Local object patterns for representation and classification of colon tissue images

Cataloged from PDF version of article.This paper presents a new approach for the effective representation and classification of images of histopathological colon tissues stained with hematoxylin and eosin. In this approach, we propose to decompose a tissue image into its histological components and introduce a set of new texture descriptors, which we call local object patterns, on these components to model their composition within a tissue. We define these descriptors using the idea of local binary patterns, which quantify a pixel by constructing a binary string based on relative intensities of its neighbors. However, as opposed to pixel-level local binary patterns, we define our local object pattern descriptors at the component level to quantify a component. To this end, we specify neighborhoods with different locality ranges and encode spatial arrangements of the components within the specified local neighborhoods by generating strings. We then extract our texture descriptors from these strings to characterize histological components and construct the bag-of-words representation of an image from the characterized components. Working on microscopic images of colon tissues, our experiments reveal that the use of these component-level texture descriptors results in higher classification accuracies than the previous textural approaches. © 2013 IEEE

Graph walks for classification of histopathological images

This paper reports a new structural approach for automated classification of histopathological tissue images. It has two main contributions: First, unlike previous structural approaches that use a single graph for representing a tissue image, it proposes to obtain a set of subgraphs through graph walking and use these subgraphs in representing the image. Second, it proposes to characterize subgraphs by directly using distribution of their edges, instead of employing conventional global graph features, and use these characterizations in classification. Our experiments on colon tissue images reveal that the proposed structural approach is effective to obtain high accuracies in tissue image classification. © 2013 IEEE

Unsupervised tissue image segmentation through object-oriented texture

This paper presents a new algorithm for the unsupervised segmentation of tissue images. It relies on using the spatial information of cytological tissue components. As opposed to the previous study, it does not only use this information in defining its homogeneity measures, but it also uses it in its region growing process. This algorithm has been implemented and tested. Its visual and quantitative results are compared with the previous study. The results show that the proposed segmentation algorithm is more robust in giving better accuracies with less number of segmented regions. © 2010 IEEE

A picture of medically assisted reproduction activities during the COVID-19 pandemic in Europe

STUDY QUESTION: How did coronavirus disease 2019 (COVID-19) impact on medically assisted reproduction (MAR) services in Europe during the COVID-19 pandemic (March to May 2020)? SUMMARY ANSWER: MAR services, and hence treatments for infertile couples, were stopped in most European countries for a mean of 7 weeks. WHAT IS KNOWN ALREADY: With the outbreak of COVID-19 in Europe, non-urgent medical care was reduced by local authorities to preserve health resources and maintain social distancing. Furthermore, ESHRE and other societies recommended to postpone ART pregnancies as of 14 March 2020. STUDY DESIGN, SIZE, DURATION: A structured questionnaire was distributed in April among the ESHRE Committee of National Representatives, followed by further information collection through email. PARTICIPANTS/MATERIALS, SETTING, METHODS: The information was collected through the questionnaire and afterwards summarised and aligned with data from the European Centre for Disease Control on the number of COVID-19 cases per country. MAIN RESULTS AND THE ROLE OF CHANCE: By aligning the data for each country with respective epidemiological data, we show a large variation in the time and the phase in the epidemic in the curve when MAR/ART treatments were suspended and restarted. Similarly, the duration of interruption varied. Fertility preservation treatments and patient supportive care for patients remained available during the pandemic. LARGE SCALE DATA: N/A. LIMITATIONS, REASONS FOR CAUTION: Data collection was prone to misinterpretation of the questions and replies, and required further follow-up to check the accuracy. Some representatives reported that they, themselves, were not always aware of the situation throughout the country or reported difficulties with providing single generalised replies, for instance when there were regional differences within their country. WIDER IMPLICATIONS OF THE FINDINGS: The current article provides a basis for further research of the different strategies developed in response to the COVID-19 crisis. Such conclusions will be invaluable for health authorities and healthcare professionals with respect to future similar situations.peer-reviewe

Automatic segmentation of colon glands using object-graphs

Cataloged from PDF version of article.Gland segmentation is an important step to automate the analysis of biopsies that contain glandular structures. However, this remains a challenging problem as the variation in staining, fixation, and sectioning procedures lead to a considerable amount of artifacts and variances in tissue sections, which may result in huge variances in gland appearances. In this work, we report a new approach for gland segmentation. This approach decomposes the tissue image into a set of primitive objects and segments glands making use of the organizational properties of these objects, which are quantified with the definition of object-graphs. As opposed to the previous literature, the proposed approach employs the object-based information for the gland segmentation problem, instead of using the pixel-based information alone. Working with the images of colon tissues, our experiments demonstrate that the proposed object-graph approach yields high segmentation accuracies for the training and test sets and significantly improves the segmentation performance of its pixel-based counterparts. The experiments also show that the object-based structure of the proposed approach provides more tolerance to artifacts and variances in tissues. © 2009 Elsevier B.V. All rights reserved

Multilevel segmentation of histopathological images using cooccurance of tissue objects

Cataloged from PDF version of article.This paper presents a new approach for unsupervised segmentation of histopathological tissue images. This approach has two main contributions. First, it introduces a new set of high-level texture features to represent the prior knowledge of spatial organization of the tissue components. These texture features are defined on the tissue components, which are approximately represented by tissue objects, and quantify the frequency of two component types being cooccurred in a particular spatial relationship. As they are defined on components, rather than on image pixels, these object cooccurrence features are expected to be less vulnerable to noise and variations that are typically observed at the pixel level of tissue images. Second, it proposes to obtain multiple segmentations by multilevel partitioning of a graph constructed on the tissue objects and combine them by an ensemble function. This multilevel graph partitioning algorithm introduces randomization in graph construction and refinements in its multilevel scheme to increase diversity of individual segmentations, and thus, improve the fi- nal result. The experiments on 200 colon tissue images reveal that the proposed approach—the object cooccurrence features together with the multilevel segmentation algorithm—is effective to obtain high-quality results. The experiments also show that it improves the segmentation results compared to the previous approaches

A new method for lumen restoration in a patient with aphagia: Oro-oesophageal through-the-scope magnetic compression anastomosis

PMID = 3096856