High Performance Image Analysis for Large Histological Datasets PDF Download
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Author: Lee Alex Donald Cooper
Publisher:
ISBN:
Category :
Languages : en
Pages : 224
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Book Description
This dissertation addresses two significant problems in the analysis of large histological images: reconstruction and tissue segmentation. The proposed methods form a framework that is intended to provide researchers with tools to explore and quantitatively analyze large image datasets.
Author: Lee Alex Donald Cooper
Publisher:
ISBN:
Category :
Languages : en
Pages : 224
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Book Description
This dissertation addresses two significant problems in the analysis of large histological images: reconstruction and tissue segmentation. The proposed methods form a framework that is intended to provide researchers with tools to explore and quantitatively analyze large image datasets.
Author: Gurcan
Publisher: Wiley-Blackwell
ISBN: 9781119099093
Category :
Languages : en
Pages : 256
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Book Description
Author: João Manuel R. S. Tavares
Publisher: Springer
ISBN: 331915799X
Category : Technology & Engineering
Languages : en
Pages : 262
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Book Description
This book contains the full papers presented at ICCEBS 2013 – the 1st International Conference on Computational and Experimental Biomedical Sciences, which was organized in Azores, in October 2013. The included papers present and discuss new trends in those fields, using several methods and techniques, including active shape models, constitutive models, isogeometric elements, genetic algorithms, level sets, material models, neural networks, optimization and the finite element method, in order to address more efficiently different and timely applications involving biofluids, computer simulation, computational biomechanics, image based diagnosis, image processing and analysis, image segmentation, image registration, scaffolds, simulation and surgical planning. The main audience for this book consists of researchers, Ph.D students and graduate students with multidisciplinary interests related to the areas of artificial intelligence, bioengineering, biology, biomechanics, computational fluid dynamics, computational mechanics, computational vision, histology, human motion, imagiology, applied mathematics, medical image, medicine, orthopaedics, rehabilitation, speech production and tissue engineering.
Author: A. Ravishankar Rao
Publisher: Artech House
ISBN: 1596932961
Category : Medical
Languages : en
Pages : 353
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Book Description
This innovative volume surveys the latest image acquisition advances in serial block face techniques in scanning electron microscopy, knife-edge scanning microscopy, and 4D imaging of multi-component biological systems. The book introduces parallel processing for biological applications. You learn advanced parallelization techniques for decomposing a problem domain and mapping it onto a parallel processing architecture using the message-passing interface (MPI) and OpenMP. Case studies show how these techniques have been successfully used in simulation tasks, data mining, and graphical visualization of biological datasets. You also find coverage of methods for developing scalable biological image databases and for facilitating greater interactive visualization of large image sets.
Author: Jie Zhou
Publisher: World Scientific
ISBN: 9811286140
Category : Computers
Languages : en
Pages : 374
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Book Description
This unique compendium provides state-of-the-art computational methodology and applications in bioimage informatics. It covers cutting-edge technology developments in biological image analysis, where images come from new modalities and are often large scale, high throughput and high dimensional. The book reflects advances in intelligent algorithms for tasks such as biological image segmentation, reconstruction, and object tracking.Contributed by world renowned researchers, this useful reference text presents case studies that can potentially help readers find approaches and resources to address their imminent scientific problems.
