variations are often prevalent in real world images and are known to ...potentially affect the classification process of textured images. We show that these degradations not necessarily strongly affect the discriminative powers of computer based classifiers in a scenario with similar degradations in training and evaluation set. We propose a degradation-adaptive classification approach, which exploits this knowledge by dividing one large data set into several smaller ones, each containing images with some kind of degradation-similarity. In a large experimental study, it can be shown that our method continuously enhances the classification accuracies in case of simulated as well as real world image degradations. Surprisingly, by means of a pre-classification, the framework turns out to be beneficial even in case of idealistic images which are free from strong degradations.
Abstract This study monitors the epidemiology of invasive pneumococcal diseases (IPD) in hospitalized children up to 60 months of age before (February 2001–October 2004) and after (November ...2004–January 2007) the introduction of a national risk group immunization program with “Prevenar® ” in Austria. The IPD incidence rates, per 100,000, for IPD were 7.6 before and 6.4 after the risk group immunization program, while there was a significant reduction ( p < 0.05) for meningitis, 3.1 before and 1.6 after. Overall, the most commonly observed serotypes were 14 (34.2%), 6B (11.7%), and 23F (6.7%). 71.7% of the identified strains were vaccine types; 12.5% were vaccine-related serotypes. No clinically relevant changes in the incidence rate of IPDs or shift/replacement of serotypes was documented. Antimicrobial resistance predominated against erythromycin (32.5%) and clarithromycin (26.7%). Our data show that this risk group vaccination program had no impact on the incidence of IPD in young children.
There is a big tradition for apricot production in Hungary, but it is restricted because of bad ecological adaptability and frost sensitivity of genotypes. Before introduction of new foreign ...cultivars, it is very important to observe accurately the frost hardiness and winter tolerance of them. We have started to examine six foreign cultivars in winter 2010-2011. In this project our aim was to define the frost hardiness of the overwintering organs of new cultivars in different times of winter, and to describe the frost hardiness profile of them during winter. The frost hardiness of overwintering organs was determined by artificial freezing tests. Based on the results the LT50 (it is the temperature causes 50% frost damage) values were calculated. The following cultivars were observed: ‘Laycot’, ‘Pincot’, ‘Pisana’, ‘Sungiant’ (‘Goldrich’), ‘Sylred’, ‘Sylvercot’, and ‘Gönci magyar kajszi’ as control. Flower buds are the most frost-sensitive organs during winter, so we concentrated on them. The frost hardiness of flower buds increased gradually in the first half of winter, and decreased gradually in the second half of winter. The flower buds were the most frost hardy in the middle of winter, when the LT50 value of ‘Gönci magyar kajszi’ was -20.5°C. The examined foreign cultivars showed -18.5°C LT50 values in this time. Based on our first year results these cultivars are more frost sensitive than traditional Hungarian cultivars. It is necessary to continue observing to get more accurate results.
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