Deep Convolutional Neural Networks for Histological Image Analysis in Gastric Carcinoma Whole Slide Images

Introduction/ Background In this paper, histopathological whole slide images of gastric carcinoma are analyzed using deep learning methods. A convolutional neural network architecture is proposed for two classification applications in H&E stained tissue images, namely, cancer classification based on immunohistochemistry (IHC) into classes Her2/neu+ tumor, Her2/neu- tumor and non-tumor, and necrosis detection based on existence of necrosis into classes necrotic and non-necrotic. The studies in [1] and [2] explored computer-aided classification using graphbased methods and necrosis detection by textural approach respectively, which are extended using deep convolutional neural networks. Performance is quantitatively compared with established handcrafted image features, namely Haralick GLCM, Gabor filter-banks, LBP histograms, Gray histograms, RGB histograms and HSV histograms followed by classification by random forests, another well-known machine learning algorithm. Aims Convolutional neural networks (CNN) have recently gained tremendous attention in general image analysis [3-5]. There has also been an emergence of deep learning in digital histopathology for diverse classification and detection problems [6-8]. The prime motivation behind this work is that no previous study has explored deep learning for the specified goals in gastric cancer WSI. Automated cancer classification can assist pathologists in computer-aided diagnosis in H&E stained WSI without the requirement of IHC staining, thereby reducing preparation and inspection times, and decreasing inter- and intra-observer variability. Necrosis detection can play an important role in prognosis, as larger necrotic areas indicate a smaller chance of survival and vice-versa. Moreover, most deep learning studies have used smaller image sizes mainly due to memory restrictions of GPU, however, we consider larger regions in order to preserve context i.e. neighborhood information and tissue architecture at higher magnification. Further, this method is independent of nuclei segmentation, hence its performance is not limited by segmentation performance as in [1] (evaluation details in [9]). Methods Firstly, standard data augmentation techniques are applied on the available gastric cancer WSI dataset and thousands of images of size 512x512 are generated. Different CNN architectures are empirically studied to observe the behavior of variation in model characteristics (network depth, layer properties, training parameters, etc.) by training them from scratch on a representative subset of whole data for cancer classification. One of these is the Imagenet model [4], however it doesn’t perform desirably on the representative dataset. The self-designed CNN architecture with best classification rates is selected. Later, the proposed CNN is also applied for necrosis detection. Performance is compared with state of the art methods using handcrafted features and random forests. For evaluation, randomized three-fold stratified shuffle split and leave-one-patient-out cross validations are used. Results Conclusion: A self-designed CNN architecture is proposed for image analysis (cancer classification based on IHC and necrosis detection) in H&E stained WSI of gastric cancer. Quantitative evaluation shows that deep learning methods mostly compare favorably to state of the art methods, especially for necrosis detection. In future the aim is to expand the current WSI dataset and to improve the CNN architecture for optimal performance

Development Of An Android Based Interactive Guide For The Berliner Medizinhistorisches Museum Der Charité

Introduction/ Background Pathology is the science of diseases that ranges from macroscopic to histologic, and of course molecular changes. To offer a holistic education we wanted to involve portable electronic devices to combine information on diseases with microscopic changes and formalin fixed organs (macroscopic preparation). Aims At the time of compilation of this application there was no alternative, useful solution that offers the possibility of extensions towards virtual microscopy. Moreover, other solutions always use fixed databases or do not provide tools for content updates. Hence, it was required to create an appropriate system. Additional aimed feature are high performance, data-caching and the opinion to use the app in offline mode without a network connection. By the reason of the large amount of smartphone and tablet computer that runs the Android operating system and cheaper devices this platform was used. Methods We combined our virtual microscope „AndroScope“ [1] with a new developed user-interface of the „Berliner Medizinhistorisches Museum“(BMM) for android based mobile devices such as smartphone and tablet computer. As content we used images of the exhibition samples, information on the corresponding organ and disease, as well as the epidemiology data and whole slide images for visualization of histological changes. Linkage of digital content and samples is realized using QR-codes to assure valid and user-friendly recognition. We have also evaluated other technologies such as NFC, Bluetooth, WiFi or GPS to ensure that the QR-Code solution is the best opinion [2]. The application offers an online mode with full functionality and an offline mode with limited access to images as well as to the virtual microscope. The application main database is stored local on the android device and online update capabilities were added. Results The “BMM Guide” is available for all visitors of the museum on lendable devices or for students (professional audience) using their personal devices and installing the application manually via the web-access eduroam. The guide is connected to the internet. It is designed to easily expand, update or transfer the content catalogue data. At the moment there is a connection between the exhibit and text-, image-, video- or virtual microscope content via QR-Code. The offline mode is limited to the connection between text content and the exhibit. We also implemented a multi-language support for English and German. The application has information like room plans, opening times and latest news of the museum. The museum guide is an easy handable, selfexplaining blended learning tool that can be embedded in the general education. .This guide for the exhibitions of the Berliner Medizinhistorisches Museum opens a new branch for self-study of students. Nevertheless he still has a potential to be integrated in curricular lectures in the future