Author: Wenyuan Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 198
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Book Description
Ever since the advent of Alexnet in the ImageNet challenge in 2012, the medical image analysis community has taken notice of deep learning techniques and made the transition from systems that use handcrafted features to systems that learn feature from the data gradually. Histopathology images have been widely used to detect and diagnose a variety of cancers. With the growing availability of large scale gigapixel whole-slide images (WSI) of tissue specimen, digital pathology has become a very popular application area for deep learning techniques. Nevertheless, challenges exist in current computer-aided histopathology image analysis. Perhaps the biggest challenge is the insufficiency of annotated data. Deep learning requires extremely abundant training data to achieve good performance. However, only pathologists, who have been trained for years, can annotate the histopathology image accurately. Therefore, labeling histopathology images is both expensive and labor-intensive. The scarcity of the annotation can also be found at different scales. For example, to do a semantic segmentation task, it requires the network to have annotations at ``pixel-wise'' level; by tiling WSIs into different patches, patch-level labels are needed to provide accurate predictions. But in reality, most labels of WSIs are at case-level (\eg final diagnosis) at most. This dissertation attempts to improve data efficiency on histopathology image analysis. We first start with a novel fully-supervised segmentation model for Gleason grading of prostate cancer. This method adopts two branches, an EpithelialNetwork Head (EHN) for detecting epithelial cells, and a Grading Network Head (GNH) for detecting, segmenting, and classifying the cancerous regions. Then we present a series of studies on semi-supervised learning, where we can take leverage of unannotated data. We focus on methods using generative adversarial networks (GANs). To this end, we demonstrate a pyramid GAN structure for high-resolution large-scale histopathology image generation and segmentation on both fully-supervised and semi-supervised scenarios. Finally, we present an active learning framework that is able to reduce the annotations required from the expert and handle noisy labels simultaneously. Extensive experiments and results have proved the e ectiveness of these methods, paving the way to optimize and improve the e ectiveness of data usage in histopathology image analysis.
Author: Richard Wootton
Publisher: CUP Archive
ISBN: 9780521434829
Category : Medical
Languages : en
Pages : 466
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Book Description
This volume provides a timely and useful introduction to the theory and practical application of image analysis in histology. This powerful research technique can be used to detect not only stored products in a cell (immunocytochemistry) but the synthetic machinery and the genes that control it (in situ hybridisation), as well as the specific binding sites that act as receptors for a molecule following its release (in vitro autoradiography). The book provides a good introduction for beginners before looking in greater detail at more advanced material in selected areas. The volume highlights the importance of technique in gathering quantitative information. The book is divided into four sections: introductory material, image acquisition, image processing, and applications. The applications areas include quantitative immunochemistry, quantification of nerves and neurotransmitters and automated grain counting in in situ hybridisation histochemistry.
Author: Le Lu
Publisher: Springer
ISBN: 331942999X
Category : Computers
Languages : en
Pages : 327
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Book Description
This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Author: Aparajita Nanda
Publisher: Springer Nature
ISBN: 9811568448
Category : Technology & Engineering
Languages : en
Pages : 264
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Book Description
This book focuses on the challenges and the recent findings in vision intelligence incorporating high performance computing applications. The contents provide in-depth discussions on a range of emerging multidisciplinary topics like computer vision, image processing, artificial intelligence, machine learning, cloud computing, IoT, and big data. The book also includes illustrations of algorithms, architecture, applications, software systems, and data analytics within the scope of the discussed topics. This book will help students, researchers, and technology professionals discover latest trends in the fields of computer vision and artificial intelligence.
Author: Bindesh Shrestha
Publisher: Elsevier
ISBN: 0128189991
Category : Science
Languages : en
Pages : 272
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Book Description
Imaging mass spectrometry (MS) techniques are often utilized without an understanding of their underlying principles, making it difficult for scientists to determine when and how they can exploit MS to visualize their biomolecules of interest. Introduction to Spatial Mapping of Biomolecules by Imaging Mass Spectrometry is an essential reference to help scientists determine the status and strategies of biomolecule analysis, describing its many applications for diverse classes of biomolecules. The book builds a foundation of imaging MS knowledge by introducing ionization sources, sample preparation, visualization guidelines, molecule identification, quantification, data analysis, etc. The second section contains chapters focused on case studies on analyzing a biomolecule class of molecules. Case studies include an introduction/background, and a summary of successful imaging MS studies with illustrative figures and future directions. Provides the introductory foundations of imaging mass spectrometry for those new to the technique Organized by topic to facilitate a quick deep dive, allowing researchers to immediately apply the imaging MS techniques to their work Includes case studies summarizing the imaging MS techniques developed for the class of molecules
