Gland Instance Segmentation in Colon Histology Images

This thesis looks at approaches to gland instance segmentation in histology images. The aim is to find suitable local image representations to describe the gland structures in images with benign tissue and those with malignant tissue and subsequently use them for design of accurate, scalable and flexible gland instance segmentation methods. The gland instance segmentation is a clinically important and technically challenging problem as the morphological structure and visual appearance of gland tissue is highly variable and complex. Glands are one of the most common organs in the human body. The glandular features are present in many cancer types and histopathologists use these features to predict tumour grade. Accurate tumour grading is critical for prescribing suitable cancer treatment resulting in improved outcome and survival rate. Different cancer grades are reflected by differences in glands morphology and structure. It is therefore important to accurately segment glands in histology images in order to get a valid prediction of tumour grade. Several segmentation methods, including segmentation with and without pre-classification, have been proposed and investigated as part of the research reported in this thesis. A number of feature spaces, including hand-crafted and deep features, have been investigated and experimentally validated to find a suitable set of image attributes for representation of benign and malignant gland tissue for the segmentation task. Furthermore, an exhaustive experimental examination of different combinations of features and classification methods have been carried out using both qualitative and quantitative assessments, including detection, shape and area fidelity metrics. It has been shown that the proposed hybrid method combining image level classification, to identify images with benign and malignant tissue, and pixel level classification, to perform gland segmentation, achieved the best results. It has been further shown that modelling benign glands using a three-class model, i.e. inside, outside and gland boundary, and malignant tissue using a two-class model is the best combination for achieving accurate and robust gland instance segmentation results. The deep learning features have been shown to overall outperform handcrafted features, however proposed ring-histogram features still performed adequately, particularly for segmentation of benign glands. The adopted transfer-learning model with proposed image augmentation has proven very successful with 100% image classification accuracy on the available test dataset. It has been shown that the modified object- level Boundary Jaccard metric is more suitable for measuring shape similarity than the previously used object-level Hausdorff distance, as it is not sensitive to outliers and could be easily integrated with region- based metrics such as the object-level Dice index, as contrary to the Hausdorff distance it is bounded between 0 and 1. Dissimilar to most of the other reported research, this study provides comprehensive comparative results for gland segmentation, with a large collection of diverse types of image features, including hand-crafted and deep features. The novel contributions include hybrid segmentation model superimposing image and pixel level classification, data augmentation for re-training deep learning models for the proposed image level classification, and the object- level Boundary Jaccard metric adopted for evaluation of instance segmentation methods

Colorectal Cancer

The projections for future growth in the number of new patients with colorectal cancer in most parts of the world remain unfavorable. When we consider the substantial morbidity and mortality that accompanies the disease, the acute need for improvements and better solutions in patient care becomes evident. This volume, organized in five sections, represents a synopsis of the significant efforts from scientists, clinicians and investigators towards finding improvements in different patient care aspects including nutrition, diagnostic approaches, treatment strategies with the addition of some novel therapeutic approaches, and prevention. For scientists involved in investigations that explore fundamental cellular events in colorectal cancer, this volume provides a framework for translational integration of cell biological and clinical information. Clinicians as well as other healthcare professionals involved in patient management for colorectal cancer will find this volume useful

Mitosis detection in intestinal crypt images with hough forest and conditional random fields.

Intestinal enteroendocrine cells secrete hormones that are vital for the regulation of glucose metabolism but their differentiation from intestinal stem cells is not fully understood. Asymmetric stem cell divisions have been linked to intestinal stem cell homeostasis and secretory fate commitment. We monitored cell divisions using 4D live cell imaging of cultured intestinal crypts to characterize division modes by means of measurable features such as orientation or shape. A statistical analysis of these measurements requires annotation of mitosis events, which is currently a tedious and time-consuming task that has to be performed manually. To assist data processing, we developed a learning based method to automatically detect mitosis events. The method contains a dual-phase framework for joint detection of dividing cells (mothers) and their progeny (daughters). In the first phase we detect mother and daughters independently using Hough Forest whilst in the second phase we associate mother and daughters by modelling their joint probability as Conditional Random Field (CRF). The method has been evaluated on 32 movies and has achieved an AUC of 72%, which can be used in conjunction with manual correction and dramatically speed up the processing pipeline

The HBP1 tumor suppressor is a negative epigenetic regulator of MYCN driven neuroblastoma through interaction with the PRC2 complex

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Polyphenols for Cancer Treatment or Prevention

Polyphenols are commonly found in fruits and vegetables, and have been suggested to have protective effects against chronic diseases, such as cancers. They are a diverse group of molecules, many of which possess antioxidant, anti-inflammatory, epigenetic, drug sensitization, and/or modulation of xenobiotic metabolizing enzyme properties. However, there is mixed evidence regarding their protective effects with respect to various cancers. Some of this controversy may be due to the combination of polyphenols administered, synergistic effects of accompanying compounds, bio-accessibility, bioavailability, effect of gut microbiota, and the type of cancer investigated. The purpose of this Special Issue is to present the recent evidence for the effect of polyphenol intake on cancer, as well as mechanisms of action. This Special Issue, entitled "Polyphenols for Cancer Treatment or Prevention", welcomes manuscript submissions of original research, meta-analyses, or reviews of the scientific literature. Authors should focus their manuscripts on polyphenol bioactives or dietary patterns naturally rich in polyphenols that have been identified and used for the prevention and or treatment of cancer

Targeting STAT3 and STAT5 in Cancer

Every minute, 34 new patients are diagnosed with cancer globally. Although over the past 50 years treatments have improved and survival rates have increased dramatically for several types of cancers, many remain incurable. Several aggressive types of blood and solid cancers form when mutations occur in a critical cellular signaling pathway, the JAK-STAT pathway; (Janus Kinase-Signal Transducer and Activator of Transcription). Currently, there are no clinically available drugs that target the oncogenic STAT3/5 proteins in particular or their Gain of Function hyperactive mutant products. Here, we summarize targeting approaches on STAT3/5, as the field moves towards clinical applications as well as we illuminate on upstream or downstream JAK-STAT pathway interference with kinase inhibitors, heat shock protein blockers or changing nuclear import/export processes. We cover the design paradigms and medicinal chemistry approaches to illuminate progress and challenges in understanding the pleiotropic role of STAT3 and STAT5 in oncogenesis, the microenvironment, the immune system in particular, all culminating in a complex interplay towards cancer progression

Food, Nutrition, Physical Activity, and the Prevention of Cancer: a Global Perspective

This Report has a number of inter-related general purposes. One is to explore the extent to which food, nutrition, physical activity, and body composition modify the risk of cancer, and to specify which factors are most important. To the extent that environmental factors such as food, nutrition, and physical activity influence the risk of cancer, it is a preventable disease. The Report specifies recommendations based on solid evidence which, when followed, will be expected to reduce the incidence of cancer