Adult Hyalomma marginatum tick positive for Rickettsia aeschlimannii in Austria, October 2018

We report on a non-indigenous adult Hyalomma marginatum tick in Austria carrying the human pathogenic Rickettsia aeschlimannii; presumably introduced as a nymph via migratory birds and completed the moulting within the same year. It was negative for Crimean-Congo haemorrhagic fever virus, but the finding of R. aeschlimannii represents a potential threat for humans due to its zoonotic character. Awareness of invasive tick species and carried pathogens should be improved in central and northern Europe

Prevention of Birch Pollen-Related Food Allergy by Mucosal Treatment with Multi-Allergen-Chimers in Mice

Among birch pollen allergic patients up to 70% develop allergic reactions to Bet v 1-homologue food allergens such as Api g 1 (celery) or Dau c 1 (carrot), termed as birch pollen-related food allergy. In most cases, specific immunotherapy with birch pollen extracts does not reduce allergic symptoms to the homologue food allergens. We therefore genetically engineered a multi-allergen chimer and tested if mucosal treatment with this construct could represent a novel approach for prevention of birch pollen-related food allergy.BALB/c mice were poly-sensitized with a mixture of Bet v 1, Api g 1 and Dau c 1 followed by a sublingual challenge with carrot, celery and birch pollen extracts. For prevention of allergy sensitization an allergen chimer composed of immunodominant T cell epitopes of Api g 1 and Dau c 1 linked to the whole Bet v 1 allergen, was intranasally applied prior to sensitization.Intranasal pretreatment with the allergen chimer led to significantly decreased antigen-specific IgE-dependent β-hexosaminidase release, but enhanced allergen-specific IgG2a and IgA antibodies. Accordingly, IL-4 levels in spleen cell cultures and IL-5 levels in restimulated spleen and cervical lymph node cell cultures were markedly reduced, while IFN-γ levels were increased. Immunomodulation was associated with increased IL-10, TGF-β and Foxp3 mRNA levels in NALT and Foxp3 in oral mucosal tissues. Treatment with anti-TGF-β, anti-IL10R or anti-CD25 antibodies abrogated the suppression of allergic responses induced by the chimer.Our results indicate that mucosal application of the allergen chimer led to decreased Th2 immune responses against Bet v 1 and its homologue food allergens Api g 1 and Dau c 1 by regulatory and Th1-biased immune responses. These data suggest that mucosal treatment with a multi-allergen vaccine could be a promising treatment strategy to prevent birch pollen-related food allergy

eDNA metabarcoding biodiversity of freshwater fish in the Alpine area

Environmental DNA (eDNA) based methods are proving to be a promising tool for freshwater fish biodiversity assessment in Europe within the Water Framework Directive (WFD, 2000/60/EC) especially for large rivers and lakes where current fish monitoring techniques have known shortcomings. Many freshwater fish are experiencing critical population declines with risk of local or global extinction because of intense anthropogenic pressure and this can have serious consequences on freshwater ecosystem functioning and diversity. Within the EU project Eco-AlpsWater, advanced high throughput sequencing (HTS) techniques are used to improve the traditional WFD monitoring approaches by using environmental DNA (eDNA) collected in Alpine waterbodies. An eDNA metabarcoding approach specifically designed to measure freshwater fish biodiversity in Alpine lakes and rivers has been extensively evaluated by using mock samples within an intercalibration test. This eDNA method was validated and used to study fish biodiversity of eight lakes and six rivers of the Alpine region including four EC countries (Austria, France, Italy, Slovenia) and Switzerland. More in detail, this metabarcoding approach, based on HTS sequencing of a section of the 12S rRNA gene, was used to assess freshwater fish biodiversity and their distribution in the different habitats. These data represent the first attempt to provide a comprehensive description of freshwater fish diversity in different ecosystems of the Alpine area confirming the applicability of eDNA metabarcoding analyses for the biomonitoring of fish inhabiting Alpine and perialpine lakes and rivers

