Cellular Processes In Segmentation
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Cellular Processes in Segmentation
The evolution of segmentation is one of the central questions in evolutionary developmental biology. Indeed, it is one of the best case studies for the role of changes in development in the evolution of body plans. Segmented body plans are believed to have appeared several times in animal evolution, and to have contributed significantly to the evolutionary success of the taxa in which they are present. Because of the centrality of the subject, and the continuing interest in understanding segmentation, this book offers an often overlooked focus on the cellular aspects of the process of segmentation, providing an invaluable reference for students of evolutionary developmental biology at all levels. Key Features Explores the role that segmentation has played in the diversity of animals Documents the diverse cellular mechanisms by which segmentation develops Reviews the independent evolutionary origins of segmentation Provides insight into the general patterns of serial homology at the cellular level Related Titles Lynne Bianchi. Developmental Neurobiology (ISBN 978-0-8153-4482-7). Jonathan Bard. Principles of Evolution: Systems, Species, and the History of Life (ISBN 978-0-8153-4539-8). Gerhard Scholtz. Evolutionary Developmental Biology of Crustacea (ISBN 978-9-0580-9637-1). Dr. Ariel D. Chipman is Associate Professor in the Department of Ecology, Evolution & Behavior of the Silberman Institute of Life Sciences at The Hebrew University of Jerusalem. He is the author or co-author of dozens of peer reviewed scientific journal articles. His research focuses upon the evolution of animal body plans with a focus on arthropod segmentation, integrating comparative embryology, the fossil record and genome evolution.
Cellular Processes in Segmentation
The evolution of segmentation is a central question in evolutionary developmental biology. Segmented body plans evolved several times in the history of life and contributed to the evolutionary success of these taxa. The book will prove an invaluable reference for students of evolutionary developmental biology at all levels.
Deep Learning Applications in Medical Image Segmentation
Author: Sajid Yousuf Bhat
language: en
Publisher: John Wiley & Sons
Release Date: 2025-01-03
Apply revolutionary deep learning technology to the fast-growing field of medical image segmentation Precise medical image segmentation is rapidly becoming one of the most important tools in medical research, diagnosis, and treatment. The potential for deep learning, a technology which is already revolutionizing practice across hundreds of subfields, is immense. The prospect of using deep learning to address the traditional shortcomings of image segmentation demands close inspection and wide proliferation of relevant knowledge. Deep Learning Applications in Medical Image Segmentation meets this demand with a comprehensive introduction and its growing applications. Covering foundational concepts and its advanced techniques, it offers a one-stop resource for researchers and other readers looking for a detailed understanding of the topic. It is deeply engaged with the main challenges and recent advances in the field of deep-learning-based medical image segmentation. Readers will also find: Analysis of deep learning models, including FCN, UNet, SegNet, Dee Lab, and many more Detailed discussion of medical image segmentation divided by area, incorporating all major organs and organ systems Recent deep learning advancements in segmenting brain tumors, retinal vessels, and inner ear structures Analyzes the effectiveness of deep learning models in segmenting lung fields for respiratory disease diagnosis Explores the application and benefits of Generative Adversarial Networks (GANs) in enhancing medical image segmentation Identifies and discusses the key challenges faced in medical image segmentation using deep learning techniques Provides an overview of the latest advancements, applications, and future trends in deep learning for medical image analysis Deep Learning Applications in Medical Image Segmentation is ideal for academics and researchers working with medical image segmentation, as well as professionals in medical imaging, data science, and biomedical engineering.