11 research outputs found

    Colonoscopy and Colorectal Cancer Screening

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    Colorectal cancer (CRC) represents a major public health problem worldwide. Fortunately most CRCs originate from a precursor lesion, the adenoma, which is accessible and removable. This is the rationale for CRC screening programs, which are aimed to diagnose CRC at an early stage or even better to detect and resect the advanced adenoma before CRC has developed. In this background colonoscopy emerges as the main tool to achieve these goals with recent evidence supporting its role in CRC prevention. This book deals with several topics to be faced when implementing a CRC screening program. The interested reader will learn about the rationale and challenges of implementing such a program, the management of the detected lesions, the prevention of complications of colonoscopy, and finally the use of other screening modalities that are emerging as valuable alternatives. The relevance of the topics covered in it and the updated evidence included by the authors turn this book into a very useful tool to introduce the reader in this amazing and evolving field

    New Techniques in Gastrointestinal Endoscopy

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    As result of progress, endoscopy has became more complex, using more sophisticated devices and has claimed a special form. In this moment, the gastroenterologist performing endoscopy has to be an expert in macroscopic view of the lesions in the gut, with good skills for using standard endoscopes, with good experience in ultrasound (for performing endoscopic ultrasound), with pathology experience for confocal examination. It is compulsory to get experience and to have patience and attention for the follow-up of thousands of images transmitted during capsule endoscopy or to have knowledge in physics necessary for autofluorescence imaging endoscopy. Therefore, the idea of an endoscopist has changed. Examinations mentioned need a special formation, a superior level of instruction, accessible to those who have already gained enough experience in basic diagnostic endoscopy. This is the reason for what these new issues of endoscopy are presented in this book of New techniques in Gastrointestinal Endoscopy

    Endoscopic Procedures in Colon and Rectum

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    Endoscopic procedures in colon and rectum presents nine chapters which start with introductory ones like screening by colonoscopy as the preparation and monitoring for this exam. In addition to these approaches the book aims in the last four chapters to explain endoscopic diagnostic and therapeutic aspects in the colon and rectum. The description of each text is very comprehensive, instructive and easy to understand and presents the most current practices on the topics described. This book is recommended for general and colorectal surgeons as it presents guidelines for diagnosis and treatment which are very well established

    Polyp Segmentation with Fully Convolutional Deep Neural Networks—Extended Evaluation Study

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    Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two of the most relevant clinical target applications—lesion detection and classification, thereby providing important means to make both processes more accurate and robust. To automate video colonoscopy analysis, computer vision and machine learning methods have been utilized and shown to enhance polyp detectability and segmentation objectivity. This paper describes a polyp segmentation algorithm, developed based on fully convolutional network models, that was originally developed for the Endoscopic Vision Gastrointestinal Image Analysis (GIANA) polyp segmentation challenges. The key contribution of the paper is an extended evaluation of the proposed architecture, by comparing it against established image segmentation benchmarks utilizing several metrics with cross-validation on the GIANA training dataset. Different experiments are described, including examination of various network configurations, values of design parameters, data augmentation approaches, and polyp characteristics. The reported results demonstrate the significance of the data augmentation, and careful selection of the method’s design parameters. The proposed method delivers state-of-the-art results with near real-time performance. The described solution was instrumental in securing the top spot for the polyp segmentation sub-challenge at the 2017 GIANA challenge and second place for the standard image resolution segmentation task at the 2018 GIANA challenge

    GIANA Polyp Segmentation with Fully Convolutional Dilation Neural Networks

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    Polyp detection and segmentation in colonoscopy images plays an important role in early detection of colorectal cancer. The paper describes methodology adopted for the EndoVisSub2017/2018 Gastrointestinal Image ANAlysis – (GIANA) polyp segmentation sub-challenges. The developed segmentation algorithms are based on the fully convolutional neural network (FCNN) model. Two novel variants of the FCNN have been investigated, implemented and evaluated. The first one, combines the deep residual network and the dilation kernel layers within the fully convolutional network framework. The second proposed architecture is based on the U-net network augmented by the dilation kernels and “squeeze and extraction” units. The proposed architectures have been evaluated against the well-known FCN8 model. The paper describes the adopted evaluation metrics and presents the results on the GIANA dataset. The proposed methods produced competitive results, securing the first place for the SD and HD image segm entation tasks at the 2017 GIANA challenge and the second place for the SD images at the 2018 GIANA challenge

    Polyp Segmentation in Colonoscopy Images with Convolutional Neural Networks

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    The thesis looks at approaches to segmentation of polyps in colonoscopy images. The aim was to investigate and develop methods that are robust, accurate and computationally efficient and which can compete with the current state-of-the-art in polyp segmentation. Colorectal cancer is one of the leading cause of cancer deaths worldwide. To decrease mortality, an assessment of polyp malignancy is performed during colonoscopy examination so polyps can be removed at an early stage. In current routine clinical practice, polyps are detected and delineated manually in colonoscopy images by highly trained clinicians. To automate these processes, machine learning and computer vision techniques have been utilised. They have been shown to improve polyp detectability and segmentation objectivity. However, polyp segmentation is a very challenging task due to inherent variability of polyp morphology and colonoscopy image appearance. This research considers a range of approaches to polyp segmentation – seeking out those that offer a best compromise between accuracy and computational complexity. Based on analysis of existing machine learning and polyp image segmentation techniques, a novel hybrid deep learning segmentation method is proposed to alleviate the impact of the above stated challenges on polyp segmentation. The method consists of two fully convolutional networks. The first proposed network is based on a compact architecture with large receptive fields and multiple classification paths. The method performs well on most images, accurately segmenting polyps of diverse morphology and appearance. However, this network is prone to misdetection of very small polyps. To solve this problem, a second network is proposed, which primarily aims to improve sensitivity to small polyp details by emphasising low-level image features. In order to fully utilise information contained in the available training dataset, comprehensive data augmentation techniques are adopted. To further improve the performance of the proposed segmentation methods, test-time data augmentation is also implemented. A comprehensive multi-criterion analysis of the proposed methods is provided. The result demonstrates that the new methodology has better accuracy and robustness than the current state-of-the-art, as proven by the outstanding performance at the 2017 and 2018 GIANA polyp segmentation challenges
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