Block-Centric Visualization Of Histological Whole Slide Images With Application To Revealing Growth-Patterns Of Early Colorectal Adenomas And Aberrant Crypt Foci

Introduction/ Background Comfortable navigation through diagnostic images is a prospective challenge for the acceptance of virtual microscopy applications in routine pathology [1],[2]. Tracing different regions of interest through multiple sections on one or several slides is a typical task in diagnostic slide examination. This laborious and time-consuming co-localization is currently executed by pathologists. Retaining the relative positions of tissue structures while alternating between multiple slides is still not feasible in a satisfactory manner in conventional nor virtual microscopy. Aims To address this issue we present a more comfortable and intuitive method to read slides using computer-assisted navigation. Furthermore, we demonstrate the strengths of our method by applying it to large series of serial colorectal tissue sections, creating new kinds of visualizations of different adenomatous mucosal architectures in human tissue, while looking for human correlates of lesions recently described in mice [3]. Methods Histological images contain multiple distortions from different sources in the laboratory and digitalization process. An interconnection model was created to describe distortions by several layers, providing a normalized tissue representation. Layers were associated with specific distortions with each layer serving as a new level of abstraction. The first layers enabled a coarse alignment of tissue sections. Further alignment is achieved by piecewise, multi-resolution, SIFT-based [4] correspondence extraction and refinement. Inside the convex hull of all fiducial points local affine transformations were applied whereas a global affine transformation was used on the outside. Animated stacks were generated for regions of interest using local rigid transformations to preserve exact morphological coherences. For subsequent creation of 3D models, the relevant histological objects within these images were annotated by pathologists, partly using computer assisted segmentation based on active contours [5]. These annotations were used subsequently to create simplified 3D models by applying VTK [6].  Results The presented methods provide an efficient means to retrieve correspondences and additional spatial information from serial sections of histological slides. They also show good applicability for specimen from different origin. Alignment methods can be applied to generate block-centric visualizations such as parallel and transparent viewing of multiple stains. Moreover, the generated stack videos and 3D models demonstrate the very good accuracy of section alignment even in large series. The visualizations enable pathologists and researchers to grasp the 3D structural relationships in the tissue at a glance, providing an excellent tool to communicate more complex histomorphological findings. Interestingly, we see two kinds of tubular adenomas, which could imply multiple ways to tubular adenoma formation in FAP-patients, possibly akin to the recent observations in mice [3]

A forest typology for monitoring sustainable forest management: The case of European Forest Types

Sustainable forest management (SFM) is presently widely accepted as the overriding objective for forest policy and practice. Regional processes are in progress all over the world to develop and implement criteria and indicators of SFM. In continental Europe, a set of 35 Pan-European indicators has been endorsed under the Ministerial Conference on the Protection of Forests in Europe (MCPFE) to measure progress towards SFM in the 44 countries of the region. The formulation of seven indicators (forest area, growing stock, age structure/diameter distribution, deadwood, tree species composition, damaging agents, naturalness) requires national data to be reported by forest types. Within the vast European forest area the values taken by these indicators show a considerable range of variation, due to variable natural conditions and anthropogenic influences. Given this variability, it is very difficult to grasp the meaning of these indicators when taken out of their ecological background. The paper discusses the concepts behind, and the requirements of, a classification more soundly ecologically framed and suitable for MCPFE reporting than the three (un-informative) classes adopted so far: broadleaved forest, coniferous forest, mixed broadleaved and coniferous forest. We propose a European Forest Types scheme structured into a reasonably higher number of classes, that would improve the specificity of the indicators reported under the MCPFE process and its understanding.L'articolo è disponibile sul sito dell'editore www.tandf.co.uk/journals